Article: Intelligent serial cascade of hybrid deep learning model for plant leaf disease identification and classification with multi-scale dilation assisted 3D-CNN features Journal: International Journal of Computational Vision and Robotics (IJCVR) 2026 Vol.16 No.4 pp.439 - 471 Abstract: A novel deep learning framework is explored for plant leaf disease detection to resolve the challenges of existing leaf disease detection models. The pre-processed through optimal weighted threshold histogram equalisation. The parameters inside the histogram equalisation approach are optimised via the hybrid heuristic algorithm like rat aquila swarm optimisation (RASO). Subsequently, the deep features from the pre-processed image are acquired through multi-scale dilation assisted 3D-CNN. Thus, the resultant image is classified using the serial cascade of autoencoder and gated recurrent unit (GRU) (SC-AGRU). Then, the RASO is also used to perform the parameter tuning to increase the classification performance. Throughout the analysis, the accuracy and precision rate of the suggested method are 96% and 95%. Thus, the overall effectiveness of the proposed plant leaf disease classification technique is encountered by conducting a comparative analysis of various plant leaf disease classification techniques regarding various evaluation measures. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent serial cascade of hybrid deep learning model for plant leaf disease identification and classification with multi-scale dilation assisted 3D-CNN features

Authors: P. Vinay; G. Santhosh Kumar

Addresses: Computer Science and Engineering, Canara Engineering College, Benjanapadavu-574219, Mangaluru, India ' Electronics and Communication Engineering, East West College of Engineering, Yelahanka New Town-560064, Bengaluru, India

Abstract: A novel deep learning framework is explored for plant leaf disease detection to resolve the challenges of existing leaf disease detection models. The pre-processed through optimal weighted threshold histogram equalisation. The parameters inside the histogram equalisation approach are optimised via the hybrid heuristic algorithm like rat aquila swarm optimisation (RASO). Subsequently, the deep features from the pre-processed image are acquired through multi-scale dilation assisted 3D-CNN. Thus, the resultant image is classified using the serial cascade of autoencoder and gated recurrent unit (GRU) (SC-AGRU). Then, the RASO is also used to perform the parameter tuning to increase the classification performance. Throughout the analysis, the accuracy and precision rate of the suggested method are 96% and 95%. Thus, the overall effectiveness of the proposed plant leaf disease classification technique is encountered by conducting a comparative analysis of various plant leaf disease classification techniques regarding various evaluation measures.

Keywords: plant leaf disease identification; optimal weighted threshold histogram equalisation; rat Aquila swarm optimisation; serial cascade of autoencoder and gated recurrent unit neural network; multi-scale dilation assisted convolution neural network.

DOI: 10.1504/IJCVR.2026.154145

International Journal of Computational Vision and Robotics, 2026 Vol.16 No.4, pp.439 - 471

Received: 10 Nov 2022
Accepted: 08 Feb 2024
Published online: 15 Jun 2026 *

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Title: Intelligent serial cascade of hybrid deep learning model for plant leaf disease identification and classification with multi-scale dilation assisted 3D-CNN features

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