Article: A modified-two-fold-deep-learning-classifier paradigm for crop disease detection Journal: International Journal of Computational Vision and Robotics (IJCVR) 2026 Vol.16 No.2 pp.191 - 221 Abstract: In this research work, a novel modified-two-fold-deep-learning-classifier paradigm is introduced for crop disease detection. The collected raw images are pre-processed via median filtering (for noise removal) and HE (for contrast enhancement). Then, from the pre-processed images, the features like improved texture features (I-CLBP, GMCM, AACM, and EACM), and colour features [(RGB), (HSV) or (HSB)] are extracted. Among the extracted features, the optimal features are selected using a THBOA (proposed). The projected hybrid optimisation model is the conceptual enhancement of standard HBA and TSO, respectively. The leaf disease detection phase is modelled with a modified-two-fold-deep-learning-classifiers approach. In the first phase, the bidirectional LSTM and ARNN. Both classifiers (Bi-LSTM and ARNN) are trained using the optimally selected features. The outcome from Bi-LSTM and ARNN is fed as input to the M-CNN. The final detected outcome is acquired from the modified CNN (proposed). To further enhance the detection accuracy, the loss function of CNN (ultimate decision maker) is modified (instead of the entropy-based loss function, the RMSE is computed). The final detected outcomes (presence/absence of crop disease) are acquired from modified CNN. The proposed model is validated over the existing models in terms of accuracy, precision, and sensitivity as well. Inderscience Publishers - linking academia, business and industry through research

Title: A modified-two-fold-deep-learning-classifier paradigm for crop disease detection

Authors: M. Chithambarathanu; M.K. Jeyakumar

Addresses: Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumarakoil, Nagercoil, Tamilnadu, India ' Department of Computer Applications, Noorul Islam Centre for Higher Education, Kumarakoil, Nagercoil, Tamilnadu, India

Abstract: In this research work, a novel modified-two-fold-deep-learning-classifier paradigm is introduced for crop disease detection. The collected raw images are pre-processed via median filtering (for noise removal) and HE (for contrast enhancement). Then, from the pre-processed images, the features like improved texture features (I-CLBP, GMCM, AACM, and EACM), and colour features [(RGB), (HSV) or (HSB)] are extracted. Among the extracted features, the optimal features are selected using a THBOA (proposed). The projected hybrid optimisation model is the conceptual enhancement of standard HBA and TSO, respectively. The leaf disease detection phase is modelled with a modified-two-fold-deep-learning-classifiers approach. In the first phase, the bidirectional LSTM and ARNN. Both classifiers (Bi-LSTM and ARNN) are trained using the optimally selected features. The outcome from Bi-LSTM and ARNN is fed as input to the M-CNN. The final detected outcome is acquired from the modified CNN (proposed). To further enhance the detection accuracy, the loss function of CNN (ultimate decision maker) is modified (instead of the entropy-based loss function, the RMSE is computed). The final detected outcomes (presence/absence of crop disease) are acquired from modified CNN. The proposed model is validated over the existing models in terms of accuracy, precision, and sensitivity as well.

Keywords: plant diseases and pests; digital image processing; I-CLBP; tuna honey badger optimisation algorithm; THBOA; modified-two-fold-deep-learning-classifiers.

DOI: 10.1504/IJCVR.2026.151540

International Journal of Computational Vision and Robotics, 2026 Vol.16 No.2, pp.191 - 221

Received: 24 Mar 2023
Accepted: 15 Nov 2023
Published online: 05 Feb 2026 *

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Title: A modified-two-fold-deep-learning-classifier paradigm for crop disease detection

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