Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform

• Published in: Precision agriculture 2014-02, Vol.15 (1), p.80-94
• Main Authors: Cubero, S, Aleixos, N, Albert, F, Torregrosa, A, Ortiz, C, García-Navarrete, O, Blasco, J
• Format: Article
• Language: English
• Subjects: Agricultural equipment; Agricultural technology; Agriculture; Algorithms; Analysis; Atmospheric Sciences; Automation; Biomedical and Life Sciences; Chemistry and Earth Sciences; Citrus; Citrus fruits; color; Computer Science; Computer vision; Cost control; Farm machinery; Fruits; Global positioning systems; GPS; Greenhouses; Harvest; Harvesting; Inspection; Laboratories; Life Sciences; lighting; marketing; packing houses; Physics; Precision farming; prediction; Production costs; raw fruit; Remote Sensing/Photogrammetry; Robotics; robots; Soil Science & Conservation; sorting; Statistics for Engineering; Studies; Vegetables; Vision systems
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-013-9324-7

The mechanisation and automation of citrus harvesting is considered to be one of the best options to reduce production costs. Computer vision technology has been shown to be a useful tool for fresh fruit and vegetable inspection, and is currently used in post-harvest fruit and vegetable automated grading systems in packing houses. Although computer vision technology has been used in some harvesting robots, it is not commonly utilised in fruit grading during harvesting due to the difficulties involved in adapting it to field conditions. Carrying out fruit inspection before arrival at the packing lines could offer many advantages, such as having an accurate fruit assessment in order to decide among different fruit treatments or savings in the cost of transport and marketing non-commercial fruit. This work presents a computer vision system, mounted on a mobile platform where workers place the harvested fruits, that was specially designed for sorting fruit in the field. Due to the specific field conditions, an efficient and robust lighting system, very low-power image acquisition and processing hardware, and a reduced inspection chamber had to be developed. The equipment is capable of analysing fruit colour and size at a speed of eight fruits per second. The algorithms developed achieved prediction accuracy with an R² coefficient of 0.993 for size estimation and an R² coefficient of 0.918 for the colour index.