Outdoor color rating of sweet cherries using computer vision

Authors:
Qi Wang;Hui Wang;Lijuan Xie;Qin Zhang
Affiliations:
Center for Precision & Automated Agricultural Systems, Washington State University, 24106 N. Bunn Rd., Prosser, WA 99350, USA;Center for Precision & Automated Agricultural Systems, Washington State University, 24106 N. Bunn Rd., Prosser, WA 99350, USA and Chinese Academy of Agricultural Mechanization Sciences, Beijing 10 ...;Center for Precision & Automated Agricultural Systems, Washington State University, 24106 N. Bunn Rd., Prosser, WA 99350, USA and College of Biosystems Engineering and Food Science, Zhejiang Unive ...;Center for Precision & Automated Agricultural Systems, Washington State University, 24106 N. Bunn Rd., Prosser, WA 99350, USA
Venue:
Computers and Electronics in Agriculture
Year:
2012

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Abstract

In this paper, we report the results of an exploration study of the feasibility of using computer vision to conduct accurate color rating of sweet cherry in outdoor orchard environments. Pre-harvest color rating of cherry is important to growers in determining the optimal harvest time. Currently, the in-field rating relies heavily on the manual comparison between the color of cherry fruits and standard color charts, which is both labor intensive and subjective. It is not uncommon to have one or two grades of deviation. A computer vision-based color rating system was developed in an attempt to provide an automatic and objective way to achieve more consistent and accurate color ratings. This system successfully used a camera flash to reduce the effects of two major obstacles in outdoor color rating: (1) inconsistent ambient light; and (2) glaring reflections on cherry skin. To mimic the manual color rating practice that is widely accepted by sweet cherry growers today, a task-oriented image processing algorithm was developed to remove the glaring reflections and to classify the color of cherries into seven levels. Field tests showed that the overall accuracy of the rating exceeded 85% based on 660 samples from three field tests under natural, outdoor lighting conditions. The tests validated the feasibility of using a computer vision system to achieve accurate and objective color ratings of sweet cherry under outdoor natural light conditions for actual in-orchard use.