Soluble solids content (SSC) is an important index of apple internal quality. To invent a more flexible and efficient method of apple internal quality detection and classification, a robot system for the autodetection and classification of apple internal quality attributes was developed. Visible and near infrared (Vis/NIR) spectroscopy is a promising technology for the nondestructive detection of the internal quality attributes of apples. The end effector of the robot system mainly carried the Vis/NIR spectra collection module and gripping mechanism. The Vis/NIR spectrum was collected when the end effector gripped the apple. Single shot multibox detector (SSD) target detection algorithm was applied to process the images and calculate the position of the apple, which greatly reduced the low accuracy of apple identification caused by light intensity and complex backgrounds, and the speed was approximately 0.055 s per frame. In comparing different modeling results, the normalized spectral ratio (NSR) pretreatment combined with the competitive adaptive reweighted sampling algorithm (CARS) obtained the best modeling result, with R_c and R_cv values of 0.979 and 0.969 and RMSEC and RMSECV values of 0.335 % and 0.385 %, respectively. The classification accuracy of independent validation was 90.0 % with R_p and RMSEP values of 0.952 and 0.393 %. The robot system required approximately 5.200 s to complete a classification for each sample. The results showed feasibility of the robot system to detect the internal quality attributes of apples.

可溶性固形物含量（SSC）是苹果内部品质的重要指标。为发明一种更灵活高效的苹果内部品质检测与分类方法，开发了苹果内部品质属性自动检测与分类机器人系统。可见光和近红外 (Vis/NIR) 光谱是一种用于无损检测苹果内部质量属性的有前途的技术。机器人系统的末端执行器主要承载Vis/NIR光谱采集模块和夹持机构。当末端执行器抓住苹果时收集 Vis/NIR 光谱。应用单发多盒检测器（SSD）目标检测算法对图像进行处理并计算苹果的位置，大大降低了由于光照强度和复杂背景导致的苹果识别准确率低的问题，速度约为每帧0.055 s。在比较不同建模结果时，归一化谱比（NSR）预处理结合竞争性自适应重加权采样算法（CARS）获得了最好的建模结果，R _c和R _cv值分别为 0.979 和 0.969，RMSEC和RMSECV值分别为 0.335 % 和 0.385 %。独立验证的分类准确率为 90.0%，R _p和RMSEP值为 0.952 和 0.393%。机器人系统需要大约 5.200 秒来完成每个样本的分类。结果表明，机器人系统在检测苹果内部品质属性方面具有可行性。