Design and implementation of smart agricultural soil acquisition system based on FPGA

He Bing; Xin Wang; Ruidong Chen; Maocai Yang

doi:10.1117/12.2659996

3 February 2023 Design and implementation of smart agricultural soil acquisition system based on FPGA

He Bing, Xin Wang, Ruidong Chen, Maocai Yang

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 1251131 (2023) https://doi.org/10.1117/12.2659996
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

With the continuous development of economy, China, as the largest agricultural country in the world, has a low level of agricultural automation and a large demand for labor. Therefore, it is necessary to improve production efficiency and improve the environment by developing smart agriculture, so as to realize the dual benefits of agriculture and environment. The traditional CPU and single chip microcomputer have weak running speed and timing implementation ability. In this paper, a design and implementation scheme of intelligent agricultural soil acquisition system based on FPGA is proposed by investigating the research status and development trend of a large number of domestic and foreign intelligent orchards. It has the advantages of high integration, fast speed, strong reliability and low power consumption. This paper proposes the overall design scheme of the system. The hardware part includes the selection of various devices, and the software part includes the language, software development and simulation. It mainly includes soil information collection, data transmission, data storage, data processing and other aspects, which simplifies the design of the circuit and conforms to the development trend of modern intelligent orchard.

Citation Download Citation

He Bing, Xin Wang, Ruidong Chen, and Maocai Yang "Design and implementation of smart agricultural soil acquisition system based on FPGA", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 1251131 (3 February 2023); https://doi.org/10.1117/12.2659996

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