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Plant Phenomics / 2022 / Article

Review Article | Open Access

Volume 2022 |Article ID 9760269 |

Rui Xu, Changying Li, "A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots", Plant Phenomics, vol. 2022, Article ID 9760269, 20 pages, 2022.

A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots

Received22 Dec 2021
Accepted25 Apr 2022
Published17 Jun 2022


Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient. The high-throughput field phenotyping systems and in particular robotic systems play an important role to automate data collection and to measure novel and fine-scale phenotypic traits that were previously unattainable by humans. The main goal of this paper is to review the state-of-the-art of high-throughput field phenotyping systems with a focus on autonomous ground robotic systems. This paper first provides a brief review of nonautonomous ground phenotyping systems including tractors, manually pushed or motorized carts, gantries, and cable-driven systems. Then, a detailed review of autonomous ground phenotyping robots is provided with regard to the robot’s main components, including mobile platforms, sensors, manipulators, computing units, and software. It also reviews the navigation algorithms and simulation tools developed for phenotyping robots and the applications of phenotyping robots in measuring plant phenotypic traits and collecting phenotyping datasets. At the end of the review, this paper discusses current major challenges and future research directions.

1. Introduction

Plant phenotyping is an emerging science that links genomics with plant ecophysiology and agronomy. Assessments of plant phenotypes in the field can be labor-intensive and inefficient. The emergence of high-throughput field phenotyping (HTFP) is to increase the throughput by leveraging sensing technologies and data processing algorithms. The HTFP systems integrate sensors with mobile platforms to collect data in the field with minimal or no human intervention. Many HTFP systems have been developed so far, including aerial and ground systems. While aerial systems can provide higher efficiency and coverage than ground systems, ground systems usually have a higher payload to carry heavy sensors and equipment and can collect high-resolution data for measuring phenotypic traits at finer levels (e.g., the plant and organ level) and with more viewing angles than aerial systems. In addition, ground platforms typically provide better data quality than aerial systems by controlling the data collection environment (such as light conditions) with well-designed enclosures, and they are less affected by the wind to maintain a straight path to perform more even longitudinal scanning than aerial systems. Early ground HTFP systems were based on tractors and human-operated pushcarts. The recent trend is to use autonomous phenotyping robots to automate data collection.

Most agricultural environments are unstructured that can change rapidly in time and space; therefore, a phenotyping robot needs to be intelligent to operate by itself. A robot’s intelligence follows a “sense-think-act” cycle where the robot needs to understand its surrounding environment, to make decisions, and to perform certain operations to achieve its goals. For phenotyping robots, the intelligence mainly focuses on navigation in unstructured environments because the phenotyping robot’s primary mission is to collect data autonomously in the field. A typical phenotyping robot primarily consists of the mobile platform, sensors including phenotyping sensors for measuring phenotypic traits and perception sensors for navigation, and computing units for data collection and robot navigation (Figure 1). Manipulators, such as robotic arms, are sometimes used in phenotyping robots to measure certain phenotypic traits.

There are a few excellent review papers on robotic technologies for plant high-throughput phenotyping [1, 2], but they either do not solely focus on infield systems or lack detailed review of technical aspects of field robots. Thus, the goal of this paper is to fill the gap and review the existing ground phenotyping robots for HTFP focusing on technical perspectives. First, this paper provides a brief review of the ground phenotyping systems using nonrobotic systems and then a detailed review of ground phenotyping robots with regard to the robot’s main components, including mobile platforms, sensors, manipulators, computing units, and software. It then reviewed the navigation algorithms and simulation tools developed for phenotyping robots and the applications of phenotyping robot. At the end, this paper discusses current challenges and presents future research directions for phenotyping robots. This paper is focused on phenotyping robots developed in academia and does not include commercial robots. The reviewed papers were searched using Google Scholar and Web of Science covering literature from the year of 2011 to 2022. We used “high throughput phenotyping”, “robot”, and “robotics” as keywords to search relevant studies. In total, 117 papers including 90 journal papers from 34 different journals and 27 conference papers were selected and reviewed.

2. Field-Based Ground Phenotyping Systems

The earliest ground HTFP systems developed were based primarily on tractors because of their wide availability and ease in modification for mounting sensors. However, the soil compaction created by tractors makes them unsuitable for frequent data collection. Therefore, a lightweight pushcart, or motorized cart, was developed to replace tractors. Since tractors and pushcarts still need manual control, the gantry and cable-driven platforms were developed to automate data collection fully. However, gantry and cable-driven platforms are not mobile and can only cover certain fields, limiting the number of experimental plots. Their high construction and maintenance costs also limit their usage. Table 1 lists the major ground HTFP systems in literature and compares their strengths and limitations.

SystemSensorsCropPhenotypical traitsAdvantagesDisadvantages

Tractor-based systemHigh payload
Wide availability
Easy to be modified for data collection
Large vibration
Heavyweight can create soil compaction
Data collection is limited by the soil conditions
Manual drive could damage the crops
Difficult to control its speed and trajectory
[3]RGB camera
Canopy height
Canopy temperature
Green pixel fraction
Fraction of intercepted radiation
Chlorophyll content
Plant and ear density
BreedVision [4]3D camera
Light curtain
Laser distance sensor
Hyperspectral camera
RGB camera
TriticalePlant height
Canopy reflectance
[5]Infrared temperature sensor
Ultrasonic sensor
Spectral reflectance sensor
CottonCanopy height
Canopy temperature
[6]Ultrasonic sensor
Spectral reflectance sensor
RGB camera
Infrared thermometer
CottonPlant height
Ground cover fraction
Canopy temperature
Phenoliner [7]RGB camera
NIR camera
Thermal camera
Hyperspectral camera
GrapePlant count
GPhenoVision [8]RGBD camera
Thermal camera
Hyperspectral camera
CottonPlant height
Projected leaf area
Canopy volume
Canopy temperature
In-row width
Cross-row width
ProTractor [9]RGB cameraBrassicaSeedling count

PushcartLight weight
Low cost
Easy to control the speed and position
Not practical for large field
Manual control could damage the crops
[10]Ultrasonic sensor
NDVI sensor
Infrared thermometer
RGB camera
Canopy height
Canopy temperature
Green pixel fraction
Phenocart [11]Spectral reflectance sensor
RGB camera
Infrared thermometer
WheatCanopy temperature
Proximal sensing cart [12]Ultrasonic sensor
Infrared thermometer
Spectral reflectance sensor
RGB camera
CottonCanopy height
Canopy temperature
Canopy cover
Crop water stress index
Leaf area index
Phenocart [13]RGB camera
NIR camera
[14]Hyperspectral cameraTobacco
Motorized pushcart
Professor [15]Not specifiedWheat
Not specified
[16]LiDAR sensor
Spectral reflectance sensor
Photochemical reflectance index
Canopy height

Gantry systemFully autonomous
No physical contact with the soil or plants
Field scanning can be precisely controlled
No damaging to the plants
Fixed experimental site
Only the top view of the canopy can be scanned
High construction and maintenance cost
Field Scanalyzer [17]Thermal camera
Chlorophyll fluorescence imager
3D laser scanner
RGB camera
Hyperspectral camera
WheatPlant morphology
Canopy temperature
Spectral indices
PhénoField [18]LiDAR sensor
RGB camera
WheatGreen cover fractions
Green area index
Average leaf angle
Meris terrestrial chlorophyll index
Plant height
Cable system
Field phenotyping platform [19]RGB camera
NIR camera
Laser scanner
Thermal camera
Ultrasonic sensor
Multispectral camera
WheatEnhanced NDVI
Canopy cover
Canopy height
Canopy temperature
Canopy spectral reflectance
NU-Spidercam [20]Multispectral camera
Thermal camera
Canopy cover
Canopy temperature
Canopy height
Canopy reflectance

2.1. Tractor-Based Systems

Early tractor-based HTFP systems were modified from high-clearance tractors mounted with multiple sensors to measure plant phenotypic traits. One of the first systems was developed by USDA Maricopa Agricultural Center in Arizona. Their system had eight sets of infrared thermometer and ultrasonic sensors that measure the temperature and height of the canopy of eight rows with a single pass [5]. A Real-time Kinematic Global Navigation Satellite System (RTK-GNSS) was mounted onto the tractor to georeference measurements. Another representative system is BreedVision, which used a tractor with an enclosure to carry multiple sensors [4]. The BreedVision integrated a 3D time-of-flight camera, a light curtain sensor, a laser distance sensor, a hyperspectral camera, and a RGB camera. As imaging technologies advanced, the later developed HTFP platforms mainly used imaging sensors to collect data. For example, the GPhenoVision is a tractor-based multisensor system that integrated a hyperspectral, thermal, and RGB-D camera [9]. The GPhenoVision system has been used to measure morphological traits for cotton [21].

The high payload capacity of the tractor-based HTFP system eases the carrying of heavy equipment, such as the imaging chamber, so that the environmental conditions can be controlled partly for data collection. However, the tractor’s heavy weight can create soil compaction with frequent data collection, which could interrupt the crop’s growth. Additionally, the data acquisition system can suffer from the tractor’s vibrations, which potentially could damage the sensors and affect the data quality if the vibrations are not properly isolated. The field soil conditions (such as muddy soil after rain) could limit the operation of the tractor. Because tractors need to be driven manually, the lack of precise controls for the tractor’s speed and trajectory can affect specific sensors, such as the push-broom hyperspectral camera. Furthermore, there is the potential risk for damaging the crops with manual driving, but this is a common issue for manually controlled, ground HTFP systems.

2.2. Pushcart and Motorized Cart

As an alternative to the tractor, pushcart can be assembled easily with low-cost materials. It was developed to solve the soil compaction issue of the tractor. Most cart-type systems were made of metal frames with bicycle wheels, which makes them low cost and lightweight [1015]. The frame structure eases the mounting of sensors. Since the pushcart is activated manually, its position is easier to control than that of tractors such that it can stop at any position and scan the field [14]. However, manual driving makes pushcarts impractical for large fields. The motorized cart uses electronic motors to move the cart, meaning it can be controlled remotely. “Professor” is a platform that uses two DC motors for driving and two DC motors for steering [15]. Its frame is made of aluminum extrusions, and its width and height can be adjusted with an inner frame. It is manually controlled with a remote controller. A self-propelled electric platform was developed recently for wheat phenotyping, and it can carry a person to manually drive it [16]. The major drawback of the pushcart and motorized cart is that they require manual power and manual control, which is inefficient and not practical for large fields. Similar to tractor-based systems, manually controlled pushcart and motorized cart also have the potential risk for damaging the crops with frequent data collection.

2.3. Gantry and Cable-Driven System

A gantry is an overhead, bridge-like structure that supports equipment such as a crane. The gantry-based HTFP system uses a gantry to carry sensors and can move linearly on parallel rails. The camera can move along the gantry bridge as well as vertically, making the camera move in XYZ axes. One well-known gantry-based system is the Field Scanalyzer that was developed by LemnaTec [22] and one such system was built at University of Arizona’s Maricopa Agricultural Center [17]. The high payload (500) enables the system to carry heavy sensors, such as the chlorophyll fluorescence imager (120). PhénoField is a gantry system managed by the applied research institute ARVALIS in France that was used primarily for wheat breeding [18]. It has a mobile rainout shelter equipped with irrigation booms to control the water stress for desired plots.

Like the gantry-based system, the cable-driven system is a fixed-site system where the sensors were suspended from cables supported by towers at the outside corners of the field. The movements of the sensors were driven by cable winches. Some representative systems include the field phenotyping platform (FIP) at the Swiss Federal Institute of Technology in Zurich [19] and the NU-Spidercam from the University of Nebraska-Lincoln [20].

The advantages of gantry-based and cable-driven platforms are that they do not make physical contact with the soil or plants in addition to being fully autonomous, where the sensors can be precisely controlled at a particular position to scan the crops and can scan the field repeatedly throughout the growing season. The primary disadvantage is the limited field coverage and a fixed experimental site, which can limit their usage in breeding programs. Since the systems usually scan the top view of the canopy, they do not provide information under the canopy without side views. Other disadvantages include high construction and maintenance costs.

3. Phenotyping Robot

The development of agricultural robots has advanced significantly in the past decade to address labor shortages in agricultural. The advantages of automation make the agricultural robot a promising means for managing large farms with minimal human labor and make it an ideal solution for HTFP. Thus, there is a trend to develop phenotyping robots to replace tractors and pushcarts (Figure 2).

3.1. Mobile Platform

Existing phenotyping robots can be classified into three categories based on the drive mechanism: wheeled robot, tracked robot, and wheel-legged robot. The wheeled robot uses wheels to drive the robot while the tracked robot uses tracks. These are the two mostly used forms. The legged robot uses articulated legs to provide locomotion, such as a hexapod robot [23]. The wheel-legged robot combines the wheels with articulated legs, so it has more control of locomotion. Table 2 summarizes some developed phenotyping robots mentioned in the literature.

RobotPhenotyping sensorPerception sensorCropAdvantagesDisadvantagesApplications

Wheeled robot (skid steering)
VinBot [28]RGB-D camera
RTK-GNSSIMUGrapeSimple mechanical structure
Simple motion control
Low cost
Low power efficiency in turning
High wear and tear of the tires
Can cause damage to the soil
Yield estimation [47]
Shrimp [25]3D LiDAR
RGB camera
Hyperspectral camera
RTK-GNSSIMUHorticultural cropMango fruit detection [25]
Almond mapping of flower, fruits, and yield [48]
Almond fruit detection [49]
Robotanist [26]Stereo camera
RGB camera
Stereo camera
SorghumCorn stalk count and width estimation [50]
Vinobot [24]Trinocular cameraGNSS2D LiDARMaize
Simulation of Vinobot [51]
TerraSentia [27]Multispectral camera2D LiDAR
Hyperspectral camera
RGB camera
RGB-D camera
MaizeCorn stem width estimation [52]
Under canopy navigation [53]
RGB-D camera
RTK-GNSSIMUNot specifiedSimulation of an agricultural robot [54]
[55]RGB-D cameraRTK-GNSS2D LiDARMaizeCorn stalk diameter estimation [55]

Wheeled robot (differential drive)
RobHortic [30]Thermal camera
Multispectral camera
RGB camera
NIR camera
Hyperspectral camera
RTK-GNSSHorticultural cropSimple mechanical structure
Simple motion control
Low cost
Less precise in steering controlCarrot disease detection [30]

Wheeled robot (2WD2WS and Ackerman steering)
AgBotII [31]RGB cameraRTK‐GNSSRow cropPrecise steering controlNeed coordination between drive wheel and steering wheelWeed detection [56]
Phenobot 1.0 [32]Stereo cameraRTK‐GNSSSorghumSorghum plant height and stalk diameter estimation [32]
Sorghum plant architecture [57]
AgriRover-01 [33]3D LiDARRTK‐GNSSCornPlant height and row spacing estimation [58]
DairyBioBot [34]2D LiDAR
NDVI sensor
RTK‐GNSSRyegrassRyegrass biomass estimation [34]

Wheeled robot (articulated steering)
Phenobot 3.0 [2, 35]Stereo cameraRTK-GNSSSorghumGood mobility for rough terrain
Precise steering control
Increased complexity in mechanical structure

Wheeled robot (4WD4WS)
Thorvald II [36]Application dependentGPSIMU2D LiDARRow crop
Horticulture crop
High maneuverability
Good terrain adaptation
Complex mechanical structure
Complex motion control
High cost
Development of a strawberry harvesting robot [59]
Development of row-following algorithm in polytunnels [60]
Ladybird [37]RGB camera2D LiDAR
Hyperspectral camera
Stereo camera
Thermal camera
Row cropWeed detection [61]
Dataset collection [62]
Phenomobile V1 [38]2D LiDAR
RGB camera
Multispectral camera
RTK-GNSSRow crop
Mixed crop
Estimation of plant height from LiDAR measurement [38]
Estimation of wheat green area index from LiDAR measurement [63]
AgRover [64]Not specifiedRTK-GNSSRow crop
Flex-Ro [39]RGB camera
Ultrasonic sensor
Infrared thermometer
Obstacle detector
Row crop
MARS X [40]Application dependentRTK-GNSSRow crop

Tracked (differential drive)
Armadillo Scout [41]Application dependentGNSS2D LiDARNot specifiedGood mobility for rough terrain
Low ground pressure
Complex mechanical structure
Low power efficiency in turning
Can damage the soil
PHENObot [42]RGB cameraRTK-GNSSGrapeGrape bunch and berry detection [66]
[65]RGB cameraRTK-GNSSSoybean
[43]RGB camera
TERRA-MEPP [44]Stereo camera
Depth camera
RGB camera
Wheel encoder
Phenomobile V2 [45]2D LiDAR
RGB camera
Multispectral camera
Mixed crop

Wheel-legged (4WD4WS)
BoniRob [67]Application dependentRTK-GNSS
Inertial sensor3D LiDAR
Row cropHigh maneuverability
Good terrain adaptation
Physical dimension can be changed
Complex mechanical structure
Complex motion control
High cost
Soil compaction and moisture measurement [68]
Weed image dataset collection [69]
Image dataset collection [70]
Ground and aerial robot collaboration [71]

3.1.1. Wheeled Robot

The wheeled robot is the most common phenotyping robot. Wheeled robots can be classified broadly into two categories: robots with locally restricted mobility (such as skid steering and differential drive robots) and robots with full mobility (such as omnidirectional robots). Skid steering and differential drive robots are the most common robots used for phenotyping because of their simplicity in mechanical structure and motion control [2428]. Commercial robotic platforms, such as Jackal and Husky from Clearpath, are skid steering robots commonly used for HTFP [24, 72]. Differential drive robots use two drive wheels and passive caster wheels for support [30]. The locomotion of the skid steering and differential drive robot is controlled by the forward/backward and turning speeds of the wheels. It can achieve in-place rotation. Because the turning of skid steering relies on the necessary sliding of the wheel in the lateral direction, skid steering robots have low power efficiency in turning and high wear and tear of tires and can disturb the soil. The difference in rolling resistance or traction in the wheels of a differential drive robot can make the robot turn unexpectedly, making the robot less precise in steering control.

Some wheeled robots use two wheels for steering and two wheels for driving (2WD2WS) [31, 34]. The locomotion is controlled by the forward/backward speed and turning angle, similar to an Ackermann steering, enabling precise steering control. One example is the Phenobot 1.0, which was modified from a small tractor [57]. Its redesigned version, Phenobot 3.0, uses articulated steering [35]. Articulated steered robot has good off-road performance in which the robot was divided into front and rear halves which are connected by a vertical hinge and the steering is controlled by the angle of the two halves. The mechanical complexity of articulated steered robots is increased compared to 2WD2WS robots.

Unlike the above-mentioned robots with restricted locomotion, omnidirectional robots can move in any direction without restrictions, enabling extra maneuverability and terrain adaptation and at the same time increasing the cost and complexity in mechanical structure and motion control. The Thorvald II [36] and Ladybird [37] are two representative four-wheel drive and four-wheel steering (4WD4WS) robots. The modular agricultural robotic system (MARS) is a recently developed phenotyping robotic system featuring a low-cost 3D printable design (MARS mini) and a high-payload high-clearance 4WD4WS robot (MARS X) [40].

3.1.2. Tracked Robot

Tracked robots use tracks to increase the contact area with the ground, and thus, its terrain adaptability is better than the wheeled robot. It can operate on rough terrains and soil conditions (e.g., muddy fields) that wheeled robots are not capable of due to low ground pressure. The kinematics and control of the tracked robot are similar to those of the differential drive wheeled robot. Armadillo and its improved version, Armadillo Scout, are tracked robots featuring a modular design for the track module and a robot computer platform FroboBox running the modular robot architecture, FroboMind, based on ROS [41]. TERRA-MEPP is a tracked robot designed for phenotyping energy sorghum [44]. It uses a tracked platform to carry a vertical, extendable mast (up to 4.88), so the sensor can capture the top view of plants. Phenomobile V2 is a heavy-duty tracked robot that carries a telescopic boom to raise the height of the measurement head mounted on the boom [45]. Commercial tracked robot platforms, such as LT2 from SuperDroid Robots, were used in some studies [43, 65].

3.1.3. Wheel-Legged Robot

Wheel-legged robots combine the advantage of the wheeled and legged robot. It offers the speed as high as the wheeled robot and the high terrain adaptability of the legged robot. Wheel-legged robot can achieve high maneuverability and adjust the robot’s dimensions (width and height) to adapt to different field layouts [73]. One well-known wheel-legged robot is the BoniRob, which has four legs with omnidirectional wheels [67]. This robot can adjust its width and height by adjusting the legs’ posture and can achieve the same maneuverability as a 4WD4WS robot. BoniRob has a detachable module that reconfigures the robot to perform different tasks by changing the module. The downside of the wheel-legged robot is that its complexity increases the cost of the robot and makes it less robust than a wheeled robot. The increased cost makes it uneconomical since the added benefits of wheel-legged robot is not essential for most phenotyping projects.

3.2. Sensors and Manipulators

The primary function of a phenotyping robot is to measure phenotypic traits, so the robot usually carries multiple sensors to capture related information for phenotypic traits. Furthermore, sensors enabling the robot to self-drive and avoid obstacles are necessary. Manipulators are needed when making contact and destructive measurements for certain phenotypic traits, such as the stalk strength of sorghum [26].

3.2.1. Sensors

The sensors used in phenotyping robots include the phenotyping sensors for measuring phenotypic traits and perception sensors for navigation. The phenotyping sensors and the perception sensors can be interchangeable or be independent. The perception sensors are used primarily for localization and path planning. The phenotyping sensors include noncontact sensors, such as imaging sensors, and contact sensors, such as a penetrometer. The most widely used noncontact sensors are the RGB camera, multispectral camera, hyperspectral camera, thermal camera, stereo camera, RGB-D camera, and LiDAR sensor [74, 75]. Most phenotyping robots provide mounting points to carry different sensors according to the targeted phenotypic traits. Some phenotyping robots carry environmental sensors such as soil sensors to measure environmental parameters which are useful metadata for data processing [24, 29, 37].

RGB cameras are the most widely used phenotyping sensor, and RGB images can be used to measure many traits of the crops, such as morphology of the plants [24] and plant organ count [25, 49, 76]. The image quality can be affected by the natural illumination in the field so a light chamber can be used to control the lighting [76]. Artificial lighting can be used when collecting data at night, which can effectively remove the background crops in the image [66]. Strobe lights can be used in the daytime to enhance the foreground [48]. Stereo cameras and RGB-D cameras can provide depth measurements other than RGB images so they can be used to measure 3D structure of the plants. With the depth information, the 3D morphology of the plants can be measured such as canopy size [21] and plant architecture [57, 77]. The depth information can assist the detection and counting of fruits for horticulture crops and estimate the size of the fruits [78]. Similar to RGB cameras, the depth measurement can be greatly affected by the illumination conditions, especially for the RGB-D cameras that use structured light [79]. Therefore, properly controlling the lighting condition in the field is important to improve the measurement accuracy.

Multispectral, hyperspectral, and thermal cameras can provide more spectral information about the crops than an RGB camera. Multispectral and hyperspectral are typically used to measure phenotypic traits that are related to the spectral reflectance of the plants. For example, vegetation indices derived from certain spectral bands like NDVI are related to the physiological activities of the plants, which can be used to detect plant disease [30], abiotic stresses [80], and fruit maturity [81]. Thermal camera is typically used to measure the temperature of the plants, which is correlated with the water status of the plants [80]. Similar to other imaging sensors, multispectral and hyperspectral cameras are affected by the sunlight in the field, which requires in-field calibration to get correct spectral reflectance of the plants. Thermal imaging is less sensitive to sunlight but more easily affected by the atmospheric condition so the environmental conditions should be recorded to calibrate the thermal image.

LiDAR sensors measure the distance to the target based on the time-of-flight principle using an active laser pulse. Thus, it is not limited by the lighting conditions and covers a larger sensing range than the stereo and RGB-D camera. Each laser scan can generate the shape profile of the plants of one layer from 2D LiDAR or multiple layers from 3D LiDAR. Registration of the laser scans using their position and posing generates a 3D point cloud of the plants, which can be used to measure the morphological traits of the plants [34]. Therefore, accurate localization of the robot is important for the registration of the laser scans.

3.2.2. Manipulators

Manipulators, primarily robotic arms, are used commonly in agricultural robots, such as weeding and harvesting robots. However, manipulators are not very common in phenotyping because most phenotypic traits can be measured remotely. Manipulators are useful for phenotyping robots when the phenotypic traits need to be measured in contact or at a specific location (e.g., certain leaf). For example, the Robotanist robot uses a three degree-of-freedom robotic arm to measure the stalk strength of sorghum [26]. Sensors mounted on the robotic arm can be used to change sensing position/pose actively, such as sensing individual plants from multiple viewing angles [24, 82, 83]. Other applications include collecting biological samples [84], leaf probing [85], soil sampling [29, 68], digging plants for root phenotyping, and fruit mapping [86].

3.3. Computing Unit and Software

The computing unit in phenotyping robot has two main tasks: performing autonomous navigation and collecting the phenotyping data. They are sometimes independent of each other. Single-board computers and embedded systems are used commonly in robotic systems because of their small size, low power consumption, and lightweight. However, their computing resources are usually limited. The selection of a computing unit should consider power consumption, computing performance, size, weight, interfaces, and supported operating system.

Although a single computing unit can be used for both autonomous navigation and data collection, a common design is to use a dedicated computing unit for each task. This design brings two benefits. First, appropriate computing units can be selected based on the computing resources required by each task. For example, some embedded systems that are dedicated to autonomous vehicles such as Pixhawk [87] can be used for autonomous navigation [34]. For collecting phenotyping data, a computing unit with more computing resources (e.g., PC or industrial computer) may be needed to handle the large data volume from imaging sensors. Second, it can make an independent system for data collection so it can be deployed on different robotic platforms. It is also easier to add/replace phenotyping sensors. The drawback of using several computing units is the higher communication overhead and hardware costs than using a single computing unit.

The Robot Operating System (ROS) is a widely used middleware framework for developing robotic software because it provides an integrated environment that can greatly accelerate software development [88]. ROS has become an industrial standard for robotics and supports a wide range of hardware and algorithms commonly used in robotics, but with constraints such as not supporting real-time control. The ROS 2, a newer version of ROS, was developed to support real-time control, microcontroller, and multiple robots and platforms [89]. FroboMind is a software architecture built upon ROS and designed for agricultural robots [90]. LabVIEW was used by some robots for control and data collection [39, 43]. Other robot software architectures can be found in [90].

3.4. Navigation

Navigation is an essential component of automation in robotics and includes three fundamental problems: localization, path planning, and map building. A typical agricultural environment includes many crop rows in straight lines, and the robot needs to travel along the crop rows. Therefore, a phenotyping robot’s primary navigation objective is to follow the crop row and switch between rows. GNSS, vision sensors, and LiDAR sensors are commonly used for localization and path planning. GNSS and IMU can be used to obtain the global position and posing. Vision sensors and LiDAR sensors can be used for localization and obstacle detection using the simultaneous localization and mapping (SLAM) algorithms [91]. This paper focuses on the navigation algorithms based on these sensors in the agricultural environment. Other navigation methods such as magnetic-based navigation that are not commonly used for agricultural robots were not reviewed.

3.4.1. GNSS-Based Navigation

As a global positioning technology, GNSS has been used widely to localize robots in field applications. GNSS-based guidance systems have been developed for agricultural machinery and robots [92]. The RTK-GNSS can provide positioning accuracy up to a centimeter but is not always adequate for localization when used as a single positioning sensor. The positioning accuracy of GNSS can be affected by the obstruction of line-of-sight to satellites, multipath issues, and interference from other radio frequency (RF) sources. In addition, GNSS does not provide accurate heading measurement. Therefore, it is typically used with other sensors, such as the IMU and wheel encoder, to improve the localization accuracy.

The typical application of GNSS-based navigation is to make the robot follow preset paths using path-following algorithms, such as pure pursuit controller and its variants [93]. The path-following algorithm can be designed using conventional control theories, which require the robot’s kinematic model [64]. Deep reinforcement learning can also be used for following paths, which does not require the robot’s kinematic model, and can learn the kinematics implicitly through training [94].

In agricultural environments such as orchards, the GNSS can be unreliable because the robot frequently could move under a tree canopy blocking the satellite’s signals to the GNSS receiver. The GNSS-based navigation is not suitable for dynamic environments with unexpected changes or events in the environment. In those cases, vision-based and LiDAR-based navigation algorithms can be used.

3.4.2. Vision-Based Navigation

Vision-based navigation keeps the robot following crop rows using machine vision. RGB cameras typically are used to detect crop rows and calculate the robot’s orientation relative to the crop row [95]. Stereo vision can provide depth information, which can help detect crop rows with different illumination conditions and weed pressure than a single camera [96]. Besides the traditional machine vision techniques, the deep learning methods can obtain directly the crop row’s orientation from raw images [97].

Vision-based navigation relies on the image feature of the crop rows and can suffer from illumination changes and lack of texture [96]. Typically, it is used with GNSS guidance to improve the robustness, for example, fusing the vision guidance and GNSS guidance results or using the vision guidance for row following and switching to GNSS guidance when the robot shifts between rows.

3.4.3. LiDAR-Based Navigation

LiDAR can measure the distance between objects. Like vision-based navigation, LiDAR-based navigation relies on landmarks that can differentiate crop rows, such as the plant, trunk, and poles in a polytunnel [54, 60, 98]. The crop row measured by LiDAR sensors is represented as points along with some noise. Because the LiDAR sensor is subject to noise, it is difficult to detect crop rows from noisy points. A standard method is to detect the crop row using line detection algorithms, such as Hough transform and random sample consensus (RANSAC) [54, 60]. Another method is to model the LiDAR measurements and noise using a particle filter and estimate the robot’s heading and lateral deviation relative to the crop row [99, 100].

Using the LiDAR sensor alone can make it challenging to understand the surrounding environment because of the coarse data. The vision sensor can be used to provide complementary information to exclude the LiDAR points of no interest from data processing. For example, image features were used to separate the LiDAR points of the trunk from other objects in vineyards so that crop rows could be detected correctly [101]. The LiDAR sensor also can be used for obstacle avoidance, but can falsely detect grass, weed, and plant leaves as obstacles, so using vision sensors can help identify real obstacles.

3.5. Simulation

Simulation of the robotic system and its operating environment can accelerate the development of robotic systems through quick and efficient tests and validation of the robot’s design without physically building the robots (Figure 3) [46, 54, 102, 103]. Simulation is also useful for developing and testing control algorithms, navigation algorithms, and data processing algorithms [60, 94, 103, 104]. It is easy to create repeatable testing conditions in simulations for the robot, a process that can be difficult in a real environment.

There are many simulation software/platforms, and popular ones include Gazebo [105], Webots [106], and V-REP (now called CoppeliaSim) [107]. All three simulation platforms can provide a complete simulation environment to model and program a wide range of mobile robots and sensors. Gazebo is one of the most popular multirobot simulators which support a wide range of sensors and objects. It is open-source and is compatible with ROS and thus is used by many phenotyping robots for simulation [46, 51, 54, 102, 103]. However, Gazebo currently only supports Linux systems and lacks a good user interface. Webots and V-REP are cross-platform software, support multiple programming languages, and can be interfaced with third-party applications. Webots and V-REP were initially developed by industrial companies and are free to use now. A complete review of the simulation platforms can be found in [108]. Some simulators and frameworks customized for agricultural robotics and farm machinery have been designed based on professional simulation platforms, such as the Agricultural Architecture (Agriture) [109] and AgROS [110].

4. Applications of Phenotyping Robot

The primary mission of a phenotyping robot is to measure phenotypic traits of plants. The data collected by the phenotyping robots can be used for various purposes. We grouped the applications into three categories based on the phenotypic traits and usage of the traits. The three categories are crop organ identification and counting, crop detection and classification, and crop growth monitoring, as summarized in Table 3.

CropKey issuesRobotPhenotyping sensorData processing methodReference

Organ identification and counting
AlmondAlmond fruit detectionShrimp [25]RGB cameraUse Faster R-CNN to detect fruit in the color image[49]
Not mentionedPlant detection and leaf countBoniRob [67]RGB cameraA customized single-stage object detection network based on FCN[113]
KiwifruitKiwifruit detectionCustomized tracked robotRGB cameraImage features were extracted and classified using machine learning[111]
MangoMango fruit detection, localization, and yield predictionShrimp [25]RGB camera
LiDAR sensor
Use FR-CNN for detecting fruits in the color image
Use LiDAR point cloud and a hidden semi-Markov model to separate individual tree
Use epipolar geometry to track fruits and triangulation for fruit localization

Crop detection and classification
CornCorn plant detection and mappingVolksbot RT-3LiDAR sensorDetect a plan as ground
Cluster the nonground points into individual plants
CornCorn stalk count and stalk width estimationRobotanist [26]Stereo cameraUse Faster R-CNN to detect stalk and FCN to get stalk mask
Use stalk mask to estimate stalk width
CornCorn stand countTerraSentia [27]RGB cameraUse Faster R-CNN to detect corn stand[27, 114]
CarrotWeed detectionBoniRob [67]Multispectral cameraClassification of weed and plant is achieved using the Random Forest classifier[69, 116]
Sugar beetDataset collection for plant classification, localization, and mappingBoniRob [67]Multispectral camera
RGB-D camera
LiDAR sensor

Crop growth monitoring
SorghumSorghum height and stem diameter estimationPhenobot 1.0 [32]Stereo cameraReconstruct dense point cloud from stereo image
Plant height and stem diameter were extracted from the dense point cloud
SorghumSorghum height, width, stem diameter, plant volume, and surface area estimationPhenobot 1.0 [32]Stereo cameraUse convex hull to estimate plant volume and surface area[57]
CornCorn stalk diameter estimationCustomized skid steering robotRGB-D cameraUse YOLO V4 to detect corn stalk[55]
CornPlant height, leaf area index estimationVinobot [24]RGB camera3D point cloud of the plant was constructed using structure from motion
Plant height and leaf area index were calculated from the point cloud
AlmondMapping canopy volume, flowers, fruit, and yield estimationShrimp [25]RGB camera
LiDAR sensor
Use color image to detect flower and fruits
Use canopy volume from LiDAR point cloud to estimate yield
RyegrassRyegrass biomass yield estimationDairyBioBot [34]LiDAR sensorEstimate the plant volume from LiDAR point cloud and correlate with the yield[34]

4.1. Crop Organ Identification and Counting

The high-resolution data collected by the ground phenotyping robots can be used to detect plant traits at the organ level, which cannot be achieved by aerial systems. RGB images can be used to detect the plant organs, such as crop leaf and fruit, using machine learning methods. For example, a customized tracked robot was developed to collect RGB images of kiwifruits and an image processing algorithm using traditional machine learning methods was designed to count the fruits [111]. Deep learning methods can be used to detect plant organs by designing and training appropriate neural networks [112]. A customized neural network model was designed to detect and localize crop leaves using the RGB images collected by BoniRob [113]. Mango fruits were detected and counted from RGB images collected by the Shrimp robot using a Faster R-CNN model [25].

4.2. Crop Detection and Classification

Detection of crops and weeds can be used for many applications, such as weed control and plant count. RGB images can be used to detect the plants or weeds using deep learning methods. For example, RGB images from TerraSentia can be used to detect and count the corn stand using Faster R-CNN [27, 114]. When the corn plants grow tall, Robotanist can run between crop rows and count the plants by detecting the corn stalk using Faster R-CNN [50]. The width of the corn stalk can be measured from the stereo images. Plants can also be detected using LiDAR sensor by detecting the ground plane and separating the plants using clustering algorithms [115]. Machine learning-based methods for crop and weed detection require large training dataset, which can be collected by phenotyping robots. An image dataset was collected using BoniRob for weed detection in a carrot field [69], and the weeds were detected using Random Forest [116]. BoniRob also was used to collect datasets containing georeferenced multispectral images, and RGB-D images and LiDAR data were collected for plant classification, localization, and mapping in a sugar beet field [70].

4.3. Crop Growth Monitoring

The growth conditions of the crops can be reflected by many morphological traits. The 3D model of the plants can be obtained from RGB images using Structure from Motion [24], depth images from stereo or RGB-D cameras [32, 55], or LiDAR sensors [34, 48]. Plant height, width, stem diameter, plant volume, and surface area can be estimated from the 3D model [32, 57]. The corn stalk diameter was estimated using a RGB-D camera, where the RGB image was used to detect the corn stalk and the depth image was used to measure the stalk diameter [55]. The plant volume of perennial ryegrass was measured using a LiDAR sensor on DairyBioBot, which was correlated with the biomass [34]. The canopy volume of almond trees were measured using a LiDAR sensor on the Shrimp robot, which was shown to be correlated with the yield [48]. The flower and fruit density of the almond tree was also measured from RGB images.

5. Challenges and Future Perspectives

5.1. Challenges

Despite recent advances in sensors and robotics, designing a phenotyping robot that can work in unstructured and dynamically changing agricultural environments can be challenging. There remain several major challenges. First, some phenotyping robots have been designed for specific crops and field layouts, which limits their use in other crops and field layouts. For example, robots designed for vineyards, such as PHENObot, may not be suitable for row crops because the robots’ dimensions cannot fit within the row spacing [42]. The changes in height and size of the plants due to growth also limit the usage of the robot throughout the growing season. For example, it can be difficult to run a robot between crop rows without damaging the plants when the canopy grows into each other. The robot’s design (e.g., the dimension of the robot) is constrained by agronomic practices such as row spacing and the dimension of the crops, which usually vary crop by crop, making it challenging to design a robot to work properly under the constraints without sacrificing the functionality. Second, the costs of phenotyping robots are prohibitively high in most cases [1]. The mobile platform itself may cost tens of thousands of dollars, and the total cost of a phenotyping robot is even higher with perception and phenotyping sensors [117]. Although some low-cost robots, such as the TerraSentia, have been developed, their use has been limited because of the low payload and small size of these robots. Third, the data collection efficiency of phenotyping robots remains too low for large fields with tens of thousands of plots in practice. For example, a single robot would take at least 1.7 hours to scan 1000 plots of 3 m length at a travel speed of [per-mode = repeated-symbol] 0.5. The lengthy scanning time can make time-sensitive traits (e.g., canopy temperature) unreliable across plots. Fourth, navigation in cluttered environments is challenging, especially in GNSS-denied areas such as under subcanopy. A complex navigation algorithm using vision or LiDAR is needed for those environments [118]. Fifth, data processing and phenotypic trait extraction are mainly done offline in most cases, which is not usable for real-time decision-making and online control. More robust and efficient perception and control methods are needed. Six, regulation and robot safety should be taken into consideration when designing and operating the robot, which can potentially increase the operation cost of the robot and limit its usages in some countries/areas [119, 120].

5.2. Future Perspectives

To address the above-mentioned challenges and further advance automated phenotyping, there are several future research directions for phenotyping robots. First, it is important to develop reconfigurable robots with a modular design to adapt to different cropping systems in terms of plant height, row spacing, and field layout. A few researchers and companies have developed multipurpose modular robotic platforms such as BoniRob, Thorvald II, and MARS [40]. The modular design in both hardware modules and software modules enables the robots to be flexible in operating in various environments, for instance, greenhouses, polytunnels, and open fields. In addition to be flexible, modularity also brings several other benefits including (1) reduced total cost by reusing the modules to perform phenotyping tasks for different crops and (2) easier and inexpensive maintenance by replacing and repairing only the failed modules without changing the whole robot.

Second, innovative mechanical designs of the mobile platform can be explored to improve the data quality in fields with complex terrains. One promising research direction is legged robot. Not many legged robots have been developed for agricultural purposes due to the complexity of controlling the robot’s locomotion and its low efficiency working on large farms [121]. The recent advances in robotic technologies and the commercial success of legged robots, such as the Spot from Boston Dynamics, demonstrated its potential for HTFP [122]. Low-cost open-source quadruped robots from academic institutions, such as the Mini Cheetah, also open the possibility to customize the legged robots for HTFP [123].

Third, to address the low-throughput issue for a single mobile robot, one solution is to deploy a team of heterogeneous autonomous mobile robots (i.e., robot swarms) to work collectively and cooperatively to cover a large field. The heterogeneous robots may possess different sensing capabilities (e.g., multispectral imagery for plant stress detection and LiDAR for plant growth monitoring), internal characteristics (e.g., payload, speed, and robot dynamics), and available resources (e.g., remaining battery power). Researchers have investigated this problem with the distributed coverage control approach that models the field as a weighted directed graph and uses partitioning algorithm to assign the tasks to each agent optimally [124]. Coordination between UGV and UAV has been demonstrated to achieve the best efficiency by combining the benefits of ground and aerial systems [71]. For example, UAV can quickly scan the field to find the areas of interest that need further scans for UGV, reducing the overall data collection time for UGV by focusing on the areas that require high-resolution data.

Fourth, we envision that robust low-cost global positioning method for navigation in complex and GNSS-denied environments will replace expensive Real-Time Kinematic (RTK) GNSS-based navigation. One promising solution is to fuse multiple consumer-grade low-cost sensors (such as low-cost GPS and stereo camera for visual odometry) with additional constraints such as digital elevation model provided by UAV and leverage the 6D pose graph optimization method to achieve accurate and reliable global positioning for mobile robots [118]. The benefits of this approach are multifaceted: it is low cost and more robust against issues such as multipath interference, and most importantly, it can provide the full 6D pose (translation and rotation) that conventional RTK GNSS cannot provide. It is expected that more research advancements in this direction will occur in the coming years.

Fifth, deep learning is expected to have a significant impact on phenotyping robots in robot perception and control. In terms of robot perception, one type of deep learning model called convolutional neural networks (CNNs) has consistently outperformed traditional machine learning techniques in important computer vision tasks, such as image classification/regression, object detection, and semantic/instance segmentation [112]. CNNs are expected to be deployed on the robot through edge computing for real-time inference to help robot understand the scene and to extract phenotypic traits. In terms of robot control, one important AI technique called deep reinforcement learning is expected to play an increasingly important role in path planning and trajectory following [125].

Data Availability

This review paper does not contain research data to be shared.

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Authors’ Contributions

RX and CL conceptualized the manuscript. RX drafted the manuscript, and CL substantially edited the manuscript. Both authors approved the submitted manuscript.


This work was partially supported by the USDA-NIFA under Grant No. 2017-67021-25928 and National Science Foundation under Grant No. 1934481.


  1. A. Atefi, Y. Ge, S. Pitla, and J. Schnable, “Robotic technologies for high-throughput plant phenotyping: contemporary reviews and future perspectives,” Frontiers in Plant Science, vol. 12, 2021. View at: Publisher Site | Google Scholar
  2. Y. Bao, J. Gai, L. Xiang, and L. Tang, “Field robotic systems for high-throughput plant phenotyping: a review and a case study,” in High-Throughput Crop Phenotyping, J. Zhou and H. T. Nguyen, Eds., pp. 13–38, Springer International Publishing, Cham, 2021. View at: Publisher Site | Google Scholar
  3. A. Comar, P. Burger, B. de Solan, F. Baret, F. Daumard, and J.-F. Hanocq, “A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: description and first results,” Functional Plant Biology, vol. 39, no. 11, pp. 914–924, 2012. View at: Publisher Site | Google Scholar
  4. L. Busemeyer, D. Mentrup, K. Möller et al., “BreedVision — a multi-sensor platform for non-destructive field-based phenotyping in plant breeding,” Sensors, vol. 13, no. 3, pp. 2830–2847, 2013. View at: Publisher Site | Google Scholar
  5. P. Andrade-Sanchez, M. A. Gore, J. T. Heun et al., “Development and evaluation of a field-based high-throughput phenotyping platform,” Functional Plant Biology, vol. 41, no. 1, pp. 68–79, 2013. View at: Publisher Site | Google Scholar
  6. B. Sharma and G. L. Ritchie, “High-throughput phenotyping of cotton in multiple irrigation environments,” Crop Science, vol. 55, no. 2, pp. 958–969, 2015. View at: Publisher Site | Google Scholar
  7. A. Kicherer, K. Herzog, N. Bendel et al., “Phenoliner: a new field phenotyping platform for grapevine research,” Sensors, vol. 17, no. 7, p. 1625, 2017. View at: Publisher Site | Google Scholar
  8. Y. Jiang, C. Li, J. S. Robertson, S. Sun, R. Xu, and A. H. Paterson, “GPhenoVision: a ground mobile system with multi-modal imaging for field- based high throughput phenotyping of cotton,” Scientific Reports, vol. 8, no. 1, pp. 1–15, 2018. View at: Publisher Site | Google Scholar
  9. N. Higgs, B. Leyeza, J. Ubbens et al., “ProTractor: a lightweight ground imaging and analysis system for early-season field phenotyping,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA, 2019. View at: Google Scholar
  10. G. Bai, Y. Ge, W. Hussain, P. S. Baenziger, and G. Graef, “A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding,” Computers and Electronics in Agriculture, vol. 128, pp. 181–192, 2016. View at: Publisher Site | Google Scholar
  11. J. L. Crain, Y. Wei, J. Barker III et al., “Development and deployment of a portable field phenotyping platform,” Crop Science, vol. 56, no. 3, pp. 965–975, 2016. View at: Publisher Site | Google Scholar
  12. A. L. Thompson, K. R. Thorp, M. Conley et al., “Deploying a proximal sensing cart to identify drought-adaptive traits in upland cotton for high-throughput phenotyping,” Frontiers in Plant Science, vol. 9, p. 507, 2018. View at: Publisher Site | Google Scholar
  13. D. Kumar, S. Kushwaha, C. Delvento et al., “Affordable phenotyping of winter wheat under field and controlled conditions for drought tolerance,” Agronomy, vol. 10, no. 6, p. 882, 2020. View at: Publisher Site | Google Scholar
  14. K. Meacham-Hensold, P. Fu, J. Wu et al., “Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging,” Journal of Experimental Botany, vol. 71, no. 7, pp. 2312–2328, 2020. View at: Publisher Site | Google Scholar
  15. A. L. Thompson, A. Conrad, M. M. Conley et al., “Professor: a motorized field-based phenotyping cart,” Hardware X, vol. 4, article e00025, 2018. View at: Publisher Site | Google Scholar
  16. M. Pérez-Ruiz, A. Prior, J. Martinez-Guanter, O. E. Apolo-Apolo, P. Andrade-Sanchez, and G. Egea, “Development and evaluation of a self-propelled electric platform for high- throughput field phenotyping in wheat breeding trials,” Computers and Electronics in Agriculture, vol. 169, article 105237, 2020. View at: Publisher Site | Google Scholar
  17. N. Virlet, K. Sabermanesh, P. Sadeghi-Tehran, and M. J. Hawkesford, “Field scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring,” Functional Plant Biology, vol. 44, no. 1, pp. 143–153, 2017. View at: Publisher Site | Google Scholar
  18. K. Beauchêne, F. Leroy, A. Fournier et al., “Management and characterization of Abiotic Stress via PhénoField®, a high-throughput field phenotyping platform,” Frontiers in plant science, vol. 10, 2019. View at: Publisher Site | Google Scholar
  19. N. Kirchgessner, F. Liebisch, K. Yu et al., “The eth field phenotyping platform FIP: a cable-suspended multi-sensor system,” Functional Plant Biology, vol. 44, no. 1, pp. 154–168, 2017. View at: Publisher Site | Google Scholar
  20. G. Bai, Y. Ge, D. Scoby et al., “NU-Spidercam: a large-scale, cable-driven, integrated sensing and robotic system for advanced phenotyping, remote sensing, and agronomic research,” Computers and Electronics in Agriculture, vol. 160, pp. 71–81, 2019. View at: Publisher Site | Google Scholar
  21. Y. Jiang, C. Li, A. H. Paterson, S. Sun, R. Xu, and J. Robertson, “Quantitative analysis of cotton canopy size in field conditions using a consumer-grade RGB-D camera,” Frontiers in Plant Science, vol. 8, p. 2233, 2018. View at: Publisher Site | Google Scholar
  22. M. Burnette, R. Kooper, J. D. Maloney et al., “Terra-ref data processing infrastructure,” in Proceedings of the Practice and Experience on Advanced Research Computing, pp. 1–7, New York, NY, USA, 2018. View at: Google Scholar
  23. X. Zhou and S. Bi, “A survey of bio-inspired compliant legged robot designs,” Bioinspiration & Biomimetics, vol. 7, no. 4, p. 041001, 2012. View at: Publisher Site | Google Scholar
  24. A. Shafiekhani, S. Kadam, F. B. Fritschi, and G. N. DeSouza, “Vinobot and vinoculer: two robotic platforms for high-throughput field phenotyping,” Sensors, vol. 17, no. 12, p. 214, 2017. View at: Publisher Site | Google Scholar
  25. M. Stein, S. Bargoti, and J. Underwood, “Image based mango fruit detection, localisation and yield estimation using multiple view geometry,” Sensors, vol. 16, no. 11, p. 1915, 2016. View at: Publisher Site | Google Scholar
  26. T. Mueller-Sim, M. Jenkins, J. Abel, and G. Kantor, “The robotanist: a ground-based agricultural robot for high-throughput crop phenotyping,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 3634–3639, Singapore, 2017. View at: Publisher Site | Google Scholar
  27. Z. Zhang, E. Kayacan, B. Thompson, and G. Chowdhary, “High precision control and deep learning-based corn stand counting algorithms for agricultural robot,” Autonomous Robots, vol. 44, no. 7, pp. 1289–1302, 2020. View at: Publisher Site | Google Scholar
  28. R. Guzmán, J. Ariño, R. Navarro et al., “Autonomous hybrid gps/reactive navigation of an unmanned ground vehicle for precision viticulture-vinbot,” in 62nd German Winegrowers Conference, Stuttgart, 2016. View at: Google Scholar
  29. J. Iqbal, R. Xu, H. Halloran, and C. Li, “Development of a multi-purpose autonomous differential drive mobile robot for plant phenotyping and soil sensing,” Electronics, vol. 9, no. 9, p. 1550, 2020. View at: Publisher Site | Google Scholar
  30. S. Cubero, E. Marco-Noales, N. Aleixos, S. Barbé, and J. Blasco, “RobHortic: a field robot to detect pests and diseases in horticultural crops by proximal sensing,” Agriculture, vol. 10, no. 7, p. 276, 2020. View at: Publisher Site | Google Scholar
  31. O. Bawden, J. Kulk, R. Russell et al., “Robot for weed species plant-specific management,” Journal of Field Robotics, vol. 34, no. 6, pp. 1179–1199, 2017. View at: Publisher Site | Google Scholar
  32. M. G. S. Fernandez, Y. Bao, L. Tang, and P. S. Schnable, “A high-throughput, field-based phenotyping technology for tall biomass crops,” Plant Physiology, vol. 174, no. 4, pp. 2008–2022, 2017. View at: Publisher Site | Google Scholar
  33. Q. Qiu, Z. Fan, Z. Meng et al., “Extended Ackerman steering principle for the coordinated movement control of a four wheel drive agricultural mobile robot,” Computers and Electronics in Agriculture, vol. 152, pp. 40–50, 2018. View at: Publisher Site | Google Scholar
  34. P. Nguyen, P. E. Badenhorst, F. Shi, G. C. Spangenberg, K. F. Smith, and H. D. Daetwyler, “Design of an unmanned ground vehicle and lidar pipeline for the high-throughput phenotyping of biomass in perennial ryegrass,” Remote Sensing, vol. 13, no. 1, p. 20, 2021. View at: Publisher Site | Google Scholar
  35. T. L. Tuel, A robotic proximal sensing platform for in-field high-throughput crop phenotyping [Ph. D. dissertation], Iowa State University, 2019.
  36. L. Grimstad and P. J. From, “The thorvald ii agricultural robotic system,” Robotics, vol. 6, no. 4, p. 24, 2017. View at: Publisher Site | Google Scholar
  37. J. Underwood, A. Wendel, B. Schofield, L. McMurray, and R. Kimber, “Efficient in-field plant phenomics for row-crops with an autonomous ground vehicle,” Journal of Field Robotics, vol. 34, no. 6, pp. 1061–1083, 2017. View at: Publisher Site | Google Scholar
  38. S. Madec, F. Baret, B. de Solan et al., “High-throughput phenotyping of plant height: comparing unmanned aerial vehicles and ground lidar estimates,” Frontiers in Plant Science, vol. 8, p. 2002, 2017. View at: Publisher Site | Google Scholar
  39. J. N. Murman, Flex-Ro: A Robotic High Throughput Field Phenotyping System, University of Nebraska-Lincoln, 2019.
  40. R. Xu and C. Li, “Development of the modular agricultural robotic system (MARS): concept and implementation,” Journal of Field Robotics, vol. 39, p. 387, 2022. View at: Google Scholar
  41. K. Jensen, S. H. Nielsen, R. N. Joergensen et al., “A low cost, modular robotics tool carrier for precision agriculture research,” in Proc Int Conf on Precision Agriculture, Indianapolis, IN, United States, 2012. View at: Google Scholar
  42. A. Kicherer, K. Herzog, M. Pflanz et al., “An automated field phenotyping pipeline for application in grapevine research,” Sensors, vol. 15, no. 3, pp. 4823–4836, 2015. View at: Publisher Site | Google Scholar
  43. A. Stager, H. G. Tanner, and E. E. Sparks, “Design and construction of unmanned ground vehicles for sub-canopy plant phenotyping,” 2019, View at: Google Scholar
  44. S. N. Young, E. Kayacan, and J. M. Peschel, “Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum,” Precision Agriculture, vol. 20, no. 4, pp. 697–722, 2019. View at: Publisher Site | Google Scholar
  45. F. Baret, B. de Solan, S. Thomas et al., “Phenomobile: A fully automatic robot for high-throughput field phenotyping of a large range of crops with active measurements,” April 2022, View at: Google Scholar
  46. P. Biber, U. Weiss, M. Dorna, and A. Albert, “Navigation system of the autonomous agricultural robot bonirob,” in Workshop on Agricultural Robotics: Enabling Safe, Efficient, and Affordable Robots for Food Production (Collocated with IROS 2012), Vilamoura, Portugal, 2012. View at: Google Scholar
  47. C. M. Lopes, J. Graça, J. Sastre et al., “Vineyard yeld estimation by vinbot robot-preliminary the white variety viosinho,” in Proceedings 11th Int. Terroir Congress, N. Jones, Ed., pp. 458–463, Southern Oregon University, Ashland, USA, 2016. View at: Google Scholar
  48. J. P. Underwood, C. Hung, B. Whelan, and S. Sukkarieh, “Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors,” Computers and Electronics Agriculture, vol. 130, pp. 83–96, 2016. View at: Publisher Site | Google Scholar
  49. S. Bargoti and J. Underwood, “Deep fruit detection in orchards,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 3626–3633, Singapore, 2017. View at: Publisher Site | Google Scholar
  50. H. S. Baweja, T. Parhar, O. Mirbod, and S. Nuske, “Stalknet: a deep learning pipeline for high-throughput measurement of plant stalk count and stalk width,” in Field and Service Robotics, pp. 271–284, Springer, 2018. View at: Publisher Site | Google Scholar
  51. A. Shafiekhani, F. B. Fritschi, and G. N. DeSouza, “Vinobot and vinoculer: from real to simulated platforms,” in Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, J. A. Thomasson, M. McKee, and R. J. Moorhead, Eds., vol. 10664, pp. 90–98, International Society for Optics and Photonics, 2018. View at: Publisher Site | Google Scholar
  52. A. Choudhuri and G. Chowdhary, “Crop stem width estimation in highly cluttered field environment,” in Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP 2018), pp. 6–13, Newcastle, UK, 2018. View at: Google Scholar
  53. V. A. Higuti, A. E. Velasquez, D. V. Magalhaes, M. Becker, and G. Chowdhary, “Under canopy light detection and ranging-based autonomous navigation,” Journal of Field Robotics, vol. 36, no. 3, pp. 547–567, 2019. View at: Publisher Site | Google Scholar
  54. J. Iqbal, R. Xu, S. Sun, and C. Li, “Simulation of an autonomous mobile robot for lidar-based in-field phenotyping and navigation,” Robotics, vol. 9, no. 2, p. 46, 2020. View at: Publisher Site | Google Scholar
  55. Z. Fan, N. Sun, Q. Qiu, T. Li, and C. Zhao, “A high-throughput phenotyping robot for measuring stalk diameters of maize crops,” in 2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 128–133, Jiaxing, China, 2021. View at: Publisher Site | Google Scholar
  56. D. Hall, F. Dayoub, J. Kulk, and C. McCool, “Towards unsupervised weed scouting for agricultural robotics,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5223–5230, Singapore, 2017. View at: Publisher Site | Google Scholar
  57. Y. Bao, L. Tang, M. W. Breitzman, M. G. Salas Fernandez, and P. S. Schnable, “Field-based robotic phenotyping of sorghum plant architecture using stereo vision,” Journal of Field Robotics, vol. 36, no. 2, pp. 397–415, 2019. View at: Publisher Site | Google Scholar
  58. Q. Qiu, N. Sun, H. Bai et al., “Field-based high-throughput phenotyping for maize plant using 3D LiDAR point cloud generated with a “Phenomobile”,” Frontiers in Plant Science, vol. 10, p. 554, 2019. View at: Publisher Site | Google Scholar
  59. Y. Xiong, C. Peng, L. Grimstad, P. J. From, and V. Isler, “Development and field evaluation of a strawberry harvesting robot with a cable-driven gripper,” Computers and Electronics in Agriculture, vol. 157, pp. 392–402, 2019. View at: Publisher Site | Google Scholar
  60. T. D. Le, V. R. Ponnambalam, J. G. Gjevestad, and P. J. From, “A low-cost and efficient autonomous row-following robot for food production in polytunnels,” Journal of Field Robotics, vol. 37, no. 2, pp. 309–321, 2020. View at: Publisher Site | Google Scholar
  61. J. P. Underwood, M. Calleija, Z. Taylor et al., “Real-time target detection and steerable spray for vegetable crops,” in Proceedings of the International Conference on Robotics and Automation: Robotics in Agriculture Workshop, pp. 26–30, Seattle, WA, USA, 2015. View at: Google Scholar
  62. A. Bender, B. Whelan, and S. Sukkarieh, “A high-resolution, multimodal data set for agricultural robotics: a Ladybird’s-eye view of Brassica,” Journal of Field Robotics, vol. 37, no. 1, pp. 73–96, 2020. View at: Publisher Site | Google Scholar
  63. S. Liu, F. Baret, M. Abichou et al., “Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model,” Agricultural and Forest Meteorology, vol. 247, pp. 12–20, 2017. View at: Publisher Site | Google Scholar
  64. X. Tu, J. Gai, and L. Tang, “Robust navigation control of a 4wd/4ws agricultural robotic vehicle,” Computers and Electronics in Agriculture, vol. 164, article 104892, 2019. View at: Publisher Site | Google Scholar
  65. T. Gao, H. Emadi, H. Saha et al., “A novel multirobot system for plant phenotyping,” Robotics, vol. 7, no. 4, p. 61, 2018. View at: Publisher Site | Google Scholar
  66. J. C. Rose, A. Kicherer, M. Wieland, L. Klingbeil, R. Töpfer, and H. Kuhlmann, “Towards automated large-scale 3d phenotyping of vineyards under field conditions,” Sensors, vol. 16, no. 12, p. 2136, 2016. View at: Publisher Site | Google Scholar
  67. A. Ruckelshausen, P. Biber, M. Dorna et al., “Bonirob–an autonomous field robot platform for individual plant phenotyping,” Precision Agriculture, vol. 9, no. 841, p. 1, 2009. View at: Google Scholar
  68. C. Scholz, K. Moeller, A. Ruckelshausen, S. Hinck, and M. Goettinger, “Automatic soil penetrometer measurements and gis based documentation with the autonomous field robot platform bonirob,” in 12th International Conference of Precision Agriculture, Sacramento, CA, USA, 2014. View at: Google Scholar
  69. S. Haug and J. Ostermann, “A crop/weed field image dataset for the evaluation of computer vision based precision agriculture tasks,” in European Conference on Computer Vision, pp. 105–116, Springer, 2014. View at: Google Scholar
  70. N. Chebrolu, P. Lottes, A. Schaefer, W. Winterhalter, W. Burgard, and C. Stachniss, “Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields,” The International Journal of Robotics Research, vol. 36, no. 10, pp. 1045–1052, 2017. View at: Publisher Site | Google Scholar
  71. A. Pretto, S. Aravecchia, W. Burgard et al., “Building an aerial–ground robotics system for precision farming: an adaptable solution,” IEEE Robotics & Automation Magazine, vol. 28, no. 3, pp. 29–49, 2021. View at: Google Scholar
  72. G. S. Sampaio, L. A. Silva, and M. Marengoni, “3D reconstruction of non-rigid plants and sensor data fusion for agriculture phenotyping,” Sensors, vol. 21, no. 12, p. 4115, 2021. View at: Publisher Site | Google Scholar
  73. P. Gonzalez-De-Santos, R. Fernández, D. Sepúlveda, E. Navas, and M. Armada, Unmanned Ground Vehicles for Smart Farms, Intech Open, 2020. View at: Publisher Site
  74. F. Y. Narvaez, G. Reina, M. Torres-Torriti, G. Kantor, and F. A. Cheein, “A survey of ranging and imaging techniques for precision agriculture phenotyping,” IEEE/ASME Transactions on Mechatronics, vol. 22, no. 6, pp. 2428–2439, 2017. View at: Publisher Site | Google Scholar
  75. X. Jin, P. J. Zarco-Tejada, U. Schmidhalter et al., “High-throughput estimation of crop traits: a review of ground and aerial phenotyping platforms,” IEEE Geoscience and Remote Sensing Magazine, vol. 9, no. 1, pp. 200–231, 2021. View at: Publisher Site | Google Scholar
  76. Y. Jiang, C. Li, R. Xu, S. Sun, J. S. Robertson, and A. H. Paterson, “DeepFlower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field,” Plant Methods, vol. 16, no. 1, pp. 1–17, 2020. View at: Publisher Site | Google Scholar
  77. Z. Fan, N. Sun, Q. Qiu, T. Li, Q. Feng, and C. Zhao, “In situ measuring stem diameters of maize crops with a high-throughput phenotyping robot,” Remote Sensing, vol. 14, no. 4, p. 1030, 2022. View at: Publisher Site | Google Scholar
  78. L. Fu, F. Gao, J. Wu, R. Li, M. Karkee, and Q. Zhang, “Application of consumer RGB-D cameras for fruit detection and localization in field: A critical review,” Computers and Electronics in Agriculture, vol. 177, article 105687, 2020. View at: Publisher Site | Google Scholar
  79. A. Vit and G. Shani, “Comparing rgb-d sensors for close range outdoor agricultural phenotyping,” Sensors, vol. 18, no. 12, p. 4413, 2018. View at: Publisher Site | Google Scholar
  80. J. Fernández-Novales, V. Saiz-Rubio, I. Barrio et al., “Monitoring and mapping vineyard water status using non-invasive technologies by a ground robot,” Remote Sensing, vol. 13, no. 14, p. 2830, 2021. View at: Publisher Site | Google Scholar
  81. A. Wendel, J. Underwood, and K. Walsh, “Maturity estimation of mangoes using hyperspectral imaging from a ground based mobile platform,” Computers and Electronics in Agriculture, vol. 155, pp. 298–313, 2018. View at: Publisher Site | Google Scholar
  82. B. Benet, C. Dubos, F. Maupas, G. Malatesta, and R. Lenain, “Development of autonomous robotic platforms for sugar beet crop phenotyping using artificial vision,” in AGENG Conference 2018, Wageningen, Netherlands, 2018. View at: Google Scholar
  83. J. A. Gibbs, M. Pound, A. P. French, D. M. Wells, E. Murchie, and T. Pridmore, “Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction,” Plant Physiology, vol. 178, no. 2, pp. 524–534, 2018. View at: Publisher Site | Google Scholar
  84. G. Quaglia, C. Visconte, L. S. Scimmi, M. Melchiorre, P. Cavallone, and S. Pastorelli, “Design of a ugv powered by solar energy for precision agriculture,” Robotics, vol. 9, no. 1, p. 13, 2020. View at: Publisher Site | Google Scholar
  85. Y. Bao, L. Tang, and D. Shah, “Robotic 3D plant perception and leaf probing with collision free motion planning for automated indoor plant phenotyping,” in 2017 ASABE annual international meeting, p. 1, American Society of Agricultural and Biological Engineers, Spokane, WA, USA, 2017. View at: Google Scholar
  86. T. Han and C. Li, “Developing a high precision cotton boll counting system using active sensing,” in 2019 ASABE annual international meeting, p. 1, American Society of Agricultural and Biological Engineers, Boston, MA, USA, 2019. View at: Google Scholar
  87. L. Meier, P. Tanskanen, F. Fraundorfer, and M. Pollefeys, “Pixhawk: a system for autonomous flight using onboard computer vision,” in 2011 IEEE International Conference on Robotics and Automation, pp. 2992–2997, Shanghai, China, 2011. View at: Publisher Site | Google Scholar
  88. M. Quigley, K. Conley, B. Gerkey et al., “Ros: an open-source robot operating system,” in ICRA workshop on open source software, vol. 3, p. 5, Kobe, Japan, 2009. View at: Google Scholar
  89. D. Thomas, W. Woodall, and E. Fernandez, “Next-generation ros: building on dds,” in Open Robotics, ROSCon Chicago 2014, Mountain View, CA, 2014. View at: Publisher Site | Google Scholar
  90. K. Jensen, M. Larsen, S. H. Nielsen, L. B. Larsen, K. S. Olsen, and R. N. Jørgensen, “Towards an open software platform for field robots in precision agriculture,” Robotics, vol. 3, no. 2, pp. 207–234, 2014. View at: Publisher Site | Google Scholar
  91. X. Gao, J. Li, L. Fan et al., “Review of wheeled mobile robots’ navigation problems and application prospects in agriculture,” IEEE Access, vol. 6, pp. 49248–49268, 2018. View at: Publisher Site | Google Scholar
  92. A. Bechar and C. Vigneault, “Agricultural robots for field operations. Part 2: operations and systems,” Biosystems Engineering, vol. 153, pp. 110–128, 2017. View at: Publisher Site | Google Scholar
  93. R. C. Coulter, Implementation of the Pure Pursuit Path Tracking Algorithm, Carnegie-Mellon UNIV Pittsburgh PA Robotics INST, 1992.
  94. W. Zhang, J. Gai, Z. Zhang, L. Tang, Q. Liao, and Y. Ding, “Double-dqn based path smoothing and tracking control method for robotic vehicle navigation,” Computers and Electronics in Agriculture, vol. 166, article 104985, 2019. View at: Publisher Site | Google Scholar
  95. D. Ball, B. Upcroft, G. Wyeth et al., “Vision-based obstacle detection and navigation for an agricultural robot,” Journal of Field Robotics, vol. 33, no. 8, pp. 1107–1130, 2016. View at: Publisher Site | Google Scholar
  96. Z. Zhai, Z. Zhu, Y. Du, Z. Song, and E. Mao, “Multi-crop-row detection algorithm based on binocular vision,” Biosystems Engineering, vol. 150, pp. 89–103, 2016. View at: Publisher Site | Google Scholar
  97. M. Bakken, R. J. Moore, and P. From, End-to-end learning for autonomous crop row-following , vol. 52, no. 30, IFAC-Papers OnLine, 2019. View at: Publisher Site
  98. F. B. Malavazi, R. Guyonneau, J.-B. Fasquel, S. Lagrange, and F. Mercier, “LiDAR-only based navigation algorithm for an autonomous agricultural robot,” Computers and Electronics in Agriculture, vol. 154, pp. 71–79, 2018. View at: Publisher Site | Google Scholar
  99. S. A. Hiremath, G. W. Van Der Heijden, F. K. Van Evert, A. Stein, and C. J. Ter Braak, “Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter,” Computers and Electronics in Agriculture, vol. 100, pp. 41–50, 2014. View at: Publisher Site | Google Scholar
  100. P. M. Blok, K. Boheemen, F. K. van Evert, J. IJsselmuiden, and G. H. Kim, “Robot navigation in orchards with localization based on particle filter and Kalman filter,” Computers and Electronics in Agriculture, vol. 157, pp. 261–269, 2019. View at: Publisher Site | Google Scholar
  101. J. M. Mendes, F. N. dos Santos, N. A. Ferraz, P. M. do Couto, and R. M. dos Santos, “Localization based on natural features detector for steep slope vineyards,” Journal of Intelligent & Robotic Systems, vol. 93, no. 3-4, pp. 433–446, 2019. View at: Publisher Site | Google Scholar
  102. L. Grimstad and P. J. From, “Software components of the Thorvald II modular robot,” Modeling, Identification and Control, vol. 39, no. 3, pp. 157–165, 2018. View at: Publisher Site | Google Scholar
  103. M. Sharifi, M. S. Young, X. Chen, D. Clucas, and C. Pretty, “Mechatronic design and development of a non-holonomic omnidirectional mobile robot for automation of primary production,” Cogent Engineering, vol. 3, no. 1, 2016. View at: Publisher Site | Google Scholar
  104. N. Habibie, A. M. Nugraha, A. Z. Anshori, M. A. Ma'sum, and W. Jatmiko, “Fruit mapping mobile robot on simulated agricultural area in gazebo simulator using simultaneous localization and mapping (slam),” in 2017 International Symposium on Micro-Nano Mechatronics and Human Science (MHS), pp. 1–7, Nagoya, Japan, 2017. View at: Google Scholar
  105. N. Koenig and A. Howard, “Design and use paradigms for gazebo, an open-source multi-robot simulator,” in 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No. 04CH37566), vol. 3, pp. 2149–2154, Sendai, Japan, 2004. View at: Publisher Site | Google Scholar
  106. Webots, “Open-source mobile robot simulation software,” View at: Google Scholar
  107. E. Rohmer, S. P. Singh, and M. Freese, “V-rep: a versatile and scalable robot simulation framework,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1321–1326, Tokyo, Japan, 2013. View at: Publisher Site | Google Scholar
  108. R. Shamshiri, I. A. Hameed, L. Pitonakova et al., “Simulation software and virtual environments for acceleration of agricultural robotics: features highlights and performance comparison,” International Journal of Agricultural and Biological Engineering, vol. 11, no. 4, pp. 15–31, 2018. View at: Google Scholar
  109. P. Nebot, J. Torres-Sospedra, and R. J. Martínez, “A new hla-based distributed control architecture for agricultural teams of robots in hybrid applications with real and simulated devices or environments,” Sensors, vol. 11, no. 4, pp. 4385–4400, 2011. View at: Publisher Site | Google Scholar
  110. N. Tsolakis, D. Bechtsis, and D. Bochtis, “AgROS: a robot operating system based emulation tool for agricultural robotics,” Agronomy, vol. 9, no. 7, p. 403, 2019. View at: Publisher Site | Google Scholar
  111. J. Massah, K. A. Vakilian, M. Shabanian, and S. M. Shariatmadari, “Design, development, and performance evaluation of a robot for yield estimation of kiwifruit,” Computers and Electronics in Agriculture, vol. 185, article 106132, 2021. View at: Publisher Site | Google Scholar
  112. Y. Jiang and C. Li, “Convolutional neural networks for image-based high-throughput plant phenotyping: a review,” Plant Phenomics, vol. 2020, article 4152816, pp. 1–22, 2020. View at: Publisher Site | Google Scholar
  113. J. Weyler, A. Milioto, T. Falck, J. Behley, and C. Stachniss, “Joint plant instance detection and leaf count estimation for in-field plant phenotyping,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 3599–3606, 2021. View at: Publisher Site | Google Scholar
  114. E. Kayacan, Z.-Z. Zhang, and G. Chowdhary, “Embedded high precision control and corn stand counting algorithms for an ultra-compact 3d printed field robot,” Robotics: Science and Systems, vol. 14, p. 9, 2018. View at: Google Scholar
  115. U. Weiss and P. Biber, “Plant detection and mapping for agricultural robots using a 3D LIDAR sensor,” Robotics and Autonomous Systems, vol. 59, no. 5, pp. 265–273, 2011. View at: Publisher Site | Google Scholar
  116. S. Haug, A. Michaels, P. Biber, and J. Ostermann, “Plant classification system for crop/weed discrimination without segmentation,” in IEEE winter conference on applications of computer vision, pp. 1142–1149, Steamboat Springs, CO, USA, 2014. View at: Publisher Site | Google Scholar
  117. D. Reynolds, F. Baret, C. Welcker et al., “What is cost-efficient phenotyping? Optimizing costs for different scenarios,” Plant Science, vol. 282, pp. 14–22, 2019. View at: Publisher Site | Google Scholar
  118. M. Imperoli, C. Potena, D. Nardi, G. Grisetti, and A. Pretto, “An effective multi-cue positioning system for agricultural robotics,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3685–3692, 2018. View at: Publisher Site | Google Scholar
  119. J. Shockley, C. Dillon, J. Lowenberg-DeBoer, and T. Mark, “How will regulation influence commercial viability of autonomous equipment in US production agriculture?” Applied Economic Perspectives and Policy, 2021. View at: Publisher Site | Google Scholar
  120. J. Lowenberg-DeBoer, K. Behrendt, M. Canavari et al., “The impact of regulation on autonomous crop equipment in Europe,” in Precision agriculture’21, pp. 851–857, Wageningen Academic Publishers, 2021. View at: Google Scholar
  121. T. Fukatsu, G. Endo, and K. Kobayashi, “Field experiments with a mobile robotic field server for smart agriculture,” in Proceedings of the WCCA-AFITA2016, no. OS6-2, pp. 1–4, Suncheon, Jeollanam-do, South Korea, 2016. View at: Google Scholar
  122. “Corteva among first to leverage agile mobile robots to walk row crops,” 2021, View at: Google Scholar
  123. B. Katz, J. Di Carlo, and S. Kim, “Mini cheetah: a platform for pushing the limits of dynamic quadruped control,” in 2019 International conference on robotics and automation (ICRA), pp. 6295–6301, Montreal, QC, Canada, 2019. View at: Publisher Site | Google Scholar
  124. M. Davoodi, J. Mohammadpour Velni, and C. Li, “Coverage control with multiple ground robots for precision agriculture,” Mechanical Engineering, vol. 140, no. 6, pp. S4–S8, 2018. View at: Publisher Site | Google Scholar
  125. S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, “A survey of deep learning techniques for autonomous driving,” Journal of Field Robotics, vol. 37, no. 3, pp. 362–386, 2020. View at: Publisher Site | Google Scholar

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