This Research Topic is part of the article collection series - Machine Vision and Machine Learning for Plant Phenotyping and Precision Agriculture.

Plant phenotyping (PP) describes the physiological and biochemical properties of plants affected by both genotypes and environments. It is an emerging research field that is assisting the breeding and cultivation of new crop varieties to be more productive and resilient to challenging environments. Precision agriculture (PA) uses sensing technologies to observe crops and then manage them optimally to ensure that they grow in healthy conditions, have maximum productivity, and have minimal negative effects on the environment. Traditionally, the observation of plant traits heavily relies on human experts which is labor intensive, time-consuming, and subjective.

Automatic crop traits measurement in PP and PA are two different fields, but they share the same sensing and data processing technologies in many respects. Recently, driven by computer and sensor technologies, machine vision (MV) and machine learning (ML) have contributed to accurate, high-throughput, and nondestructive plant phenotyping and precision agriculture. However, these technologies are still in their infant stage and there are many challenges and questions related to them that still need to be addressed. The goal of this Research Topic is to provide a platform to share the latest research results on the application of MV and ML for PP and PA. It aims to highlight cutting-edge technologies, bottle-necks, and future research directions for MV and ML in crop breeding, crop cultivation, disease management, weed control, and pest control.

The latest original research or review papers of MV and ML in either PP or PA are welcome. Manuscripts on phenotyping in controlled or field environments, shoot or root phenotyping, and PA technologies for weed and invertebrate pest detection are also suitable. In particular, this Research Topic will focus on the following areas:
· High-throughput PP in controlled environments;
· PP in field environment using ground-based vehicles, aircraft, and satellites;
· Root architecture phenotyping using x-ray or CT systems;
· Plant organ detection and counting;
· New color imaging, multispectral, and hyperspectral imaging processing methods in PP and PA;
· Novel ML algorithms for PP and PA;
· State-of-the-art MV sensor development;
· Disease and stress detection;
· Weed detection and classification.

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Frontiers in Plant Science

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