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Research on Strawberry Positioning Technology including Maturity Classification

In the development of agricultural picking equipment, the identification and positioning of the target fruit is one of the key technologies. There are three sequence steps: finding the target fruit, determining whether the target fruit is mature and needs to be picked, and determining the XYZ coordinate point of the target fruit. In this study, we modified a series of convolution neural networks to detect strawberries on XY scale and judge their maturity. In order to improve the precision of small object detection, we innovatively proposed a new structure called small feature extraction layers. On a moderate GPU, small feature extraction layers improved the positioning precision from 80.53% to 89.96% and small objects precision from 41.58% to 86.14% at a 110 fps object detection speed. When using binocular ranging to measure Z-axis distance, we achieved 15 fps XYZ coordinate determination speed on a 4.5W ultra low-power CPU, and the maximum Z-axis ranging error within 60 cm was less than 2.83%.