Harnessing Deep Learning for Precision Cotton Disease Detection: A Comprehensive Review

Abstract

Cotton cultivation plays a critical role in global agriculture, yet its productivity is significantly hindered by various plant diseases that impact yield and quality. Conventional disease detection methods often fall short due to their reliance on manual inspection and limited accuracy. This comprehensive review explores the application of deep learning techniques beyond Convolutional Neural Networks (CNNs) in enhancing cotton disease detection. The paper covers a range of deep learning methodologies, including CNNs, Recurrent Neural Networks (RNNs), and hybrid models that combine different neural network architectures. It examines how these techniques can improve the precision and efficiency of disease diagnosis for common cotton ailments such as boll rot, leaf spot, cotton wilt, and bacterial blight. By reviewing current research and case studies, the paper provides insights into the effectiveness of various deep learning approaches and their integration into practical agricultural systems. It also addresses the challenges faced in implementing these technologies and suggests future directions for advancing disease management strategies through deep learning. This review aims to offer a holistic perspective on the potential of deep learning to transform cotton disease detection and contribute to more sustainable agricultural practices.