Plant-disease detection by using computer vision approach - Chang, Man Kien (2023)

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Plant maladies have long been a major concern in agriculture, frequently resulting in substantial yield losses, economic losses, and degraded crop quality. As the global demand for food security and sustainable agricultural practices increases, there is a pressing need for effective and precise disease detection mechanisms. Computer vision and deep learning provide promising avenues for the rapid and accurate identification of plant diseases. This study explores the feasibility of utilising pre-trained deep learning models, such as ResNet18, VGG16, AlexNet, and GoogleNet, to detect and classify a wide variety of plant diseases. Using a comprehensive dataset containing images of foliage exhibiting various disease symptoms, these models were trained, refined, and evaluated with extreme care. According to preliminary findings, GoogleNet outperforms its competitors in terms of accuracy and computational efficiency. While apple leaves serve as the study's primary case study, the methodologies and findings have broader implications. It paves the way for the development of real-time disease detection systems on the field, which could revolutionise the agricultural industry. Such systems could endow farmers around the world with the means to make informed decisions, optimize crop health, and ultimately increase food production.