Color Vision System for Estimating Citrus Yield in Real-time

A machine vision system utilizing color vision was investigated as a means to identify citrus fruits and to estimate yield information of the citrus grove in real-time. Images were acquired for 98 citrus trees in a commercial grove located near Orlando, Florida. The trees were distributed over 48 plots evenly. Images were taken in stationary mode using a machine vision system consisting of a color analog camera, a DGPS receiver, and an encoder. Non-overlapping images of the citrus trees were taken by determining the field of view of the camera and using an encoder to measure the traveled distance to locate the next position for acquiring an image. The threshold of segmentation of the images to recognize citrus fruits was estimated from the pixel distribution in the HSI color plane. A computer vision algorithm to enhance and extract information from the images was developed. The total time for processing an image was 119.5 ms, excluding image acquisition time. The image processing algorithm was tested on 329 validation images and the R value between the number of fruits counted by the fruit counting algorithm and the average number of fruits counted manually was 0.79. Images belonging to a same plot were grouped together and the number of fruits estimated by the fruit counting algorithm was summed up to give the number of fruits/plot estimates. Leaving out outliers and incomplete data, the remaining 44 plots were divided into calibration and validation data sets and a model was developed for citrus yield using the calibration data set. The R value between the number of fruits/plot counted by the yield prediction model and the number of fruits/plot counted by hand harvesting for the validation data set was 0.53.