TomatoDiverse：An Open Dataset for Industrial Tomato Detection in Complex Natural Environments

Industrial tomatoes are one of the important agricultural products worldwide and play a key role in the agricultural economy. To promote the application of computer vision in smart agriculture, due to the lack of suitable datasets, we focus on building a dataset focused on industrial tomato target detection in complex natural environments. The data is collected from the industrial tomato planting base in Hejing County, Bayingolin Mongolian Autonomous Prefecture, Xinjiang Uygur Autonomous Region. It covers 1502 industrial tomato images with a resolution of 4000x3000 pixels captured by cameras in complex natural environments, taking into account changes in illumination 、distance、occlusions and other challenges in various natural environments. The images are all in jpg format and annotated using LabelImg data annotation software. Using Pascal VOC XML format and YOLO format, we provide accurate tomato bounding box information for each image. The database is divided into three levels. The total folder contains three sub-folders, named smooth illumination, counter illumination and back illumination respectively. These three folders represent the first level, each of which contains two subfolders (representing the second level), named sparse and dense. Each folder in the second level contains four sub-folders (representing the third level), named cover, cover_labels, no_cover, no_cover_labels. The pictures contained in each folder have the same name labels matching them. This data resource was established to support the research on industrial tomato target detection algorithms and promote the development of smart agriculture. This dataset can be used to delve into research on intelligent control systems, automated harvesting robots, maturity assessment, and yield estimation.工业番茄是全球重要的农产品之一，在农业经济中占据关键地位。为推动计算机视觉（Computer Vision）在智慧农业中的应用，鉴于当前适配数据集匮乏，本研究聚焦构建面向复杂自然环境的工业番茄目标检测数据集。本数据集采集自新疆维吾尔自治区巴音郭楞蒙古自治州和静县的工业番茄种植基地，共包含1502张分辨率为4000×3000像素的图像，均由相机在复杂自然环境下拍摄，覆盖光照变化、拍摄距离差异、目标遮挡等各类自然环境挑战场景。所有图像均采用JPG格式存储，并通过LabelImg数据标注软件完成标注，以Pascal VOC XML与YOLO两种格式为每张图像提供精准的番茄目标边界框信息。本数据库采用三级分类架构：总文件夹下设3个一级子文件夹，分别命名为顺光（smooth illumination）、逆光（counter illumination）与背光（back illumination）；每个一级子文件夹均包含2个二级子文件夹，分别为稀疏（sparse）与密集（dense）；每个二级子文件夹下再设4个三级子文件夹，分别为含遮挡（cover）、含遮挡标注（cover_labels）、无遮挡（no_cover）与无遮挡标注（no_cover_labels）。每个三级子文件夹内的图像均配有与之同名的标注文件。本数据集的构建初衷为支撑工业番茄目标检测算法的相关研究，推动智慧农业的发展。本数据集可用于智能控制系统、自动化采收机器人、番茄成熟度评估以及产量估算等方向的研究探索。

搜集汇总

背景与挑战

背景概述

TomatoDiverse是一个开放数据集，专注于工业番茄在复杂自然环境中的目标检测。它包含1502张高分辨率图像，覆盖多种光照、距离和遮挡挑战，并提供Pascal VOC XML和YOLO格式的标注，以支持智能农业研究，如自动化收割和产量估算。数据集结构层次分明，基于光照和密度条件组织，适用于算法开发和实际农业应用。