Skip to main content

Advertisement

Log in

Development of vegetable intelligent farming device based on mobile APP

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Poor realization of agronomic standards, low level of automation, time-consuming and labor-intensive farming are the main problems in traditional production process of vegetable. In order to improve vegetable cultivation intelligent and intensive level,reduce waste of production resources, an intelligent vegetable cultivation device was designed based on APP control, internet communications and image recognition technology, with the functions of remote control, precision sowing, quantitative dosing of liquid materials and weed recognition. The device mainly includes farming executing part, image processing part, STM32 microcontroller, and the APP which sends a command and control the device to work on the corresponding farming work. The precision seeding sowing worked through the gantry positioning and the cooperation of magnetic coupling tools and gas-suction sowing tools. By controlling the pump running time and monitoring flow volume with PVDF pressure sensor installed in the liquid pipeline interface, the liquid material was delivered quantitatively. Weed images were collected by CCD camera and recognition algorithm was developed based on BP neural network to obtain the weed location information. The test results show that the error rate of sowing was 2.75%, the passing rate of the plant spacing was up to 97.2%, no miss sowing occurs during the test; the error of the liquid delivery was within ± 5.8 g; true positive rate, true negative rate and accuracy of weed recognition were above 95%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Nanhong, M., Ying, Z., Guangming, Q., Yue, J.: Vegetables full-mechanization research present situation and the countermeasures. J. Chin. Agric. Mech. 35(3), 66–69 (2014)

    Google Scholar 

  2. Xiao, T. Study on vegetable Mechanlzed production technology system and influencing factors. Doctoral thesis, Nanjing Agricultural University (2016)

  3. Han, Y., Hongru, X., Zhiyu, S., et al.: Research on mechanization technology mode of tea plantation and management. J. Agric. Sci. Technol. 18(03), 74–81 (2016)

    Google Scholar 

  4. Pan, X. Research on the development status and problems of vegetable industry in Qingzhou. Doctoral thesis, Northwest A&F University. (2014)

  5. Yang, S. Studier on the Development of Vegetable Industry on China. Doctoral thesis, Huazhong Agricultural University (2014)

  6. Song, G., Yanli, Y., Yuefeng, Z., Xiaojun, Q.: Development status of automated equipment systems for greenhouse potted flowers production in Netherlands. Trans. Chin. Soc. Agric. Eng. 28(19), 1–8 (2012). (in Chinese with English abstract)

    Google Scholar 

  7. Gao, F., Yu, L., Zhang, WA., et al.: Research and design of crop water status monitoring system based on wireless sensor networks. Trans. CSAE 25(2), 107–112 (2009). (in Chinese with English abstract)

    Google Scholar 

  8. Yang, S., Haoran, B., Liuzhu, C., Fangyan, W., Xiu, L.: Design of the plant factory supervision system based on internet of things. J. Agric. Mech. Res. 40(02), 197–201 (2018)

    Google Scholar 

  9. Xiong, L., Li, Y., Deng, X., Zhou, H.: APP construction based on database technology for the traceability of tea quality. J. Food Saf. Qual. 7(6), 2555–2559 (2016)

    Google Scholar 

  10. Qiu, F., Liu, B., Yang, G., Fu, K., Wang, L.: Planting management APPs based on mobile intelligent terminal. Comput. Mod. 256(12), 107–110 (2016)

    Google Scholar 

  11. Han, X. The organization modes and operating mechanism of internet plus agriculture in China. Doctoral thesis, China Agricultural University (2017)

  12. Man, D. Greenhouse monitoring system based on STM32 microcontroller development. Doctoral thesis, North China University of Water Resources and Electric Power (2016)

  13. Liu, Y., Tingting, L., Xinrong, C., Jianping, L., Yuyao, S.: Universal autopilot system of tractor based on Raspberry Pi. Trans. Chin. Soc. Agric. Eng. 31(21), 109–115 (2015). (in Chinese with English abstract)

    Google Scholar 

  14. Ambrož, M.: Raspberry Pi as a low-cost data acquisition system for human powered vehicles. Measurement 100, 7–18 (2017)

    Article  Google Scholar 

  15. Vujović, V., Maksimović, M.: Raspberry Pi as a sensor web node for home automation. Comput. Electr. Eng. 44, 153–171 (2015)

    Article  Google Scholar 

  16. Zhang, G. Study on the auto-focusing technology of digital image and realization for an auto-focusing system. Doctoral thesis, Xidian University (2007)

  17. Zhang, X. Intelligent data transmission and field monitoring application based on agricultural internet of things. Doctoral thesis, Donghua University (2016)

  18. Xienan, R. Study on optimization of BP neural network based on genetic algorithm and MATLAB simulation. Doctoral thesis, Tianjin Normal University (2014)

  19. Wei, Q. Research of pattern recognition based on the local structure topological relationship modeling. Doctoral thesis, Harbin Institute of Technology (2011)

  20. Wang, D. Design of smart home system based on internet of things. Doctoral thesis, Shenyang University of Technology (2017)

  21. Xu, W. Design of intelligent home monitoring system based on WIFI and ANDROID. Doctoral thesis, Southwest Jiaotong University (2017)

Download references

Acknowledgements

The work was sponsored by the National Key Research and Development Program of China Sub-project (Nos. 2017YFD0700800 and 2016YFD0700103), the Innovation Scientists and Technicians Troop Construction Projects of Henan Province (No. 184200510017).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ji Jiangtao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xin, J., Mingyong, L., Kaixuan, Z. et al. Development of vegetable intelligent farming device based on mobile APP. Cluster Comput 22 (Suppl 4), 8847–8857 (2019). https://doi.org/10.1007/s10586-018-1979-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1979-4

Keywords

Navigation