ADVANCEMENTS IN DEEP LEARNING TECHNIQUES FOR IMAGE-BASED DETECTION OF DISEASES IN LEAVES OF MAIZE: A REVIEW
Keywords:
Maize Leaf Disease Detection, Deep Learning, Convolution Neural Network (CNNs), Image Classification, Agricultural Automation.Abstract
This review article explores the critical role of deep learning in the automated detection and classification of maize leaf diseases, which significantly threaten global agricultural productivity. Traditional methods of disease identification typically depend on manual inspections, which can be time-consuming and susceptible to human error, resulting in inconsistent outcomes. In contrast, the proposed deep learning framework employs convolutional neural networks (CNNs) and transfer learning techniques that enhance diagnostic accuracy while reducing computational requirements. By utilizing a comprehensive dataset of labeled maize leaf images, the model effectively distinguishes between healthy and diseased leaves, targeting common afflictions such as maize rust, northern leaf blight, and gray leaf spot. The study emphasizes the model's adaptability to varying environmental conditions and its superior performance compared to conventional machine learning approaches. Furthermore, the article addresses the challenges encountered in real-world agricultural settings, including issues related to variable lighting and complex backgrounds that can obscure disease symptoms. It underscores the necessity for high-resolution, meticulously labeled images and advanced technology-driven solutions to enable rapid and precise disease detection. Such advancements are crucial for improving crop management and enhancing food security. Ultimately, this review aims to democratize access to effective diagnostic tools, empowering farmers and stakeholders in the agricultural sector with the resources needed to combat maize leaf diseases effectively. By fostering the adoption of these innovative technologies, the study contributes to the ongoing efforts to enhance agricultural resilience and productivity in the face of pressing global challenges.
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