ease dataset in- cludes 11 categories of rice diseases and 1 category of healthy leaves, totaling 11,281 images. The categories in the dataset are illustrated in Figure 1, and the number of images per cate- gory is detailed in Table 1. The dataset is divided into training, validation, and testing sets, with a ratio of 60:20:20 (https://www.kaggle.com/datasets/trumanrase/rice-leaf-diseases, accessed on 22 July 2024). The search engine method involves automatically downloading images by inputting keywords into Google using a Python script. The downloaded images are then filtered and cleaned to ensure data accuracy. The images for the disease categories bacterial leaf streak, Hispa, and rice sh
Open resource ↗Kaggle · trumanrase/rice-leaf-diseases · pdf-raw-page:3 lines:1-44Unverified paper record
Deep Learning-Based Methods for Multi-Class Rice Disease Detection Using Plant Images
Agronomy · 23 Aug 2024 · 10.3390/agronomy14091879
Abstract
Rapid and accurate diagnosis of rice diseases can prevent large-scale outbreaks and reduce pesticide overuse, thereby ensuring rice yield and quality. Existing research typically focuses on a limited number of rice diseases, which makes these studies less applicable to the diverse range of diseases currently affecting rice. Consequently, these studies fail to meet the detection needs of agricultural workers. Additionally, the lack of discussion regarding advanced detection algorithms in current research makes it difficult to determine the optimal application solution. To address these limitations, this study constructs a multi-class rice disease dataset comprising eleven rice diseases and one healthy leaf class. The resulting model is more widely applicable to a variety of diseases. Additionally, we evaluated advanced detection networks and found that DenseNet emerged as the best-performing model with an accuracy of 95.7%, precision of 95.3%, recall of 94.8%, F1 score of 95.0%, and a parameter count of only 6.97 M. Considering the current interest in transfer learning, this study introduced pre-trained weights from the large-scale, multi-class ImageNet dataset into the experiments. Among the tested models, RegNet achieved the best comprehensive performance, with an accuracy of 96.8%, precision of 96.2%, recall of 95.9%, F1 score of 96.0%, and a parameter count of only 3.91 M. Based on the transfer learning-based RegNet model, we developed a rice disease identification app that provides a simple and efficient diagnosis of rice diseases.
Plant phenotyping relevance
イネ葉画像から病害状態を推定する深層学習手法を開発・比較し、データセットと診断アプリまで構築しており、植物病害表現型の取得・抽出が中心である。
abstractthis study constructs a multi-class rice disease dataset comprising eleven rice diseases and one healthy leaf class.
abstractAdditionally, we evaluated advanced detection networks and found that DenseNet emerged as the best-performing model
abstractBased on the transfer learning-based RegNet model, we developed a rice disease identification app
Code and data availability
The paper's rice disease image dataset was partly acquired from a public Kaggle dataset (trumanrase/rice-leaf-diseases), which is a paper-specific, publicly available plant image asset used directly for the disease classification experiments. No author analysis code, trained model checkpoints, or supplementary deposits
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