Cutting-edge computational approaches to plant phenotyping.

Abstract

Precision agriculture methods can achieve the highest yield by applying the optimum amount of water, selecting appropriate pesticides, and managing crops in a way that minimises environmental impact. A rapidly emerging advanced research area, computer vision and deep learning, plays a significant role in effective crop management, such as superior genotype selection, plant classification, weed and pest detection, root localization, fruit counting and ripeness detection, and yield prediction. Also, phenotyping of plants involves analysing characteristics of plants such as chlorophyll content, leaf size, growth rate, leaf surface temperature, photosynthesis efficiency, leaf count, emergence time, shoot biomass, and germination time. This article presents an exhaustive study of recent techniques in computer vision and deep learning in plant science, with examples. The study provides the frequently used imaging parameters for plant image analysis with formulae, the most popular deep neural networks for plant classification and detection, object counting, and various applications. Furthermore, we discuss the publicly available plant image datasets for disease detection, weed control, and fruit detection with the evaluation metrics, tools and frameworks, future advancements and challenges in machine learning and deep learning models.