Plant diseases and pests cause significant losses to farmers and threaten food security worldwide. Monitoring the growing conditions of crops and detecting plant diseases is critical for sustainable agriculture. Traditionally, crop inspection has been carried out by people with expert knowledge in the field. However, regarding any activity carried out by humans, this activity is prone to errors, leading to possible incorrect decisions. Innovation is, therefore, an essential fact of modern agriculture. In this context, deep learning has played a key role in solving complicated applications with increasing accuracy over time, and recent interest in this type of technology has prompted its potential application to address complex problems in agriculture, such as plant disease and pest recognition. Although substantial progress has been made in the area, several challenges still remain, especially those that limit systems to operate in real-world scenarios.This research topic aims to explore recent advanced AI methods for plant disease and pest recognition for real-world applications. We welcome submissions of all article types published in Frontiers in Plant Science. Studies of interest cover the following but are not limited to:• High-quality datasets.• Multi-modal sensor, including RGB, video, multi-spectral, and depth images, and the internet of things (IoT).• Learning with multi-crop and multi-dataset.• Foundation models and their applications.• Small deep-learning models with limited resources.• Data-centric methods, such as learning from noisy and unlabeled data, data augmentation, open set recognition, and domain generalization.• Applications, including classification, detection, and segmentation.• Early plant disease and pest recognition.• Quantitative evaluation of the plants infected by diseases and pests.• Incidence analysis.• Information security for plant disease and pest recognition.• Cloud and edge computing.• Real-world application and robotic system development.• Cell division of disease-infected plants.
Plant diseases and pests cause significant losses to farmers and threaten food security worldwide. Monitoring the growing conditions of crops and detecting plant diseases is critical for sustainable agriculture. Traditionally, crop inspection has been carried out by people with expert knowledge in the field. However, regarding any activity carried out by humans, this activity is prone to errors, leading to possible incorrect decisions. Innovation is, therefore, an essential fact of modern agriculture. In this context, deep learning has played a key role in solving complicated applications with increasing accuracy over time, and recent interest in this type of technology has prompted its potential application to address complex problems in agriculture, such as plant disease and pest recognition. Although substantial progress has been made in the area, several challenges still remain, especially those that limit systems to operate in real-world scenarios.This research topic aims to explore recent advanced AI methods for plant disease and pest recognition for real-world applications. We welcome submissions of all article types published in Frontiers in Plant Science. Studies of interest cover the following but are not limited to:• High-quality datasets.• Multi-modal sensor, including RGB, video, multi-spectral, and depth images, and the internet of things (IoT).• Learning with multi-crop and multi-dataset.• Foundation models and their applications.• Small deep-learning models with limited resources.• Data-centric methods, such as learning from noisy and unlabeled data, data augmentation, open set recognition, and domain generalization.• Applications, including classification, detection, and segmentation.• Early plant disease and pest recognition.• Quantitative evaluation of the plants infected by diseases and pests.• Incidence analysis.• Information security for plant disease and pest recognition.• Cloud and edge computing.• Real-world application and robotic system development.• Cell division of disease-infected plants.