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

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

ensemblelearning;weighted averagingvoting;convolutional neural network;image classification;plant disease

Deeplearning, especially convolutional neural networks (CNN), has gained traction in the field of image classification. In the specific case of plant diseaseclassification, improving the accuracy and reliability of image classification is paramount. This paper delves into the ensemble predictiontechnique 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 Xceptionfocusing 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

Octavian1, Ahmad Badruzzaman2, Muhammand Yusuf Ridho3, Bayu Distiawan Trisedya4

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5669/926

Faculity of Computer Sciences, University ofIndonesia, Depok, Indonesia

25-04-2024

FAJAR BAGUS W

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