Detection of Rice Diseases: Leaf Blast, Bacterial Leaf Light, and Brown Spot Using Image Enhancement and Faster Region-Based Convolutional Neural Network

Abstract

Rice diseases such as leaf blight, blast, and brown spot remain major constraints on food security and rural livelihoods across Southeast Asia, causing significant yield losses each year. In Indonesia, particularly in Lamongan, East Java, these pathogens threaten smallholder productivity and disrupt national rice supply chains. This study aims to enhance automated rice disease detection under real agricultural conditions by integrating image preprocessing techniques with a deep learning-based detection framework. The main contribution lies in developing a hybrid pipeline that combines RGB-to-grayscale conversion and contrast stretching prior to model training, effectively mitigating low-contrast conditions and noise commonly found in field-acquired image datasets. The enhanced images are subsequently processed using the Faster Region-Based Convolutional Neural Network (Faster R-CNN) with a ResNet-50 backbone to localize and classify disease symptoms. Experiments conducted on a dataset of 1,500 annotated rice leaf images achieved high detection performance, with accuracies of 97.37% for leaf blight, 94.12% for blast, and 95.24% for brown spot. Compared with the baseline Faster R-CNN model, the proposed approach improved classification accuracy from 0.8906 to 0.9297, reduced false negatives from 0.439 to 0.1998, increased foreground classification accuracy from 0.55 to 0.78, and descreased total loss from 0.839 to 0.6493. These results demonstrate that integrating RGB-to-grayscale conversion and contrast stretching significantly enhances feature representation, leading to improved detection accuracy, reduced error rates, and more stable training behavior. Overall, the proposed framework provides a robust and reliable approach for rice disease identification and offers strong potential for practical deployment in precision agriculture systems.

Plant phenotyping relevance

イネ葉の病徴を画像から検出・分類する画像前処理とFaster R-CNNの統合手法を開発し、性能比較・検証しているため、植物表現型取得が中心である。

Code and data availability

The paper describes a rice disease detection pipeline (Faster R-CNN with preprocessing) using a Kaggle-sourced and field-collected dataset, but provides no authors' public URL, deposit, or availability statement for the dataset, annotations, code, or trained model. The Kaggle mention lacks any specific link, and Robofl

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