首页|Study Results from Guangdong Academy of Science Update Understanding of Machine Learning (Accurate and Rapid Image Segmentation Method for Bayberry Automatic Pi cking Via Machine Learning)
Study Results from Guangdong Academy of Science Update Understanding of Machine Learning (Accurate and Rapid Image Segmentation Method for Bayberry Automatic Pi cking Via Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news reporting originating from Guangzhou, People's Rep ublic of China, by NewsRx correspondents, research stated, "Due to the short rip ening period and complex picking environment, bayberry generally relies on mecha nical equipment for picking, especially the automatic picking system guided by v ision. Thus, it is crucial to locate the bayberry in the view accurately and rap idly." Funders for this research include Guangdong Provincial Rural Revitalization Stra tegy Special Fund Project, GDAS'Project of Science and Technology Development. Our news editors obtained a quote from the research from the Guangdong Academy o f Science, "Although efforts have been made, the existing methods are difficult to implement due to the limited amount of data and the processing speed. In this study, an accurate and rapid segmentation method based on machine learning was proposed to address this problem. First, the images collected by the visual guid ance system were pre-processed by contrast-limited adaptive histogram equalizati on (CLAHE) based on the Y component of the YUV color space. Taking advantage of the color difference map of RB and RG for the segmentation of different colors, an adaptive color difference map foreground segmentation method was then adopted for bayberry region foreground segmentation. Finally, distance transforms and m arking control watershed methods were exploited to achieve single bayberry fruit segmentation. Furthermore, with the help of the convex hull theory and fruit sh ape characteristics, the irregular background interference areas were filtered o ut, which improved the accuracy of bayberry segmentation performance."
GuangzhouPeople's Republic of ChinaA siaCyborgsEmerging TechnologiesMachine LearningGuangdong Academy of Scie nce
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