Abstract
Deep learning has made crucial contributions to disease classification and detection tasks applied to agriculture in order to protect the yield and improve the quality. An early diagnosis of plant diseases is essential for management and decision-making to safeguard agricultural production. In this paper, we present the training and evaluation of a lightweight Convolutional Neural Network model for the classification of tomato diseases using their leaves. We used a subset of the Plantvillage dataset made up of 18,160 RGB photos divided into ten classes. This model attained accuracy of 96.37%. The result obtained through this model proves its efficacy on the classification of disease affecting tomato leaves over several other existing methodologies.
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Lmoussaoui, M., Ait Laasri, E.H., Atmani, A., Agliz, D. (2023). A Lightweight CNN Model for Tomato Crop Disease Detection. In: Kacprzyk, J., Ezziyyani, M., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development. AI2SD 2022. Lecture Notes in Networks and Systems, vol 713. Springer, Cham. https://doi.org/10.1007/978-3-031-35248-5_21
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DOI: https://doi.org/10.1007/978-3-031-35248-5_21
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