Crop disease detection via ensembled-deep-learning paradigm and ABC Coyote pack optimization algorithm (ABC-CPOA)

Crop disease is a significant issue that affects the growth and yield of crops, leading to financial loss for farmers. Identification and treatment of crop diseases have become challenging due to the increase in the variety of diseases and the lack of knowledge among farmers. To address this issue,...

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Published in:	Multimedia tools and applications Vol. 84; no. 1; pp. 37 - 62
Main Authors:	Chithambarathanu, M., Jeyakumar, M. K.
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.01.2025 Springer Nature B.V
Subjects:	Algorithms Artificial neural networks Color Computer Communication Networks Computer Science Convexity Crop diseases Data Structures and Information Theory Deep learning Feature extraction Heuristic methods Image filters Machine learning Medical imaging Multimedia Information Systems Neural networks Optimization algorithms Optimization models Plant diseases Recurrent neural networks Shape Special Purpose and Application-Based Systems Weighting functions O-DNN Crop disease detection RNN Attribute based -Bi LSTM Self-Improved Coyote Optimization Algorithm
ISSN:	1573-7721, 1380-7501, 1573-7721
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Abstract	Crop disease is a significant issue that affects the growth and yield of crops, leading to financial loss for farmers. Identification and treatment of crop diseases have become challenging due to the increase in the variety of diseases and the lack of knowledge among farmers. To address this issue, this investigate uses an ensembled-deep-learning paradigm to propose a deep learning-based model for crop disease identification trained with an ABC-CPOA. Initially, collected raw images are pre-processed via Bilateral filter and gamma correction Feature Extraction: Then, from the pre-processed images, the features like texture feature (Local Quinary Pattern (LQP), Local Gradient Pattern (LGP), Enriched Local Binary Pattern (E-LBP), color features (Color Histogram, Color Moments), shape features (Contour-based features, Convex Hull). Optimal feature selection- Among the extracted features, the optimal features is designated by means of a self-improved meta-heuristic optimization model referred as ABC-CPOA. This ABC-CPOA model is an extended version of standard Coyote Optimization Algorithm (COA). Crop disease detection phase is modelled with a new ensembled-deep-learning paradigm. Ensembled-deep-learning paradigm comprises Attention-based Bi-LSTM, Recurrent Neural Networks (RNNs) and Optimized Deep Neural Network (O-DNN). The weight function of O-DNN is fine-tuned using the new ABC-CPOA. Precision, recall, sensitivity, and specificity, in addition to TPR, FPR, FNR, and TNR, F1-score, and accuracy are used to assess the suggested approach. The implementation was performed by the MATLAB tool (version: 2022B).
AbstractList	Crop disease is a significant issue that affects the growth and yield of crops, leading to financial loss for farmers. Identification and treatment of crop diseases have become challenging due to the increase in the variety of diseases and the lack of knowledge among farmers. To address this issue, this investigate uses an ensembled-deep-learning paradigm to propose a deep learning-based model for crop disease identification trained with an ABC-CPOA. Initially, collected raw images are pre-processed via Bilateral filter and gamma correction Feature Extraction: Then, from the pre-processed images, the features like texture feature (Local Quinary Pattern (LQP), Local Gradient Pattern (LGP), Enriched Local Binary Pattern (E-LBP), color features (Color Histogram, Color Moments), shape features (Contour-based features, Convex Hull). Optimal feature selection- Among the extracted features, the optimal features is designated by means of a self-improved meta-heuristic optimization model referred as ABC-CPOA. This ABC-CPOA model is an extended version of standard Coyote Optimization Algorithm (COA). Crop disease detection phase is modelled with a new ensembled-deep-learning paradigm. Ensembled-deep-learning paradigm comprises Attention-based Bi-LSTM, Recurrent Neural Networks (RNNs) and Optimized Deep Neural Network (O-DNN). The weight function of O-DNN is fine-tuned using the new ABC-CPOA. Precision, recall, sensitivity, and specificity, in addition to TPR, FPR, FNR, and TNR, F1-score, and accuracy are used to assess the suggested approach. The implementation was performed by the MATLAB tool (version: 2022B).
Author	Jeyakumar, M. K. Chithambarathanu, M.
Author_xml	– sequence: 1 givenname: M. surname: Chithambarathanu fullname: Chithambarathanu, M. email: chithambaramthanu@gmail.com organization: Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education – sequence: 2 givenname: M. K. surname: Jeyakumar fullname: Jeyakumar, M. K. organization: Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education
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SubjectTerms	Algorithms Artificial neural networks Color Computer Communication Networks Computer Science Convexity Crop diseases Data Structures and Information Theory Deep learning Feature extraction Heuristic methods Image filters Machine learning Medical imaging Multimedia Information Systems Neural networks Optimization algorithms Optimization models Plant diseases Recurrent neural networks Shape Special Purpose and Application-Based Systems Weighting functions
Title	Crop disease detection via ensembled-deep-learning paradigm and ABC Coyote pack optimization algorithm (ABC-CPOA)
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