Green Citrus Fruit Research Articles

Machine Vision for Green Citrus Detection in Tree Images

The overall goal of this study was to develop a low-cost machine vision system for detecting green fruit on citrus trees. It is difficult to distinguish green citrus fruit from the background leaves using color information because the leaves and fruits are similar in color. Therefore, spatial characteristics such as shape and texture were analyzed. After background extraction, edge detection was performed. Then, circle-template matching was applied to the edge image to detect green fruits. The detection tests revealed that 86% of the fruit were correctly detected.

Green citrus detection using hyperspectral imaging

The goal of this study was to develop an image processing method to detect green citrus fruit in individual trees. This technology can be applied for crop yield estimation at a much earlier stage of growth, providing many benefits to citrus growers. A hyperspectral camera of 369–1042 nm was employed to acquire hyperspectral images of green fruits of three different citrus varieties (Tangelo, Valencia, and Hamlin). First, a pixel discrimination function was generated based upon a linear discriminant analysis and applied to all pixels in a hyperspectral image for image segmentation of fruit and other objects. Then, spatial image processing steps (noise reduction filtering, labeling, and area thresholding) were applied to the segmented image, and green citrus fruits were detected. The results of pixel identification tests showed that detection success rates were 70–85%, depending on citrus varieties. The fruit detection tests revealed that 80–89% of the fruit in the foreground of the validation set were identified correctly, though many occluded or highly contrasted fruits were identified incorrectly.