Sugar industry is the core industry of economic crops in Guangxi. At present, the actual sugar production in Guangxi accounts for more than 60% of the national total, and it is the backbone of the national sugar industry. At present, in order to support the healthy and stable development of Guangxi's sugar industry, the state has made financial subsidies for the promotion of sugarcane varieties in the whole region. Farmers can apply for national subsidies based on the actual plot contours measured by longitude and latitude. In this paper, a semantic segmentation-based sugar planting plot recognition method is proposed to realize the automatic recognition and accurate classification of sugar planting plots by combining remote sensing images and deep learning technology. Experimental results show that the proposed method has high accuracy and robustness in the recognition of sugar planting plots.

Dawei Zhang, Zhiguang Zeng. Semantic Segmentation Method for Sugarcane Planting Fields. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 11: 59-66. https://doi.org/10.25236/AJCIS.2024.071108.

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