With the rapid development of China's economy and society, soybean, as the most important food and oil crops, is planted all over the country and widely distributed in a large scale. However, due to the high cost of traditional manual monitoring methods, moreover, it is inefficient to monitor the soybean planting system in an all-round way. The development of traditional agriculture and soybean industry has been greatly impacted, and the ability of soybean production and supply has been weakened rapidly, which has led to the gradual disappearance of its competitive advantage in the international market. The purpose of this paper is to use image remote sensing technology to detect and study soybean planting system. In view of the above problems and in light of the actual situation of the current development of science and technology, in this paper, image mosaic technology is used. Firstly, the image is corrected by image denoising and image interpolation, noise and distortion are eliminated effectively. Then image registration is carried out. Image registration is the core of image mosaic, which directly affects the quality of mosaic. Finally, the target image is obtained by image fusion. Experiments prove that, image remote sensing technology is helpful to monitor the growth period and planting area of soybean to a certain extent. In this paper, two image feature extraction algorithms, ORB and SURF, are used respectively. Comparing the extraction time consumption of the two algorithms for feature points, the results show that the ORB algorithm is 0.215849s and the SURF algorithm is 3.2189s. It can be seen that ORB algorithm has certain advantages in remote sensing monitoring of soybean planting.

Huayang Zhao, Changhe Li, Yuming Fu, Fenglan Huang, Danyang Wang, Genxiong Zhao, Xiaoze Yu, Guizhi Zhao, Chunyou Zhang, Yongsheng, Dandan Zhang, Chunxu Guo, Zhichao Li and Zheng Ran. Monitoring of Soybean Planting System by Image Remote Sensing Technology. Academic Journal of Agricultural Sciences (2023), Vol. 4, Issue 1: 27-41. https://doi.org/10.38007/AJAS.2023.040103.

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