Image Analysis and Machine Learning for Cyber-Agricultural Systems

Special Issue


Today, efficient and cost-effective sensors, as well as high-performance computing technologies, are looking to transform traditional plant-based agriculture into an efficient cyber-physical system. The easy availability of cheap, deployable, connected sensor technology has created an enormous opportunity to collect a vast amount of data at varying spatial and temporal scales at both experimental and production agriculture levels. Therefore, both offline and real-time agricultural analytics that assimilate such heterogeneous data and provide automated, actionable information is critically needed for sustainable and profitable agriculture. The application of advanced image processing and machine learning methods to this critical societal need can be viewed as a transformative extension for the agriculture community. These papers present image analysis and machine learning algorithms, experimental technologies, software, pipelines, and new results for Cyber-Agricultural applications.

    Guest Editors

    Wei Guo, University of Tokyo

    Soumik Sarkar, Iowa State University

      Table of Contents

      High-Throughput Rice Density Estimation from Transplantation to Tillering Stages Using Deep Networks

      Plant Phenomics, vol. 2020, Article ID 1375957, 14 pages, 2020.

      Liang Liu, Hao Lu, Yanan Li, and Zhiguo Cao

      TasselGAN: An Application of the Generative Adversarial Model for Creating Field-Based Maize Tassel Data

      Plant Phenomics, vol. 2020, Article ID 8309605, 15 pages, 2020.

      Snehal Shete, Srikant Srinivasan, and Timothy A. Gonsalves

      Semantic Segmentation of Sorghum Using Hyperspectral Data Identifies Genetic Associations

      Plant Phenomics, vol. 2020, Article ID 4216373, 11 pages, 2020. 

      Chenyong Miao, Alejandro Pages, Zheng Xu, Eric Rodene, Jinliang Yang, and James C. Schnable

      Easy MPE: Extraction of Quality Microplot Images for UAV-Based High-Throughput Field Phenotyping

      Plant Phenomics, vol. 2019, Article ID 2591849, 9 pages, 2019.

      Léa Tresch, Yue Mu, Atsushi Itoh, Akito Kaga, Kazunori Taguchi, Masayuki Hirafuji, Seishi Ninomiya, and Wei Guo