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Picking Point Recognition for Ripe Tomatoes Using Semantic Segmentation and Morphological Processing by Qianjie Rong; Chunhua Hu; Xiangdong Hu; Mingxi Xu is a Agricultural and Biological Sciences article available to read on EtoBox.

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Identifying picking points is the key to the intelligent picking of ripe tomatoes and an important guarantee of efficient and lossless picking. A semantic segmentation model with the improved Swin Transformer V2 and a picking point recognition algorithm based on the connection of tomato fruit, calyx and stem are proposed for the problem of picking point recognition of ripe tomatoes in complex environments. Firstly, the tomato fruit, calyx and stem are accurately identified by an improved semantic segmentation model. On the one hand, the SeMask module is integrated into the traditional Swin Transformer V2 architecture for incorporating semantic information of the image into the encoder to improve the segmentation performance. On the other hand, UPerNet is used as the decoder of the model to improve the prediction results by integrating the feature selection module and feature alignment module into the decoder. Secondly, the calyx regions and stem regions of ripe tomatoes are extracted by a connected component labeling algorithm and morphological processing. Finally, the picking points located on the tomato lateral stems are extracted by the image refinement algorithm, deburring algo

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Author

Qianjie Rong; Chunhua Hu; Xiangdong Hu; Mingxi Xu

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Agricultural and Biological Sciences (Physical Sciences)