Tomato Maturity Detection and Quality Grading Dataset

(1) Everyone wants to purchase high-quality, fresh fruits. People nowadays are so concerned about their health and conscious about what they should consume and what they should not. According to them, rotten fruits are detrimental to their health. As a result, the sale of fruits suffers which brings significant economic ramifications. One of the main reasons behind fruits getting defective is that the maturity detection process is still manual in Bangladesh and fruits would go rotten with the passage of time if it is not harvested at the proper time. To understand the proper harvesting time it is crucial to detect the mature and immature fruit properly. Tomato is one of the significant, widely consumed, well-liked, and nutrient-dense crops grown throughout Bangladesh. According to the estimation, in Bangladesh, every day there are significant financial losses because tomatoes become rotten easily. Hence, automated classification of mature, immature, fresh, and rotten tomato identification is indispensable to overcome this situation and bring out the benefit to fruit growers, retailers, and processing firms.

(2) In the recent era, computer vision techniques are very promising in performing such types of classification and detection tasks.

(3) With a view to developing computer vision-based algorithms, an extensive tomato dataset is presented containing Tomato Maturity Detection Dataset and Tomato Quality Grading Dataset. Tomato Maturity Detection Dataset consists of two classes namely immature and mature tomatoes whereas Tomato Quality Grading Dataset contains fresh and rotten tomatoes. The classifications of this dataset are done with the help of a domain expert from an agricultural institute.

(4) A total of 2986 images of mature, immature, fresh, and rotten tomatoes were collected from Sher-e-Bangla Agricultural University. Then from these original images, a total of 10,000 augmented images are produced by using rotation, zoom, flipping, and scaling techniques to increase the data number.

(1) 人人皆渴望购得优质新鲜的果品。当下民众愈发关注自身健康，对饮食宜忌愈发明晰。他们深知腐烂水果有损健康，这会导致果品销量下滑，进而造成严重的经济损失。在孟加拉国，果品成熟度检测仍依赖人工流程，若未适时采收，果品便会随时间推移逐渐腐烂，这正是果品出现瑕疵的主要原因之一。准确识别成熟与未成熟果品，是确定最佳采收时机的关键所在。番茄是孟加拉国广泛种植、深受欢迎且营养丰富的重要作物之一。据估算，孟加拉国每日因番茄易腐烂而蒙受巨额经济损失。故此，实现成熟、未成熟、新鲜与腐烂番茄的自动化分类识别，对改善当前局面、惠及果农、零售商及加工企业而言至关重要。
(2) 近年来，计算机视觉（Computer Vision）技术在各类分类与检测任务中展现出极具潜力的应用前景。
(3) 为开发基于计算机视觉的算法，本研究构建了一套涵盖番茄成熟度检测数据集（Tomato Maturity Detection Dataset）与番茄品质分级数据集（Tomato Quality Grading Dataset）的大型番茄数据集。其中，番茄成熟度检测数据集包含未成熟与成熟番茄两个类别，而番茄品质分级数据集则涵盖新鲜与腐烂番茄两类样本。该数据集的分类标注工作由来自农业院校的领域专家完成。
(4) 研究团队从谢赫·穆吉布·拉赫曼农业大学（Sher-e-Bangla Agricultural University）采集了共计2986张成熟、未成熟、新鲜及腐烂番茄的原始图像，随后通过旋转、变焦、翻转与尺度缩放等数据增强技术，从原始图像中生成了总计10000张增强图像，以扩充数据集规模。