RiceCloud: A Cloud integrated Ensemble learning based Rice leaf Diseases Prediction System

Bhowmik, Abhimanyu; Sannigrahi, Madhushree; Chowdhury, Deepraj; Das, Debanjan

doi:10.1109/indicon56171.2022.10039790

Cited by 5 publications

(2 citation statements)

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“…The authors of [34] used a Lightweight CNN model and reported a testing accuracy of 73.02% for all classes. The authors of [35] used an ensemble model (VGG16+Light GBM) and reported a validation accuracy of 96.49% for all classes. In contrast, our proposed Modified LeafNet achieved the highest validation accuracy of 97.44% among all algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…However, for the rice classification dataset, the proposed lightweight CNN model achieved an accuracy of 73.02% for four distinct classes: brown spot, hispa, and leaf blast. Additionally, a similar study by Bhowmik et al [35] proposed an ensemble learning network with VGG16 and the Light GBM model. They used four classes, which were brown spot, healthy, hispa and leaf blast, and achieved an accuracy of 96.49%.…”

Section: Related Workmentioning

confidence: 99%

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Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification

Altabaji,

Umair,

Tan

et al. 2024

IEEE Access

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Early detection of plant diseases is essential for effective crop disease management to prevent yield loss. In this study, we developed a methodology for classifying diseases in rice leaves using four deep learning models and a dataset with 2658 images of healthy and diseased rice leaves. Four models, namely LeafNet, Modified LeafNet, MobileNetV2, and Xception, were compared. The Modified LeafNet model involved updates to LeafNet's architectural parameters, whereas transfer learning techniques were applied to the MobileNetV2 and Xception pretrained models. The optimal hyperparameters for training were determined by considering several factors such as batch size, data augmentation, learning rate, and optimizers. The Modified LeafNet model achieved the highest accuracies of 97.44% and 87.76% for the validation and testing datasets, respectively. In comparison, LeafNet obtained 88.92% and 71.84%, Xception obtained 88.64% and 71.95%, and MobileNetV2 obtained 82.10% and 67.68% for the validation and test accuracies on the same datasets, respectively. This study contributes to the development of automated disease classification systems for rice leaves, thereby leading to increased agricultural productivity and sustainability.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%