Image Analysis and Machine Learning for Cyber-Agricultural Systems
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.
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
Plant Phenomics, vol. 2020, Article ID 4216373, 11 pages, 2020.
Chenyong Miao, Alejandro Pages, Zheng Xu, Eric Rodene, Jinliang Yang, and James C. Schnable
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