RaspGrade

🍓 The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning This research investigates the application of computer vision for rapid, accurate, and non-invasive food quality assessment, focusing on the novel challenge of real-time raspberry grading into five distinct classes within an industrial environment as the fruits move along a conveyor belt. To address this, a dedicated dataset of raspberries, namely RaspGrade, was acquired and meticulously annotated. Instance segmentation experiments revealed that accurate fruit-level masks can be obtained; however, the classification of certain raspberry grades presents challenges due to color similarities and occlusion, while others are more readily distinguishable based on color.🍓 RaspGrade数据集（RaspGrade Dataset）：面向深度学习实现树莓成熟度自动分级 本研究探讨了计算机视觉（Computer Vision）在快速、精准且非侵入式食品质量评估中的应用，聚焦工业场景下传送带运输过程中树莓实时分级的全新挑战——将树莓划分为5个明确等级类别。为此，我们采集并精细标注了专属树莓数据集RaspGrade（RaspGrade Dataset）。 实例分割（Instance Segmentation）实验结果表明，可获取精准的果实级掩码；但受颜色相似性与遮挡问题影响，部分树莓等级的分类仍存在挑战，而另有部分等级可通过颜色特征轻松区分。