Image-Based Hot Pepper Disease and Pest Diagnosis Using Transfer Learning and Fine-Tuning.

Abstract

Past studies of plant disease and pest recognition used classification methods that presented a singular recognition result to the user. Unfortunately, incorrect recognition results may be output, which may lead to further crop damage. To address this issue, there is a need for a system that suggest several candidate results and allow the user to make the final decision. In this study, we propose a method for diagnosing plant diseases and identifying pests using deep features based on transfer learning. To extract deep features, we employ pre-trained VGG and ResNet 50 architectures based on the ImageNet dataset, and output disease and pest images similar to a query image via a k -nearest-neighbor algorithm. In this study, we use a total of 23,868 images of 19 types of hot-pepper diseases and pests, for which, the proposed model achieves accuracies of 96.02 and 99.61%, respectively. We also measure the effects of fine-tuning and distance metrics. The results show that the use of fine-tuning-based deep features increases accuracy by approximately 0.7-7.38%, and the Bray-Curtis distance achieves an accuracy of approximately 0.65-1.51% higher than the Euclidean distance.

Plant phenotyping relevance

植物画像から病害状態・害虫被害を診断する画像解析手法を提案し、転移学習、微調整、距離指標の効果と精度を評価しており、フェノタイピング手法が中心です。

Code and data availability

The paper uses a hot-pepper disease/pest image dataset provided by the National Institute of Horticultural and Herbal Science, but no public deposit, availability URL, or author code release is stated in the supplied blocks. All URLs present are cited references (arXiv, FAOSTAT, Google Vision), not paper-specific asset

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