Chili-Net: An Approach for Classifying Chili Leaf Diseases Using Deep Neural Networks
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-66594-3_5
Reference9 articles.
1. Araujo, S.D.C.S., Malemath, V.S., Meenakshi Sundaram, K.: Symptom-based identification of G-4 chili leaf diseases based on rotation invariant. Front. Robot. AI 8, 650134 (2021). https://doi.org/10.3389/frobt.2021.650134
2. Pujari, J.D., Yakkundimath, R., Byadgi, A.S.: Image processing based detection of fungal diseases in plants. Procedia Comput. Sci. 46, 1802–1808 (2015). https://doi.org/10.1016/j.procs.2015.02.137
3. Sambrani, Y., Bhairannawar, S.: Chili disease: detection and classification using various machine learning techniques. In: 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC), pp. 1–6. IEEE (2023)
4. Sari, Y., Baskara, A.R., Wahyuni, R.: Classification of chili leaf disease using the gray level co-occurrence matrix (GLCM) and the support vector machine (SVM) methods. In: 2021 Sixth International Conference on Informatics and Computing (ICIC), pp. 1–4. IEEE (2021)
5. Naik, B.N., Malmathanraj, R., Palanisamy, P.: Detection and classification of chilli leaf disease using a squeeze-and-excitation-based CNN model. Eco. Inform. 69, 101663 (2022)
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