A nightshade crop leaf disease detection using enhance-nightshade-CNN for ground truth data
Abstract
In the fast-growing agricultural world, early detection of plant diseases is crucial for maintaining crop health and ensuring successful harvests. Advancements in computer vision technology have led to the development of advanced methods for diagnosing plant diseases. However, factors like lighting, weather, and the number of diseases in a single image can make it difficult to detect plant diseases. Traditional deep learning-based algorithms have drawbacks, such as high hardware investment, inference speed, and generalization. This research article aims to raise awareness among farmers about cutting-edge technology for detecting plant leaf disease in nightshade crops. The Enhance-Nightshade-CNN model was used to enhance the quality of nightshade crop leaf disease samples, achieving good accuracy compared to existing algorithms. The model accurately identified healthy and unhealthy leaves in the real environment, with ground truth results showing a 95–100% accuracy rate.
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Publication History
Published: 27 October 2023
Accepted: 27 September 2023
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