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A nightshade crop leaf disease detection using enhance-nightshade-CNN for ground truth data

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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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Data availability

Plant Village repository was used for the laboratory dataset and for the real dataset we have used the google agriculture images.

Code availability

Proposed CNN models has been implemented in python with the OpenCV environment and executed on Google colab environment.

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  1. Computer Engineering Department, Sardar Vallabhbhai Patel Institute of Technology, Vasad, India

    Barkha M. Joshi

  2. Computer Science and Engineering Department, The Maharaja Sayajirao University of Baroda, Vadodara, India

    Hetal Bhavsar

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Dataset images we have used from the plant village repository.

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Joshi, B.M., Bhavsar, H. A nightshade crop leaf disease detection using enhance-nightshade-CNN for ground truth data. Vis Comput 40, 5639–5658 (2024). https://doi.org/10.1007/s00371-023-03127-y

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