Research on Tomato Maturity Detection Based on Machine Vision

Sen, Lian; Li, Linlin; Tan, Weibin; Tan, Lixin

doi:10.1007/978-981-16-6963-7_60

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…Based on the HSV(Hue, Saturation, Value) color theory and the tomato maturity color comparison table, this paper adopts the self-defined threshold segmentation method of H-component -using the ratio of binary red pixels to tomato contour pixels to calculate the maturity level [14]. Finally, the ripening process of tomatoes can be roughly divided into three categories: tomatoes on the red surface are marked as "mature tomatoes", corresponding to the ripen period; tomatoes with a green surface are labeled as "immature tomatoes", corresponding to the unripe period; tomatoes with surface color between red and green are marked as "medium tomatoes", corresponding to half-ripe period, as shown in Figure 2.…”

Section: Data Annotation and Classificationmentioning

confidence: 99%

Multi-Growth Period Tomato Fruit Detection Using Improved Yolov5

Yang

Han

et al. 2022

Int. J. Robot. Autom. Technol.

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In agricultural mechanized production, in order to ensure the efficiency of hand-eye cooperative operation of tomato picking robot, the recognition accuracy and speed of multi-growth period tomato fruit is an important basis. Therefore, in order to improve the recognition speed of multi-growth period tomato fruit while ensuring or improving the accuracy, this paper improves the Yolov5s model by adding the architecture of the lightweight mobilenetv3 model. Firstly, the deep separable convolution is replaced in the backbone network of Yolov5s, which reduces the amount of convolution operation. Secondly, the linear bottleneck inverse residual structure is fused to obtain more features in high-dimensional space and perform convolution operation in low-dimensional space. Third, the attention mechanism is inserted into the last layer of the network to highlight features and improve accuracy. The research results show that the recognition accuracy of the improved Yolov5 model remains above 98%, the CPU recognition speed is 0.88f·s-1 faster than Yolov5s, and the GPU recognition speed is 90 frames per second faster than Yolov5s. Finally, a set of the recognition software system of multi-growth period tomato fruit is designed and developed by using RealSense D435i depth camera and PYQT. The software system further verifies the feasibility of the improved Yolov5 model, and lays a foundation for the visual software design of agricultural picking robot picking recognition.

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Section: Data Annotation and Classificationmentioning

confidence: 99%