Browsing by Author "Sunil, C.K."

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An Efficient Infectious Disease Detection in Plants Using Deep Learning
(Springer Science and Business Media Deutschland GmbH, 2024) Sunil, C.K.; Jaidhar, C.D.
Over the past decade, agriculture has suffered reduced productivity from climate change and improper water, fertilizer, and pesticide use, fueling plant diseases. Pathogens pose the main threat, impacting crop yield and quality. Early detection and targeted treatments are crucial to improve both yield and quality. To address this, we have carried out deep learning-based approaches and published ours works in conferences and journSal. Those works are briefly discussed in the paper as follows: (i) Empirical work on different plant datasets is conducted to analyze the hyperparameters of the neural network. (ii) The research minimizes misclassifications by leveraging an ensemble-based strategy with AlexNet, ResNet, and VGGNet across seven plant leaf image datasets. The complexity of plant disease diagnosis in diverse conditions is tackled through a hybrid deep learning strategy, exemplified in the cardamom plant disease detection approach. (iii) An innovative deep learning-based approach is introduced to precise plant disease detection, crucial in the face of similar symptoms and imbalanced data. The proposed Multilevel Feature Fusion Network (MFFN) incorporates adaptive attention mechanisms, enhancing robustness by considering diverse network features. (iv) With cardamom plant disease classification utilizing U2-Net for background removal and EfficientNetV2 for classification, the network excels the performance on images with complex background, with this generated benchmark dataset with a complex background. This research work produced good results by achieving 99% accuracy on the tomato plant and 98.28% accuracy on the cardamom leaf dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Binary class and multi-class plant disease detection using ensemble deep learning-based approach
(Inderscience Publishers, 2022) Sunil, C.K.; Jaidhar, C.D.; Patil, N.
Providing food for the exponentially growing global population is a highly challenging task. Owing to the demand and supply gap may diminish food production due to diseases in plants, such as bacterial disease, viral disease, and fungal diseases. Early recognition of such diseases and applying an appropriate pesticide or fertiliser can improve crop yield. Accordingly, early plant disease detection necessitates continuous crop monitoring from its initial stages. Recently some research works have been proposed as remedial measures. However, such methodologies utilise costly equipment that is infeasible for small-scale farmers. Thus, there is a need for a cost-effective plant-disease-detection approach. This study embellishes the challenges and opportunities in plant disease detection. Correspondingly, this research proposes an ensemble deep learning-based plant disease diagnosis approach using a combination of AlexNet, ResNet50, and VGG16 deep learning-based models. It effectively ascertains plant diseases by analysing the plant leaf images. A broad set of experiments were conducted using different plant leaf image datasets such as cherry, grape, maize, pepper, potato, strawberry, and cardamom to evaluate the robustness of the proposed approach. Experiential results demonstrated that the proposed approach attained a maximum detection accuracy of 100% for binary and 99.53% for multi-class datasets. © © 2022 Inderscience Enterprises Ltd.
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.
Comparative Analysis of Intrusion Detection System using ML and DL Techniques
(Springer Science and Business Media Deutschland GmbH, 2023) Sunil, C.K.; Reddy, S.; Kanber, S.G.; Vuddanti, V.R.; Patil, N.
Intrusion detection system (IDS) protects the network from suspicious and harmful activities. It scans the network for harmful activity and any potential breaching. Even in the presence of the so many network intrusion APIs there are still problems in detecting the intrusion. These problems can be handled through the normalization of whole dataset, and ranking of feature on benchmark dataset before training the classification models. In this paper, used NSL-KDD dataset for the analysation of various features and test the efficiency of the various algorithms. For each value of k, then, trained each model separately and evaluated the feature selection approach with the algorithms. This work, make use of feature selection techniques like Information gain, SelectKBest, Pearson coefficient and Random forest. And also iterate over the number of features to pick the best values in order to train the dataset.The selected features then tested on different machine and deep learning approach. This work make use of stacked ensemble learning technique for classification. This stacked ensemble learner contains model which makes un-correlated error there by making the model more robust. Â© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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.
Empirical Study on Multi Convolutional Layer-based Convolutional Neural Network Classifier for Plant Leaf Disease Detection
(Institute of Electrical and Electronics Engineers Inc., 2020) Sunil, C.K.; Jaidhar, C.D.; Patil, N.
Recognizing the plant disease automatically in real-time by examining a plant leaf image is highly essential for farmers. This work focuses on an empirical study on Multi Convolutional Layer-based Convolutional Neural Network (MCLCNN) classifier to measure the detection efficacy of MCLCNN on recognizing plant leaf image as being healthy or diseased. To achieve this, a set of experiments were conducted with three distinct plant leaf datasets. Each of the experiments were conducted by setting kernel size of 3Ã— 3 and each experiment was conducted independently with different epochs i.e., 50, 75, 100, 125, and 150. The MCLCNN classifier achieved minimum accuracy of 87.47% with 50 epochs and maximum accuracy of 99.25% with 150 epochs for the Peach plant leaves. Â© 2020 IEEE.
Systematic study on deep learning-based plant disease detection or classification
(Springer Nature, 2023) Sunil, C.K.; Jaidhar, C.D.; Patil, N.
Plant diseases impact extensively on agricultural production growth. It results in a price hike on food grains and vegetables. To reduce economic loss and to predict yield loss, early detection of plant disease is highly essential. Current plant disease detection involves the physical presence of domain experts to ascertain the disease; this approach has significant limitations, namely: domain experts need to move from one place to another place which involves transportation cost as well as travel time; heavy transportation charge makes the domain expert not travel a long distance, and domain experts may not be available all the time, and though the domain experts are available, the domain expert(s) may charge high consultation charge which may not be feasible for many farmers. Thus, there is a need for a cost-effective, robust automated plant disease detection or classification approach. In this line, various plant disease detection approaches are proposed in the literature. This systematic study provides various Deep Learning-based and Machine Learning-based plant disease detection or classification approaches; 160 diverse research works are considered in this study, which comprises single network models, hybrid models, and also real-time detection approaches. Around 57 studies considered multiple plants, and 103 works considered a single plant. 50 different plant leaf disease datasets are discussed, which include publicly available and publicly unavailable datasets. This study also discusses the various challenges and research gaps in plant disease detection. This study also highlighted the importance of hyperparameters in deep learning. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.
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 Ltd
Vehicle Re-identification Using Convolutional Neural Networks
(Springer Science and Business Media Deutschland GmbH, 2023) Kedkar, N.; Karthik Reddy, K.; Arya, H.; Sunil, C.K.; Patil, N.
Vehicle re-identification is the process of matching automobiles from one place on the road (one field of vision) to the next. Important traffic characteristics like the trip duration, travel time variability, section density, and partial dynamic origin/destination needs may be acquired by performing vehicle re-identification. However, doing so without using number plates has become challenging since cars experience substantial variations in attitude, angle of view, light, and other factors, all of which have a major influence on vehicle identification performance. To increase each modelâ€™s representation ability as much as feasible, we apply a variety of strategies that will bring a major change like using filter grafting, semi-supervised learning, and multi-loss. The tests presented in this paper show that such strategies are successful in addressing challenges within this space. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.