r district (PIN: 613006), with latitude and longitude coordinates available at Google Maps link ). These locations were selected to ensure diverse environmental conditions for the dataset collection. Data accessibility Dataset Name: Okra DiseaseNet Dataset Data Identification Number: DOI: 10.17632/nh7zk4hv8z.1 Direct URL link : https://data.mendeley.com/datasets/nh7zk4hv8z/1 Related research article None 1 Value of the Data The Okra Leaf Disease Dataset is the first dataset collected from Indian agricultural farmlands, specifically for okra crop disease analysis. While many plant disease datasets exist, this dataset stands out due to its high-resolution images (enabled by superior camera len
Open resource ↗10.17632/nh7zk4hv8z.1 · lines:40-68Unverified paper record
Okra disease dataset for classification and segmentation: Dataset collection, analysis and applications.
Data in brief · 3 Jul 2025 · 10.1016/j.dib.2025.111662
Abstract
The early diagnosis of okra leaf diseases is crucial for maintaining crop health and ensuring high agricultural productivity. To facilitate the development of robust deep learning models for automated disease detection, we present a comprehensive dataset of 2500 okra leaf images collected from real-time agricultural fields in India. The dataset consists of six classes, including healthy leaves (Class 0) and five diseased categories: Leaf Curly Virus (Class 1), Alternaria Leaf Spot (Class 2), Cercospora Leaf Spot (Class 3), Phyllosticta Leaf Spot (Class 4), and Downy Mildew (Class 5). Each image is resized to 224 × 224 pixels to ensure compatibility with standard deep learning models. The primary objective of this dataset collection is to provide a benchmark resource for researchers working on early-stage plant disease classification, detection and segmentation. This dataset is unique as it is one of the first publicly available Indian okra leaf disease datasets captured in real-world conditions, incorporating natural variations in lighting, leaf positioning, and environmental factors. It serves as a valuable resource for future young researchers in the field of smart agriculture, enabling advancements in machine learning-based disease diagnosis, smart farming applications, and precision agriculture. Future enhancements will focus on expanding the dataset with more images, including different growth stages and environmental conditions, to improve model generalization and real-world applicability.
Plant phenotyping relevance
植物葉の病害状態を画像で分類・セグメンテーションする公開データセットを構築し、ベンチマーク資源として提供することが中心であるため、植物フェノタイピング手法文献に含める。
abstractwe present a comprehensive dataset of 2500 okra leaf images collected from real-time agricultural fields in India.
abstractThe primary objective of this dataset collection is to provide a benchmark resource for researchers working on early-stage plant disease classification, detection and segmentation.
Code and data availability
The paper is a Data in Brief article presenting the authors' own Okra DiseaseNet dataset of 2500 okra leaf images for disease classification and segmentation, with an explicit public Mendeley Data deposit (DOI 10.17632/nh7zk4hv8z.1) matching an allowed URL. Other listed URLs are cited prior-work datasets, not paper-own
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