Tomato Leaf Disease Classification Dataset

Dataset overview:
The dataset consists of 2995 high-resolution tomato leaf images, which are organized into seven different categories that include disease-stricken specimens, healthy leaf specimens, and deficiencies. These images were captured over different agricultural regions of Bangladesh during a time span of March 10 to March 20, 2025, while showing diverse environmental conditions as well as different leaf growth phases with various disease severity levels.
The classifications of the dataset consist of
Bacterial Spot—117 images
Yellow Leaf Curl Virus—234 images
Early Blight—524 images
Healthy—380 images
Late Blight—112 images
Leaf Miner Flies—884 images
Magnesium Deficiency—744 images
Purpose:
The data provides necessary elements to establish automatic disease identification systems on tomato plants through image processing tools alongside machine learning techniques. The system possesses functions that can develop AI models for precise agriculture and educate deep learning systems for implementing sustainable farming methods. The data set allows three fundamental applications, which include supervised learning model design as well as feature extraction analysis and real-time disease detection systems implementation.

数据集概览：
本数据集包含2995张高分辨率番茄叶片图像，共划分为7个类别，涵盖病害感染样本、健康叶片样本以及营养缺乏样本。这些图像于2025年3月10日至3月20日期间，在孟加拉国不同农业产区采集，涵盖了多样的环境条件、不同的叶片生长阶段以及不同病害严重程度的样本。

本数据集的类别划分如下：
细菌性斑点病（Bacterial Spot）：117张
黄化曲叶病毒病（Yellow Leaf Curl Virus）：234张
早疫病（Early Blight）：524张
健康叶片（Healthy）：380张
晚疫病（Late Blight）：112张
斑潜蝇虫害（Leaf Miner Flies）：884张
镁元素缺乏症（Magnesium Deficiency）：744张

数据集用途：
本数据集可为通过图像处理工具与机器学习技术构建番茄植株病害自动识别系统提供必要支撑，此类系统可开发面向精准农业的人工智能（AI）模型，同时为实现可持续耕作的深度学习系统提供训练数据。本数据集可支撑三类核心应用：监督学习模型设计、特征提取分析以及实时病害检测系统的搭建。

搜集汇总

背景与挑战

背景概述

该数据集包含2995张高分辨率番茄叶片图像，涵盖7种健康状态和病害类型（如细菌性斑点病、黄曲叶病毒等），拍摄于孟加拉国不同农业区域。主要用于开发基于AI的精准农业病害识别系统，支持监督学习模型设计和实时病害检测应用。