Research on Persimmon Fruit Diameter Accurate Detection Method Based on Improved RCNN Instance Segmentation Algorithm

Yuan Fang^1,2Yangyang Liu^3*Ya Feng⁴Yougen Chen¹HaiKun Jiang^2*

• ¹School of Horticulture college, Anhui Agricultural University, Hefei, China
• ²Institute of Vegetables, Anhui Academy of Agricultural Sciences, Hefei, China
• ³School of Mechanical Engineering, Anhui University of Technology, Ma’anshan,, China
• ⁴College of Information Engineering, Shaoxing Vocational & Technical College, Shaoxing, China

Aiming at the problem of inaccurate fruit recognition and fruit diameter detection in persimmon inspection process, this research proposes a novel persimmon accurate recognition and fruit diameter detection algorithm based on the Region-based Convolutional Neural Network (RCNN) Mask and instance segmentation algorithm. The algorithm strategically targets the object of interest by integrating cropping, morphological processing, and concave point segmentation modules into the fully connected layer following the Region of Interest (RoI) feature. Initially, the algorithm separates the front and back background of the cropped target object using morphological processing to obtain a binarized image. Subsequently, concave point segmentation is applied to address sticking issues arising from overlapping or occlusion between fruits, while a template matching algorithm helps in image recognition. The improved instance segmentation algorithm enhances the segmentation accuracy of the target fruit and reduces the relative error in the fruit diameter measurement caused by sticking problems during occlusion and overlap. Notably, compared with the original algorithm, the improved Mask RCNN instance segmentation algorithm achieves a mean Average Precision (mAP) of 94.25%, representing an improvement of 8.05%, with the Mean Intersection-over-Union (MIoU) value increases by 18.5%. The maximum relative error in fruit diameter measurement is reduced to 1.3%, while the maximum relative error in fruit thickness measurement is 1.98%, meeting the stringent requirements of orchard inspection. Overall, the proposed method enhances the precision and accuracy of fruit diameter detection, offering valuable theoretical and technical insights for intelligent inspection, yield estimation, fruit detection, and mechanized picking in the agricultural domain.