Abstract
Recognition and matching of litchi fruits are critical steps for litchi harvesting robots to successfully grasp litchi. However, due to the randomness of litchi growth, such as clustered growth with uncertain number of fruits and random occlusion by leaves, branches and other fruits, the recognition and matching of the fruit become a challenge. Therefore, this study firstly defined mature litchi fruit as three clustered categories. Then an approach for recognition and matching of clustered mature litchi fruit was developed based on litchi color images acquired by binocular charge-coupled device (CCD) color cameras. The approach mainly included three steps: (1) calibration of binocular color cameras and litchi image acquisition; (2) segmentation of litchi fruits using four kinds of supervised classifiers, and recognition of the pre-defined categories of clustered litchi fruit using a pixel threshold method; and (3) matching the recognized clustered fruit using a geometric center-based matching method. The experimental results showed that the proposed recognition method could be robust against the influences of varying illumination and occlusion conditions, and precisely recognize clustered litchi fruit. In the tested 432 clustered litchi fruits, the highest and lowest average recognition rates were 94.17% and 92.00% under sunny back-lighting and partial occlusion, and sunny front-lighting and non-occlusion conditions, respectively. From 50 pairs of tested images, the highest and lowest matching success rates were 97.37% and 91.96% under sunny back-lighting and non-occlusion, and sunny front-lighting and partial occlusion conditions, respectively.
Highlights
Litchi is one of the most popular fruits
Inspired by fruit recognition based on the color image segmentation methods, the machine learning methods and the fruit matching methods, this paper mainly focused on the combination of the three methods to find a robust method for recognition and matching of clustered mature litchi fruits
The fourclassifiers different to extract mature original litchi color images in Figure 11, respectively, the results were seen in Figure 12, which fruits of the original litchi color images in Figure 11, respectively, the results were seen in Figure 12, indicated the results were not ideal based on the single classifier
Summary
Litchi is one of the most popular fruits. The average annual yield of litchi in China was about1.45 million tons during the last decade [1]. Litchi is one of the most popular fruits. Multiple fruit harvesting robots have been developed by researchers [2,3,4,5,6,7]. The vision sensor is the direct source of the vision system acquiring fruit information. The construction and training of these classifiers using the features of litchi fruit and non-fruit Figure 5. The row maturesample, litchi fruits are described Weof set = ⋯ , the) images as the of training in which Figure. The row listed the images of mature fruits captured condition, thetop lower rows were leaves, branches and = 5
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