Detection and Classification of Corn Plant Diseases Using Computer Vision

The goal of this paper is to create a sophisticated computer vision model that is especially intended for the automatic identification and categorization of diseases affecting corn plants. The study uses cutting-edge deep learning architectures like ResNet50, VGG16, MobileNet, and Xception to improve disease identification in maize crops in terms of both efficiency and accuracy. The study includes a thorough analysis of existing approaches, with an emphasis on modifying these strategies to address the unique difficulties presented by maize pathology. Advanced image processing techniques will be used to carefully curate and compile a high-quality image dataset of maize plants exhibiting various illnesses. By incorporating the chosen model into an intuitive platform, the goal is to close the knowledge gap between cutting edge AI technology and useful agricultural solutions. This program aims to improve crop management methods by providing a dependable and effective way to detect diseases, which will ultimately lead to higher agricultural output and sustainability. The paper aims to establish a new benchmark in the use of deep learning for agricultural disease management through ongoing improvement and validation.