Abstract
In the vision system of apple-picking robots, the main challenge is to rapidly and accurately identify the apple targets with varying halation and shadows on their surfaces. To solve this problem, this study proposes a novel, multi-feature, patch-based apple image segmentation technique using the gray-centered red-green-blue (RGB) color space. The developed method presents a multi-feature selection process, which eliminates the effect of halation and shadows in apple images. By exploring all the features of the image, including halation and shadows, in the gray-centered RGB color space, the proposed algorithm, which is a generalization of K-means clustering algorithm, provides an efficient target segmentation result. The proposed method is tested on 240 apple images. It offered an average accuracy rate of 98.79%, a recall rate of 99.91%, an F1 measure of 99.35%, a false positive rate of 0.04%, and a false negative rate of 1.18%. Compared with the classical segmentation methods and conventional clustering algorithms, as well as the popular deep-learning segmentation algorithms, the proposed method can perform with high efficiency and accuracy to guide robotic harvesting.
Highlights
With agricultural production developing toward large-scale, intensive, and precise processes to realize intelligence, demand for intelligent automation of agricultural equipment has been increasing rapidly [1,2,3]
We propose a model-based segmentation algorithm for specific images of ripe apples by studying the color features and local variations of the apple images and constructing features from the essence of the segmentation target; this approach makes the segmentation of apple images easy to explain and understand [53]
To address the problem of low segmentation accuracy of apple images because of the non-uniform illumination in the natural environment of unstructured orchards, a new segmentation method was established on the basis of the characteristics of the apple images
Summary
With agricultural production developing toward large-scale, intensive, and precise processes to realize intelligence, demand for intelligent automation of agricultural equipment has been increasing rapidly [1,2,3]. One of the major fruits in the world, are still picked manually owing to the complex environment of the orchards. The main function of the vision system is to accurately identify the target fruit and provide information for motion control [7,8]. Non-uniform halation and shadows are special kind of noise in the images acquired by the vision system, and they cause the loss of information regarding the location of apples in the images, thereby increasing the difficulty of recognition and segmentation [11]. How to effectively and accurately remove or weaken the effect of halation and shadows is one of the key issues of the vision system of agricultural harvesting robots; this topic has received extensive research attention [12]
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