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Abstract
Feature extraction of hyperspectral images for detecting immature green citrus fruit
Highlights
Efficient detection and estimation of the number of fruit within tree canopies in natural environments is one of the main applications of computer vision in agriculture
This study showed that hyperspectral imaging technique has the potential for detecting immature green citrus and constructing an early estimation yield system based on selected multispectral bands
In view of the above difficulties, this paper aimed to propose a new detection method for green citrus fruit
Summary
Efficient detection and estimation of the number of fruit within tree canopies in natural environments is one of the main applications of computer vision in agriculture. Bulanon et al.[1,2] applied multispectral imaging using six optical bands of 550, 600, 650, 700, 750, and 800 nm to enhance citrus fruit detection in the field under natural daylight condition. The Otsu’s thresholding technique, which is used to select the most suitable gray scale as the threshold value, can utilize a discriminant analysis to find the maximum separability of green vegetation and background based on the histogram of the NDVI image. The valley at level 0.55 between the second and the third peaks could be chosen as a segmentation threshold to remove the background It is demonstrated from the experimental results that the different images have different segmentation thresholds ranging from 0.5–0.8 because of the varying illumination conditions.
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