Clustered tomato detection for automatic robotic harvesting

In unstructured agricultural environments, accurate fruit detection and picking point localization are important for automatic robotic harvesting. In order to solve the machine vision problem of selective mechanized picking of clustered fruit, this study proposed a novel vision algorithm for target recognition and picking point localization based on the shape and growth characteristics of clustered tomatoes.

The picking point has to be determined according to its position and posture relative to the fruit or fruit cluster. The shape, texture, and color features of tomatoes were extracted and combined to realize accurate tomato recognition. The precision, recall, and accuracy of the recognition model were as high as 100%, and the recognition time was less than 1 s. After the recognition of the tomato fruit area, this study further developed a two-step picking point positioning algorithm. Firstly, the coordinates and radius of the fruit center of mass were acquired, and then the contour line of the whole bunch of fruits was fitted based on Hough circle detection.

Secondly, the spatially symmetrical spline interpolation method and geometric analysis were applied for peduncle estimation, contour fitting, and picking point location. Experimental results showed that located picking points were distributed on the branches, and the position deviation was small, which met the requirements within a certain positioning accuracy range. This indicated that the proposed method could achieve a satisfactory picking-point location effect of clustered fruit in complex environments.

Read the complete research at www.researchgate.net.

Yuhao, Bai & Mao, Shihan & Zhou, Jun & Zhang, Baohua. (2022). Clustered tomato detection and picking point location using machine learning-aided image analysis for automatic robotic harvesting. Precision Agriculture. 1-17. 10.1007/s11119-022-09972-6. 


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