Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications.

Abstract

Sugarcane, a vital crop for the global sugar industry, is susceptible to various diseases that significantly impact its yield and quality. Accurate and timely disease detection is crucial for effective management and prevention strategies. We persent the "Sugarcane Leaf Dataset" consisting of 6748 high-resolution leaf images classified into nine disease categories, a healthy leaves category, and a dried leaves category. The dataset covers diseases such as smut, yellow leaf disease, pokkah boeng, mosale, grassy shoot, brown spot, brown rust, banded cholorsis, and sett rot. The dataset's potential for reuse is significant. The provided dataset serves as a valuable resource for researchers and practitioners interested in developing machine learning algorithms for disease detection and classification in sugarcane leaves. By leveraging this dataset, various machine learning techniques can be applied, including deep learning, feature extraction, and pattern recognition, to enhance the accuracy and efficiency of automated sugarcane disease identification systems. The open availability of this dataset encourages collaboration within the scientific community, expediting research on disease control strategies and improving sugarcane production. By leveraging the "Sugarcane Leaf Dataset," we can advance disease detection, monitoring, and management in sugarcane cultivation, leading to enhanced agricultural practices and higher crop yields.

Plant phenotyping relevance

サトウキビ葉の病徴画像を用いた疾患分類データセット自体が中心であり、植物の病害状態を観測・分類する再利用可能な資源である。

Code and data availability

The paper is a data descriptor for the authors' own Sugarcane Leaf Dataset (6748 leaf images across 11 classes), publicly deposited on Mendeley Data with DOI 10.17632/355y629ynj.1 and a direct URL matching an allowed URL. This is a paper-specific, publicly available plant image dataset directly reproducing the paper's酚