Faculty Publications
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Item Cardamom Plant Disease Detection Approach Using EfficientNetV2(Institute of Electrical and Electronics Engineers Inc., 2022) Sunil, C.K.; Jaidhar, C.D.; Patil, N.Cardamom is a queen of spices. It is indigenously grown in the evergreen forests of Karnataka, Kerala, Tamil Nadu, and the northeastern states of India. India is the third largest producer of cardamom. Plant diseases cause a catastrophic influence on food production safety; they reduce the eminence and quantum of agricultural products. Plant diseases may cause significantly high loss or no harvest in dreadful cases. Various diseases and pests affect the growth of cardamom plants at different stages and crop yields. This study concentrated on two diseases of cardamom plants, Colletotrichum Blight and Phyllosticta Leaf Spot of cardamom and three diseases of grape, Black Rot, ESCA, and Isariopsis Leaf Spot. Various methods have been proposed for plant disease detection, and deep learning has become the preferred method because of its spectacular accomplishment. In this study, U2-Net was used to remove the unwanted background of an input image by selecting multiscale features. This work proposes a cardamom plant disease detection approach using the EfficientNetV2 model. A comprehensive set of experiments was carried out to ascertain the performance of the proposed approach and compare it with other models such as EfficientNet and Convolutional Neural Network (CNN). The experimental results showed that the proposed approach achieved a detection accuracy of 98.26%. © 2013 IEEE.Item Tomato plant disease classification using Multilevel Feature Fusion with adaptive channel spatial and pixel attention mechanism(Elsevier Ltd, 2023) Sunil, C.K.; Jaidhar, C.D.; Patil, N.Agriculture's productivity has decreased in the last decade due to climate change and inappropriate usage of water, fertilizer, and pesticides, which stimulate plant diseases. Plant pathogens are the prime threat to agriculture; diseases causes the development of plant and affects the quality and yield of the crop. To enhance crop yield and quality, early perceive the pathogens and insinuation of the proper medications are essential. Deep learning approaches produce promising results for classifying the input images, and the results vary for many reasons, such as data imbalance and fewer or identical features among other classes of the dataset. In this work, tomato plant disease classification is proposed by using Multilevel Feature Fusion Network (MFFN). It employs ResNet50, MFFN, and Adaptive Attention Mechanism, which combines channel, spatial, and pixel attention to classify the tomato plant leaf images. The proposed deep learning-based approach is trained and tested on a tomato plant leaves dataset and achieved 99.88% training accuracy, 99.88% validation accuracy, and 99.83% external testing accuracy. It outperformed the existing approaches relevant to the tomato plant dataset. Further, this work also proposes a pesticide prescription module that provides pesticide information based on the type of leaf disease. © 2023 Elsevier LtdItem DFN-PSAN: Multi-level deep information feature fusion extraction network for interpretable plant disease classification(Elsevier B.V., 2024) Dai, G.; Tian, Z.; Fan, J.; Sunil, C.K.; Dewi, C.Accurate identification of crop diseases is an effective way to promote the development of intelligent and modernized agricultural production, as well as to reduce the use of pesticides and improve crop yield and quality. Deep learning methods have achieved better performance in classifying input plant disease images. However, many plant disease datasets are often constructed from controlled scenarios, and these deep learning models may not perform well when tested in real-world agricultural environments, highlighting the challenges of transitioning to natural farm environments under the new demand paradigm of Agri 4.0. Based on the above reasons, this work proposes using a multi-level deep information feature fusion extraction network (DFN-PSAN) to achieve plant disease classification in natural field environments. DFN-PSAN adopts the YOLOv5 Backbone and Neck network as the base structure DFN and uses pyramidal squeezed attention (PSA) combined with multiple convolutional layers to design a novel classification network PSAN, which fuses and processes the multi-level depth information features output from DFN and highlights the critical regions of plant disease images with the help of pixel-level attention provided by PSA, thus realizing effective classification of multiple fine-grained plant diseases. The proposed DFN-PSAN was trained and tested on three plant disease datasets. The average accuracy and F1-score exceeded 95.27%. The PSA attention mechanism saved 26% of model parameters, achieving a competitive performance among existing related methods. In addition, this work effectively enhances the transparency of the features of the model attention to plant diseases through t-SNE with SHAP interpretable methods. © 2023 Elsevier B.V.