The recognition of apple fruits in plastic bags based on block classification

• Published in: Precision agriculture Vol. 19; no. 4; pp. 735 - 749
• Main Authors: Liu, Xiaoyang, Jia, Weikuan, Ruan, Chengzhi, Zhao, Dean, Gu, Yuwan, Chen, Wei
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2018; Springer Nature B.V
• Subjects: Agriculture; Algorithms; apples; Atmospheric Sciences; Biomedical and Life Sciences; Chemistry and Earth Sciences; Classification; color; Computer Science; Edge detection; Feature extraction; Fruits; Image classification; Image detection; Image processing; Image segmentation; leaves; Life Sciences; Object recognition; Physics; Pixels; plastic bags; Plastics; Remote Sensing/Photogrammetry; Soil Science & Conservation; Statistics for Engineering; support vector machines; texture; Texture recognition; Watersheds; Image recognition; Block classification; SVM; Edge detection; Watershed
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-017-9553-2

The recognition of apple fruits in plastic bags is easy to be affected by reflected and refracted light. In order to weaken the influence of light, a method based on block classification is proposed. The method adopts watershed algorithm to segment original images into irregular blocks based on edge detection results of R – G grayscale images firstly. Compared with the watershed algorithm based on gradient images, the segmentation method can preserve fruits edge and reduce the number of blocks by 20.31%, because graying image method, R – G , filters most of leaves and edge detection operator insures that the edge of fruits are detected accurately. Next, these blocks are classified into fruit blocks and non-fruit blocks by support vector machine on the basis of the color and texture features extracted from blocks. Compared with the image recognition method based on pixel classification, the proposed method can restrain the interference of light caused by plastic bags effectively. The false negative rate ( FNR ) and false positive rate ( FPR ) of the method based on pixel classification are 21.71 and 14.53% respectively. The FNR and FPR of the proposed method are 4.65 and 3.50% respectively.