Automated Plant Leaf Classification using Ensemble Transfer Learning in CNN model

S, Yuvalatha; Keerthika, J.; Prabhavathy, S.; Banupriya, M.; Priyadharshini, R.

doi:10.1109/nkcon56289.2022.10126722

Cited by 2 publications

(1 citation statement)

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“…Ghosal S et al [7] researched transfer learning, repurposing pre-trained models for rice plant disease classification, and achieved a reduction in training time without compromising on accuracy. In more recent research, Ho et al [8] and Yuvalatha et al [9] are using ensemble CNN techniques, both studies leveraged various transfer learning models, including ResNet and DenseNet architectures, to achieve high accuracies in identifying plant diseases early, with results showing promise for enhancing agricultural practices and reducing crop losses.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Weighted Averaging for CNN Model Ensemble in Plant Diseases Image Classification

Octavian,

Badruzzaman,

Muhammand Yusuf Ridho

et al. 2024

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Deep learning, especially convolutional neural networks (CNN), has gained traction in the field of image classification. In the specific case of plant disease classification, improving the accuracy and reliability of image classification is paramount. This paper delves into the ensemble prediction technique using a weighted soft-voting method. Instead of assigning a generalized weight to each CNN model, our approach emphasizes giving weights to each label's prediction within every individual model. We employed three esteemed CNN architectures for our experiments: DenseNet201, InceptionV3, and Xception focusing on classifying various diseases affecting grapes. By harnessing transfer learning coupled with end-to-end fine-tuning, we achieved a streamlined and efficient training process. Notably, the f1-score for each grape disease class was used as a parameter for weight determination and as a metric for the final evaluation. In our study, the newly proposed method was tested across various datasets and ensemble scenarios, demonstrating its effectiveness by not only outperforming the conventional soft-voting and prevalent weighted soft-voting methods, which achieved best scores of 95.68% and 95.81% respectively, but also by achieving a remarkable accuracy of 96.56%. This method's efficacy is heightened when ensemble models exhibit distinct characteristics; the more varied the model characteristics, the more enhanced the ensemble results.

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Section: Introductionmentioning

confidence: 99%