About this Research Topic

Background

Identifying and managing plant diseases is a critical aspect of agriculture, significantly impacting the economy and food security. The early detection and management of plant diseases are essential to maintain healthy crops, reduce plant damage, and ensure high yields and quality of agricultural products. The traditional approach involves visually observing symptoms and abnormal growth patterns, which is often limited by the subjectivity of human observation and labor costs. Nowadays, traditional manual inspection of plant diseases is being replaced by advanced techniques, such as sensing networks, machine vision, remote sensing, and robotics. Researchers and engineers have developed numerous plant disease identification techniques and automatic disease inspection systems over the last few decades.

The identification of agricultural plant diseases has undergone significant evolution over the years, transitioning from traditional methods to more advanced techniques, such as deep learning. Deep learning-based methods offer promising solutions for recognizing plant diseases even in complex conditions and detecting early symptoms. Recent studies have extensively explored deep learning-based disease identification approaches in various scenarios, including crop fields, greenhouses, and orchards. Convolutional neural networks and transformer-based models have demonstrated considerable potential in the detection and classification of plant diseases, yielding encouraging results in terms of identification accuracy and classification precision. Furthermore, these techniques have been successfully integrated into various platforms for plant disease inspection, including video surveillance, remote sensing, agricultural robots, and smartphone applications.

This topic discusses the latest advancements in plant disease identification based on deep learning, exploring the technical progress as well as the challenges faced in practical applications of plant disease identification. Additionally, it delves into the future trends in this field. By examining the past, present, and potential future of agricultural plant disease identification, this topic aims to provide.

We welcome submissions of different types of manuscripts including original research papers, reviews, and methods, including but not limited to:

▪ Deep learning algorithms for plant disease recognition
▪ Deep learning based plant phenotyping
▪ Spectral imaging and analysis for early disease detection
▪ Remote sensing and analysis for plant disease detection
▪ Image processing techniques in plant pathology
▪ Automated disease detection systems using drones and IoT devices
▪ Automated plant monitoring systems and sensing networks

Keywords: plant disease, disease identification, deep learning, crops, convolutional neural networks

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