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 encoder was calibrated in the grove

before the field-testing of the system.

 Images of the citrus trees were analyzed and a histogram and pixel distribution of

various classes (citrus fruit, leaf, and background) were developed in RGB and HSI color

space. The threshold of segmentation of the images to recognize citrus fruits was

estimated from the pixel distribution of the HSI color plane. A computer vision algorithm

to enhance and extract information from the images was developed. Preprocessing steps

for removing noise and identifying properly the number of citrus fruits were carried out

using a threshold and a combination of erosion and dilation. 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 R2 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 the 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 R2 value between the number of fruits/plot

counted by the yield prediction model and the number of fruits/plot identified by hand

harvesting for the validation data set was 0.46. Although this study was conducted for

citrus fruits, the concept could be easily applied with little modifications to estimate yield

of most fruits that differ in color from the foliage.