Abstract
Leaf constitutes an important part of a plant as it gives prior information about its health. Timely and proper diagnosis of different pests, fungal and viral diseases of plants is indispensable for successful crop production. Sustainability in crop production is of utmost importance to meet the growing demand for food and support the economic growth of a country. In recent years, deep learning, a far superior method to traditional methods has paved the way in the field of digital image processing. Deep learning has the ability to process large numbers of features making it very powerful when dealing with unstructured data. It has achieved impressive results in the field of image classification. In this study, deep learning-based approach is adopted to identify and classify nine classes of tomato leaf diseases and mite infection along with healthy leaves and two types each of rice leaf diseases and pest infestation along with healthy leaves. For identification and classification of tomato leaf diseases, Convolutional Recurrent Neural Network architecture with Gated Recurrent Unit was used. To classify the rice leaf diseases and pest infestation, the concept of transfer learning using the same model trained with tomato leaf dataset was used due to insufficient and imbalance rice leaf datasets. The model achieved a success rate of 99.62% for the tomato leaf and 91.63% for the rice leaf.
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Kusal Roy, Dibyarup Pal, and Dipak Kumar Kole have contributed equally to this work.
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Mondal, D., Roy, K., Pal, D. et al. Deep Learning-Based Approach to Detect and Classify Signs of Crop Leaf Diseases and Pest Damage. SN COMPUT. SCI. 3, 433 (2022). https://doi.org/10.1007/s42979-022-01332-5
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DOI: https://doi.org/10.1007/s42979-022-01332-5