Abstract
With every forward step that humanity takes, it is our responsibility to contribute to the development of agricultural ecosystem. Precision agriculture effectually leverages science and technology for practicing agronomic principles towards the goal of sustainable agriculture. Deep learning supports the identification of plant disease and enables early and timely disease diagnosis for effective disease detection and management. To investigate the application of Compact Convolutional Transformer (CCT) for classification of mango leaf diseases is the prime objective of the work undertaken in this paper. Two diseases - anthracnose and powdery mildew, that most commonly affect the mango leaves in Andhra Pradesh region, India were considered for our research. An CCT-based prediction model is proposed to use for developing the automated system for detection of anthracnose and powdery mildew diseases from mango leaf images. Two CCT models CCT-7/8 × 2 and CCT-7/4 × 2 were experimented with. The proposed methodology has been evaluated against the VGG16. The CCT-7/8 × 2 model demonstrated an increased performance in terms of - accuracy of 3%–5%, F1-score of 3%–4%, precision of 2%–3% over the VGG16 model. The experimental results demonstrated that the proposed CCT-based model performed automated detection of anthracnose and powdery mildew diseases effectually and eases to treat the affected area of plant properly and it aids in increasing mango output and supplying the world market.
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Shereesha, M., Hemavathy, C., Teja, H., Reddy, G.M., Kumar, B.V., Sunitha, G. (2023). Precision Mango Farming: Using Compact Convolutional Transformer for Disease Detection. In: Abraham, A., Bajaj, A., Gandhi, N., Madureira, A.M., Kahraman, C. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2022. Lecture Notes in Networks and Systems, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-031-27499-2_43
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