VCNet: Optimized Deep Learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detection
Convolution Neural Networks (CNN) are best in their ability to detect rice diseases but still face challenges in generalizing equally well for all classes of disease in multiclass classification. Detecting rice crop disease like sheath rot is still challenging due to unavailability of dataset and in...
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Elsevier
2025-12-01
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| author | Sanam Salman Kazi Bhakti Palkar Dhirendra Mishra |
| author_facet | Sanam Salman Kazi Bhakti Palkar Dhirendra Mishra |
| author_sort | Sanam Salman Kazi |
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| description | Convolution Neural Networks (CNN) are best in their ability to detect rice diseases but still face challenges in generalizing equally well for all classes of disease in multiclass classification. Detecting rice crop disease like sheath rot is still challenging due to unavailability of dataset and intraclass variations in symptoms. Transfer learning models take more resources for execution due to its deep architecture. To conquer these challenges, VCNet, an optimized, novel and efficient multiclass rice crop disease detection framework is proposed. The study focuses on developing a shallow model with deep feature extraction to bring down the computational load with reduced time for training without compromising on any performance parameters. Further the model goes through two level optimization process where optimal hyperparameters identified through experimentation is given as parameters to genetic algorithm for optimization of VCNet during training. Novel dataset containing field images is generated with the help of plant pathologist to improve model capability to identify diseases. Rigorous empirical comparison and evaluation with state-of-the-art models for each class of disease is done to validate proposed technique. VCNet outperforms the existing transfer learning models with training accuracy 99.72 % and testing accuracy 97.71 %. It also requires fewer parameters and takes minimum training time. • The major contribution of this study is the design of an optimized, efficient and enhanced deep learning technique for multiclass rice crop disease detection embracing with batch normalization, dropout and genetic optimization algorithm to improve generalization power and restrict the overlearning capability for seen and unseen data. • Proposed VCNet, a shallow model with deep feature extraction, employs VGG16 layers for initial extraction fused with custom CNN architecture to correctly detect the challenging classes of diseases like sheath rot in multiclass classification. • The most significant observation is that VCNet accurately predicts the rice disease for each class of diseases under study whereas the existing powerful models largely misclassified for some classes of diseases in multiclass classification. |
| format | Article |
| id | doaj-art-d4aa0c1b94b841e4b95339e3039464ad |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-d4aa0c1b94b841e4b95339e3039464ad2025-08-20T03:38:58ZengElsevierMethodsX2215-01612025-12-011510355110.1016/j.mex.2025.103551VCNet: Optimized Deep Learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detectionSanam Salman Kazi0Bhakti Palkar1Dhirendra Mishra2Department of Computer Engineering, K. J. Somaiya School of Engineering (KJSSE), Somaiya Vidyavihar University, Mumbai, Maharashtra 400077, India; Department of Engineering and Technology, Bharati Vidyapeeth Deemed to be University, Kharghar, Navi Mumbai, Maharashtra 410210, India; Corresponding author.Department of Computer Engineering, K. J. Somaiya School of Engineering (KJSSE), Somaiya Vidyavihar University, Mumbai, Maharashtra 400077, IndiaDepartment of Computer Engineering, SVKM’s NMIMS Deemed to be University Mukesh Patel School of Technology Management & Engineering, Mumbai, 400056, IndiaConvolution Neural Networks (CNN) are best in their ability to detect rice diseases but still face challenges in generalizing equally well for all classes of disease in multiclass classification. Detecting rice crop disease like sheath rot is still challenging due to unavailability of dataset and intraclass variations in symptoms. Transfer learning models take more resources for execution due to its deep architecture. To conquer these challenges, VCNet, an optimized, novel and efficient multiclass rice crop disease detection framework is proposed. The study focuses on developing a shallow model with deep feature extraction to bring down the computational load with reduced time for training without compromising on any performance parameters. Further the model goes through two level optimization process where optimal hyperparameters identified through experimentation is given as parameters to genetic algorithm for optimization of VCNet during training. Novel dataset containing field images is generated with the help of plant pathologist to improve model capability to identify diseases. Rigorous empirical comparison and evaluation with state-of-the-art models for each class of disease is done to validate proposed technique. VCNet outperforms the existing transfer learning models with training accuracy 99.72 % and testing accuracy 97.71 %. It also requires fewer parameters and takes minimum training time. • The major contribution of this study is the design of an optimized, efficient and enhanced deep learning technique for multiclass rice crop disease detection embracing with batch normalization, dropout and genetic optimization algorithm to improve generalization power and restrict the overlearning capability for seen and unseen data. • Proposed VCNet, a shallow model with deep feature extraction, employs VGG16 layers for initial extraction fused with custom CNN architecture to correctly detect the challenging classes of diseases like sheath rot in multiclass classification. • The most significant observation is that VCNet accurately predicts the rice disease for each class of diseases under study whereas the existing powerful models largely misclassified for some classes of diseases in multiclass classification.http://www.sciencedirect.com/science/article/pii/S2215016125003954Deep learningRice disease detectionOptimizationMulticlass ClassificationTransfer learning |
| spellingShingle | Sanam Salman Kazi Bhakti Palkar Dhirendra Mishra VCNet: Optimized Deep Learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detection MethodsX Deep learning Rice disease detection Optimization Multiclass Classification Transfer learning |
| title | VCNet: Optimized Deep Learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detection |
| title_full | VCNet: Optimized Deep Learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detection |
| title_fullStr | VCNet: Optimized Deep Learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detection |
| title_full_unstemmed | VCNet: Optimized Deep Learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detection |
| title_short | VCNet: Optimized Deep Learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detection |
| title_sort | vcnet optimized deep learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detection |
| topic | Deep learning Rice disease detection Optimization Multiclass Classification Transfer learning |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125003954 |
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