Hierarchical Deep Learning Ensemble Framework for Multi-Class Rice Foliar Disease Diagnosis: A Comparative Architecture Analysis

Chanchal Ghosh; Biplab kanti Das; Tapashri Sur; Prasanta Mazumdar; Pratik Halder; Sukanta Kundu; Subhojeet Prasad

doi:10.57159/jcmm.5.1.25248

Authors

Chanchal Ghosh Department of Computer Science and Engineering, Future Institute of Engineering and Management, Kolkata, West Bengal, India 700150
Biplab kanti Das Department of Computer Science and Engineering, Gargi Memorial Institute of Technology, Kolkata, West Bengal, India, 700144
Tapashri Sur Department of Computer Science and Engineering, Future Institute of Engineering and Management, Kolkata, West Bengal, India, 700150
Prasanta Mazumdar Department of Computer Science and Engineering, Future Institute of Engineering and Management, Kolkata, West Bengal, India, 700150
Pratik Halder Department of Computer Science Engineering (AI ML), Heritage Institute of Technology, Anandapur, Kolkata, India, 700107
Sukanta Kundu Department of Computer Science and Engineering, Gargi Memorial Institute of Technology, Kolkata, West Bengal, India, 700144
Subhojeet Prasad Department of Computer Science and Engineering, Gargi Memorial Institute of Technology, Kolkata, West Bengal, India, 700144

DOI:

https://doi.org/10.57159/jcmm.5.1.25248

Keywords:

Rice Leaf Diseases, Smart Farming Framework, Ensemble Model Learning, Deep Learning Models, Agricultural Imaging, Transfer Learning

Abstract

Infestations of foliar diseases in rice plants are common and can reduce harvest yields and affect food supplies worldwide. A system for the automatic detection of these diseases was developed in this study using seven different deep learning models. Six common types of rice leaf diseases were tested using models such as EfficientNet (B0 and B7), ResNet50, InceptionV3, VGG16, and VGG19. The proposed framework integrates the advantages of all models, assigning greater significance to those that exhibit superior performance. By achieving 96.97% accuracy while retaining speed and lightweight features, MobileNetV2 demonstrated superior performance. Both InceptionV3 and EfficientNetB7 performed well, reporting accuracies of 96.78% and 96.40%, respectively. It was also observed that newer, more efficient models exhibited markedly superior performance compared to older deep networks. This method makes it easier to bridge the gap between the urgent need for rapid disease detection on farms and the lack of agricultural experience. The system, which uses low-cost equipment, helps small farmers all over the world diagnose diseases accurately, resulting in better yields of crops.

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