A Comprehensive Review on Multi-Class Recognition of Soybean Leaf Diseases

This paper presents a comprehensive review of the current state-of-the-art methodologies in the multi-class recognition of soybean leaf diseases, addressing the challenges faced by soybean cultivation globally. Focusing on diseases like rust, bacterial blight, anthracnose, and powdery mildew, the review encompasses traditional image processing techniques as well as modern advancements in deep learning, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Topics covered include dataset compilation, preprocessing, feature extraction, and the application of various machine learning algorithms. Special emphasis is placed on exploring the potential of transfer learning, domain adaptation, and the integration of spectral imaging and remote sensing technologies for enhanced disease detection. By providing a thorough comparative analysis, this review aims to guide future research efforts, aiding researchers, agronomists, and practitioners in developing robust and scalable solutions to combat soybean leaf diseases and improve global agricultural productivity.