An ensembled-deep-learning paradigm trained with a self-improved coyote optimization algorithm (SI-COA) for crop disease detection

In recent times, drastic climate changes have caused a substantial increase in the growth of crop diseases. This causes large-scale demolition of crops, decreases cultivation, and eventually leads to the financial loss of farmers. Due to the rapid growth in a variety of diseases and adequate knowled...

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Vydáno v:Multimedia tools and applications Ročník 84; číslo 4; s. 1697 - 1724
Hlavní autoři: Shukla, Preeti, Chandanan, Amit Kumar
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.01.2025
Springer Nature B.V
Témata:
Algorithms
Artificial neural networks
Cluster analysis
Clustering
Computer Communication Networks
Computer Science
Computer vision
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
Performance measurement
Plant diseases
Recurrent neural networks
Shape optimization
Special Purpose and Application-Based Systems
Texture
Vector quantization
Voting
Crop disease detection
SIFT
RNN
COA
Improved binary gabor pattern
Improved coyote optimization algorithm
GLCM
ISSN:1573-7721, 1380-7501, 1573-7721
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Shrnutí:In recent times, drastic climate changes have caused a substantial increase in the growth of crop diseases. This causes large-scale demolition of crops, decreases cultivation, and eventually leads to the financial loss of farmers. Due to the rapid growth in a variety of diseases and adequate knowledge of farmers, identification, and treatment of the disease have become a major challenge. The leaves have texture and visual similarities which are attributes for the identification of disease type. Hence, computer vision employed with deep learning provides a way to solve this problem. In this research work, a novel voting-based ensembled-deep-learning paradigm is developed for crop disease detection. Initially, the collected raw images are pre-processed via Laplacian Filter and morphological operations. From, the pre-processed image, the ROI region is identified using K-means Clustering. Then, from the ROI regions, the features like texture feature (Chi-squared Local Binary Pattern (CLBP)), Scale Invariant Feature Transform (SIFT), Gray-Level Co-occurrence Matrix (GLCM)), color features, and shape features are extracted. Then, the optimal features are selected by using Self-Improved Coyote Optimization Algorithm (SI-COA) is an extended version of the standard Coyote Optimization Algorithm (COA) and is a metaheuristic for optimization that is inspired by the canis latrans species. For crop disease detection, the voting-based ensembled-deep-learning paradigm was used which includes the Attention-based Bi-LSTM, Recurrent Neural Networks (RNNs), and Optimized Deep Neural Networks (O-DNN). The weight of DNN is optimized via Self-Improved Coyote Optimization Algorithm (SI-COA). The overall outcome of this process is evaluated by using various performance metrics such as Precision, Recall, F1-Score, Accuracy, Sensitivity and Specificity, NPV, FNR, FPR, and MCC, as well.
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ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-024-18991-6