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Citrus Yield Mapping System Using Machine Vision

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Paper number  031002,  2003 ASAE Annual Meeting . (doi: 10.13031/2013.13701) @2003
Authors:   Palaniappan Annamalai, Won Suk Lee
Keywords:   Citrus, Color, Yield mapping, Precision agriculture, Image processing

This research was conducted to develop an image processing algorithm to identify and count the number of citrus fruits in an image. Once this algorithm is completed it will be incorporated into a machine vision system consisting of a GPS receiver and distance measuring devices in a pick-up truck to estimate yield of a citrus grove on-the-go. A total of 90 images were acquired in an experimental citrus grove. Images of the citrus grove were analyzed and histogram & pixel distribution of various classes (citrus, leaf, and background) were developed. The threshold of segmentation of the images to recognize citrus fruits was estimated from the pixel distribution of hue and saturation color plane. A computer vision algorithm was developed to enhance and extract information from the images. Preprocessing steps for removing noise and identifying properly the number of citrus fruits were carried out using a combination of erosion and dilation. Finally the number of fruits was counted using blob analysis. The total time for processing an image was 283 ms excluding image acquisition time. The algorithm was tested on 59 validation images and the R2 value between the number of fruits counted by the machine vision algorithm and the average number of fruits by manual counting was 0.76.

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