Shape Analysis of Agricultural Products: A Review of Recent Research Advances and Potential Application to Computer Vision

The appearance of agricultural products deeply conditions their marketing. Appearance is normally evaluated by considering size, shape, form, colour, freshness condition and finally the absence of visual defects. Among these features, the shape plays a crucial role. Description of agricultural product shape is often necessary in research fields for a range of different purposes, including the investigation of shape traits heritability for cultivar descriptions, plant variety or cultivar patents and evaluation of consumer decision performance. This review reports the main applications of shape analysis on agricultural products such as relationships between shape and: (1) genetic; (2) conformity and condition ratios; (3) products characterization; (4) product sorting and finally, (5) clone selection. Shape can be a protagonist of evaluation criteria only if an appreciable level of image shape processing and automation and data are treated with solid multivariate statistic. In this context, image-processing algorithms have been increasingly developed in the last decade in order to objectively measure the external features of agricultural products. Grading and sorting of agricultural products using machine vision in conjunction with pattern recognition techniques offers many advantages over the conventional optical or mechanical sorting devices. With this aims, we propose a new automated shape processing system which could be useful for both scientific and industrial purposes, forming the bases of a common language for the scientific community. We applied such a processing scheme to morphologically discriminate nuts fruit of different species. Operative Matlab codes for shape analysis are reported.