Dragon Fruit Maturity Detection and Quality Grading Dataset

(1) Everyone desires to procure top-notch, fresh fruits. In today's health-conscious society, individuals are meticulous about their dietary choices. They firmly believe that spoiled fruits can adversely affect their well-being. Consequently, the fruit market experiences a decline, leading to substantial economic implications. A key factor contributing to fruit spoilage is the manual process of gauging maturity in Bangladesh. Without being harvested at the appropriate time, fruits tend to decay over time. Accurate identification of ripe and unripe fruits is essential in determining the ideal harvest period. Dragon fruit stands as a significant, nutrient-rich crop widely cultivated across Bangladesh. It's estimated that considerable financial losses occur daily in Bangladesh due to the decay of dragon fruits. Therefore, an automated system for categorizing mature, immature, fresh, and defective dragon fruits is imperative to address this issue, benefiting fruit cultivators, vendors, and processing industries. (2) In this modern age, computer vision methods show significant promise in executing classification and detection assignments of this nature. (3) To create algorithms based on computer vision, a comprehensive dataset for Dragon Fruit is introduced, comprising both Maturity Detection and Quality Grading datasets. The Maturity Detection Dataset encompasses Immature and Mature Dragon Fruits, while the Quality Grading Dataset distinguishes Fresh from Defective Dragon Fruits. Classifications within this dataset were established in collaboration with an agricultural institute's domain expert. (4) A total of 3779 images of mature, immature, fresh, and defect dragon fruits were collected from the demonstration areas of three different locations in Bangladesh. Then from these original images, a total of 10,010 augmented images are produced by using flipping, width shifting, height shifting, brightening, rotating, shearing, and zooming techniques to increase the data number.

(1) 人人都渴望获取优质新鲜的水果。在如今注重健康的社会中，人们对饮食选择愈发考究，坚信变质水果会对身体健康产生不利影响。由此导致水果市场出现滞销，进而引发显著的经济损失。在孟加拉国，人工判定果实成熟度的流程是水果腐败的关键诱因之一：若未在适宜时机采摘，果实便会随时间推移逐渐腐烂。精准识别成熟与未成熟果实，是确定最佳采摘期的核心前提。火龙果是孟加拉国广泛种植的重要营养作物，据估算，该国每日因火龙果腐烂造成的经济损失颇为可观。因此，开发一套能够自动分类成熟、未成熟、新鲜及瑕疵火龙果的系统，对解决这一问题至关重要，此举将惠及果农、经销商以及果品加工行业。
(2) 在当今时代，计算机视觉（Computer Vision）方法在执行此类分类与检测任务方面展现出巨大潜力。
(3) 为构建基于计算机视觉的算法，本研究推出了一套面向火龙果的综合性数据集，包含成熟度检测（Maturity Detection）数据集与品质分级（Quality Grading）数据集两大组成部分。其中，成熟度检测数据集涵盖未成熟与成熟火龙果样本，品质分级数据集则用于区分新鲜与瑕疵火龙果。本数据集的分类标准由农业领域的专家协同制定。
(4) 研究人员从孟加拉国三处不同示范区域采集了3779张原始图像，样本涵盖成熟、未成熟、新鲜及瑕疵火龙果。随后，通过翻转、宽度偏移、高度偏移、亮度调节、旋转、剪切及缩放等数据增强技术对原始图像进行处理，最终生成共计10010张增强图像，以扩充数据集规模。