Advanced Leaf Blight Detection in Maize Using YOLACT Deep Learning and Histogram Equalization

Leaf blight, a widespread plant disease caused by diverse pathogens (fungi, bacteria, viruses), significantly reduces agricultural productivity through leaf browning, wilting, and decay. Early detection is critical to prevent yield loss, but manual methods are labor-intensive and error-prone. This study proposes a novel deep learning approach for automated leaf blight detection and identification. We leverage the YOLACT model, demonstrating high precision in recognizing various plant diseases, including leaf blight. To address challenges like light variations, shadows, and overlapping leaves, we incorporate histogram equalization as a pre- processing step to enhance image contrast and detail. Thiscombined approach, utilizing YOLACT and histogram equalization on camera-captured leaf images, achieves a detection accuracy of ${9 8 . 9 4 \%}$. This innovative methodology offers an efficient solution for leaf blight detection, contributing to effective disease management and improved agricultural productivity.