The Application of UAV-Based Hyperspectral Imaging to Estimate Crop Traits in Maize Inbred LinesRead the full article
The open access journal Plant Phenomics, published in association with NAU, publishes novel research that advances plant phenotyping and connects phenomics with other research domains.
Plant Phenomics' editorial board is led by Seishi Ninomiya (University of Tokyo), Frédéric Baret (French National Institute of Agricultural Research), and Zong-Ming Cheng (Nanjing Agricultural University/University of Tennessee) and is comprised of leading experts in the field.
Latest ArticlesMore articles
An Integrated Method for Tracking and Monitoring Stomata Dynamics from Microscope Videos
Patchy stomata are a common and characteristic phenomenon in plants. Understanding and studying the regulation mechanism of patchy stomata are of great significance to further supplement and improve the stomatal theory. Currently, the common methods for stomatal behavior observation are based on static images, which makes it difficult to reflect dynamic changes of stomata. With the rapid development of portable microscopes and computer vision algorithms, it brings new chances for stomatal movement observation. In this study, a stomatal behavior observation system (SBOS) was proposed for real-time observation and automatic analysis of each single stoma in wheat leaf using object tracking and semantic segmentation methods. The SBOS includes two modules: the real-time observation module and the automatic analysis module. The real-time observation module can shoot videos of stomatal dynamic changes. In the automatic analysis module, object tracking locates every single stoma accurately to obtain stomatal pictures arranged in time-series; semantic segmentation can precisely quantify the stomatal opening area (SOA), with a mean pixel accuracy (MPA) of 0.8305 and a mean intersection over union (MIoU) of 0.5590 in the testing set. Moreover, we designed a graphical user interface (GUI) so that researchers could use this automatic analysis module smoothly. To verify the performance of the SBOS, the dynamic changes of stomata were observed and analyzed under chilling. Finally, we analyzed the correlation between gas exchange and SOA under drought stress, and the correlation coefficients between mean SOA and net photosynthetic rate (Pn), intercellular CO2 concentration (Ci), stomatal conductance (Gs), and transpiration rate (Tr) are 0.93, 0.96, 0.96, and 0.97.
Robust Surface Reconstruction of Plant Leaves from 3D Point Clouds
The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method’s robustness against noise and missing points. To mitigate this trade-off, we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf (the shape and distortion of that shape) separately using leaf-specific properties. This separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing points. To evaluate the proposed method, we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species (soybean and sugar beet) and compared the results with those of conventional methods. The result showed that the proposed method robustly reconstructed the leaf surfaces, despite the noise and missing points for two different leaf shapes. To evaluate the stability of the leaf surface reconstructions, we also calculated the leaf surface areas for 14 consecutive days of the target leaves. The result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.
Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature
High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding. However, manual measurements within breeding programs are time consuming and have high cost and low throughput, which limit the application in large-scale field phenotyping. In this study, we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders. In total, 13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield. Using an Unmanned Aerial Vehicle (UAV) platform equipped with a hyperspectral camera to capture images over multiple time series, a rice yield classification model based on the XGBoost algorithm was proposed. Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not. The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice. Thus, we developed a low-cost, high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency.
Detection of the Progression of Anthesis in Field-Grown Maize Tassels: A Case Study
The tassel of the maize plant is responsible for the production and dispersal of pollen for subsequent capture by the silk (stigma) and fertilization of the ovules. Both the amount and timing of pollen shed are physiological traits that impact the production of a hybrid seed. This study describes an automated end-to-end pipeline that combines deep learning and image processing approaches to extract tassel flowering patterns from time-lapse camera images of plants grown under field conditions. Inbred lines from the SAM and NAM diversity panels were grown at the Curtiss farm at Iowa State University, Ames, IA, during the summer of 2016. Using a set of around 500 pole-mounted cameras installed in the field, images of plants were captured every 10 minutes of daylight hours over a three-week period. Extracting data from imaging performed under field conditions is challenging due to variabilities in weather, illumination, and the morphological diversity of tassels. To address these issues, deep learning algorithms were used for tassel detection, classification, and segmentation. Image processing approaches were then used to crop the main spike of the tassel to track reproductive development. The results demonstrated that deep learning with well-labeled data is a powerful tool for detecting, classifying, and segmenting tassels. Our sequential workflow exhibited the following metrics: mAP for tassel detection was 0.91, F1 score obtained for tassel classification was 0.93, and accuracy of semantic segmentation in creating a binary image from the RGB tassel images was 0.95. This workflow was used to determine spatiotemporal variations in the thickness of the main spike—which serves as a proxy for anthesis progression.
A Comparative Analysis of Quantitative Metrics of Root Architecture
High throughput phenotyping is important to bridge the gap between genotype and phenotype. The methods used to describe the phenotype therefore should be robust to measurement errors, relatively stable over time, and most importantly, provide a reliable estimate of elementary phenotypic components. In this study, we use functional-structural modeling to evaluate quantitative phenotypic metrics used to describe root architecture to determine how they fit these criteria. Our results show that phenes such as root number, root diameter, and lateral root branching density are stable, reliable measures and are not affected by imaging method or plane. Metrics aggregating multiple phenes such as total length, total volume, convex hull volume, and bushiness index estimate different subsets of the constituent phenes; they however do not provide any information regarding the underlying phene states. Estimates of phene aggregates are not unique representations of underlying constituent phenes: multiple phenotypes having phenes in different states could have similar aggregate metrics. Root growth angle is an important phene which is susceptible to measurement errors when 2D projection methods are used. Metrics that aggregate phenes which are complex functions of root growth angle and other phenes are also subject to measurement errors when 2D projection methods are used. These results support the hypothesis that estimates of phenes are more useful than metrics aggregating multiple phenes for phenotyping root architecture. We propose that these concepts are broadly applicable in phenotyping and phenomics.
Semiautomated 3D Root Segmentation and Evaluation Based on X-Ray CT Imagery
Background. Computed X-ray tomography (CTX) is a high-end nondestructive approach for the visual assessment of root architecture in soil. Nevertheless, in order to evaluate high-resolution CTX data of root architectures, manual segmentation of the depicted root systems from large-scale volume data is currently necessary, which is both time consuming and error prone. The duration of such a segmentation is of importance, especially for time-resolved growth analysis, where several instances of a plant need to be segmented and evaluated. Specifically, in our application, the contrast between soil and root data varies due to different growth stages and watering situations at the time of scanning. Additionally, the root system itself is expanding in length and in the diameter of individual roots. Objective. For semiautomated and robust root system segmentation from CTX data, we propose the RootForce approach, which is an extension of Frangi’s “multi-scale vesselness” method and integrates a 3D local variance. It allows a precise delineation of roots with diameters down to several μm in pots with varying diameters. Additionally, RootForce is not limited to the segmentation of small below-ground organs, but is also able to handle storage roots with a diameter larger than 40 voxels. Results. Using CTX volume data of full-grown bean plants as well as time-resolved () growth studies of cassava plants, RootForce produces similar (and much faster) results compared to manual segmentation of the regarded root architectures. Furthermore, RootForce enables the user to obtain traits not possible to be calculated before, such as total root volume (), total root length (), root volume over depth, root growth angles (, , and ), root surrounding soil density , or form fraction . Discussion. The proposed RootForce tool can provide a higher efficiency for the semiautomatic high-throughput assessment of the root architectures of different types of plants from large-scale CTX. Furthermore, for all datasets within a growth experiment, only a single set of parameters is needed. Thus, the proposed tool can be used for a wide range of growth experiments in the field of plant phenotyping.