Abstract
Grapevine (Vitis vinifera L.) is a major fruit crop with commercial importance worldwide. Black rot, Black measles, and Leaf blight are three diseases commonly found in the grapevine. The timely and accurate diagnosis is crucial in preventing the spread of the disease and reducing loss in production. The advancement in deep learning has opened doors for new diagnostic algorithms in the domain of plant disease identification. In this paper, we propose a grapevine disease identification method using a convolutional neural network (CNN). A light weight 6-layer CNN model was designed from scratch and trained using an open repository with 3 disease classes and 1 healthy leaf image dataset. The dataset contained a total of 3423 grapevine leaf images. The model was trained with a 70–30 train-test ratio. Image augmentation and early stopping techniques were used to avoid overfitting of the model. The proposed model achieved 98.4% classification accuracy on the test dataset. Additionally, the key feature of the proposed 6-layer model is that it has lesser number of trainable parameters which reduces its computational complexity as compared to the existing pre-trained models.
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We express our thanks to Department of Electronics and Communication Engineering, Birla Institute of Technology Mesra, INDIA for providing institute fellowship to pursue doctoral research.
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Reva Nagi and Sanjaya Shankar Tripathy have contributed significantly to the implementation of the research, to the analysis of the results and agree with the manuscript.
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Nagi, R., Tripathy, S.S. Deep convolutional neural network based disease identification in grapevine leaf images. Multimed Tools Appl 81, 24995–25006 (2022). https://doi.org/10.1007/s11042-022-12662-0
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DOI: https://doi.org/10.1007/s11042-022-12662-0