Plant phenotyping is the identification of effects on plant structure and function (the phenotype) resulting from genotypic differences (i.e., differences in the genetic code) and the environmental conditions a plant has been exposed to. Knowledge of plant phenotypes is a key ingredient of the knowledge-based bioeconomy, which not only literally helps to feed the world, but is also essential for feed, fibre, and fuel production. Computer vision approaches are required to extract plant phenotypes from images and automate the detection of plants and plant organs for automated weeding or harvest. The lack of robust automated image-based phenotyping methods is widely recognized as the major obstacle to ensuring global food security.
The goals of this Research Topic include demonstrating the state-of-the-art, identifying key unsolved problems, and introducing computer scientists with an interest in plant phenotyping to the field. Effective plant phenotyping is urgently needed to support the sustainability of our planet and its inhabitants: having strong community structures and new computer vision scientists enter this field is more crucial now than ever.
This Research Topic is associated with the 7th Computer Vision in Plant Phenotyping and Agriculture Workshop at the International Computer Vision Conference on Oct. 11, 2021. The workshop includes both full papers and extended abstracts. We expect that this Research Topic will be a high-priority venue for revised versions of papers from the presented extended abstracts, as well as expanded versions of workshop papers with 30% or more new content.
The topics include, but are not restricted to:
• Novel methods for image-based plant phenotyping
• Novel image analysis and machine learning methods tailored to plant and crop images
• Plant and plant organ detection and counting from images
• Plant and crop disease classification and ratings
• New plant and agricultural image datasets
• Results of on-going machine learning competitions, including Global Wheat Challenge, Sorghum Biomass Prediction, Arabidopsis Root Segmentation Challenge, Leaf Segmentation Challenge, Leaf Counting Challenge
Keywords: Computer Vision, Plant Phenotyping, Agriculture