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Developing an Intelligent Agricultural System Based on Long Short-Term Memory


Today, the agriculture industry has been developing intellectualization and automation proactively for reducing labor force and increase yields. In the past, farmers usually followed the rule of thumb to grow crops; however, due to the dramatic climate change, it becomes harder for farmers to cope with it by merely following the rule of thumb, which leads to crop damage. Therefore, it is vital to input scientific data development and technology for optimizing the environment parameters of crops and further enhance the yields. Additionally, many farms need to spread pesticides to avoid pests and diseases; yet, too much pesticide may cause soil alkalization. To enrich the growing-power of the lands, farmers will fertilize the lands; nevertheless, too much of it will also cause soil acidification, which will need to leave the land fallow to improve the soil quality. The study provides an intelligent agriculture system based on LSTM. The system develops an Internet of Things (IoT) to monitor the environmental conditions of soil, sunlight, and temperature; additionally, the research combines the information from the Central Weather Bureau for predicting the timing for watering and notifying farmers about the suggested amount of pesticides and fertilizers. The features of this article are as follows: 1. Build a clustering tree of crops according to the adaptability; 2. Calculate the critical values of each selected crop; 3. Develop an LSTM system that provides analyses according to the current soil conditions and weather forecast information; the system will reveal the conditions of the soil, and water the land to balance the condition and reach an optimal status if the soil pH is too high. The research is capable of enhancing crop yields and optimizing the land.

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  1. 1.

    Abbes AB, Magagi R, Goita K (2019) Soil moisture estimation from smap observations using long short- term memory (lstm). In: 2019 IEEE international geoscience and remote sensing symposium

  2. 2.

    An W, Wu D, Ci S, Luo H, Adamchuk V, Xu Z (2017) Agriculture cyber-physical systems. pp 399–417, Cyber-Physical Systems

  3. 3.

    Atif M, Latif S, Ahmad R, Kiani AK, Qadir J, Baig A, Ishibuchi H, Abbas W (2019) Soft computing techniques for dependable cyber-physical systems. IEEE Access 7:72030–72049

    Article  Google Scholar 

  4. 4.

    Ayaz M, Ammad-Uddin M, Sharif Z, Mansour A, Aggoune EHM (2019) Internet-of-things (iot)-based smart agriculture: Toward making the fields talk. IEEE Access 7:129551–129583

    Article  Google Scholar 

  5. 5.

    Bayrakdar ME (2019) A smart insect pest detection technique with qualified underground wireless sensor nodes for precision agriculture. IEEE Sensors J. 19:10892–10897

    Article  Google Scholar 

  6. 6.

    Chen Y, Li Y (2019) Intelligent autonomous pollination for future farming - a micro air vehicle conceptual framework with artificial intelligence and human-in-the-loop. IEEE Access 7:119706–119717

    Article  Google Scholar 

  7. 7.

    Farooq MS, Riaz S, Abid A, Abid K, Naeem MA (2019) A survey on the role of iot in agriculture for the implementation of smart farming. IEEE Access 7:156237–156271

    Article  Google Scholar 

  8. 8.

    Food of the United Nations AO (2017) The state of food and agriculture: Leveraging food systems for inclusive rural transformation.

  9. 9.

    Gevaert CM, Suomalainen J, Tang J, Kooistra L (2015) Generation of spectral–temporal response surfaces by combining multispectral satellite and hyperspectral uav imagery for precision agriculture applications. IEEE J Sel Top Appl Earth Obs Remote Sens 8:3140–3146

    Article  Google Scholar 

  10. 10.

    Giusti E, Marsili-Libelli S (2015) A fuzzy decision support system for irrigation and water conservation in agriculture. Environ Model Softw 63:73–86

    Article  Google Scholar 

  11. 11.

    Guo P, Dusadeerungsikul PO, Nof SY (2018) Agricultural cyber physical system collaboration for greenhouse stress management. Comput Electron Agric 150:439–454

    Article  Google Scholar 

  12. 12.

    Herrera D, Tosetti S, Carelli R (2016) Dynamic modeling and identification of an agriculture autonomous vehicle. IEEE Lat Am Trans 14:2631–2637

    Article  Google Scholar 

  13. 13.

    Horng GJ, Liu MX, Chen CC (2020) The smart image recognition mechanism for crop harvesting system in intelligent agriculture. IEEE Sensors J 20:2766–2781

    Article  Google Scholar 

  14. 14.

    Kang M, Wang FY (2017) From parallel plants to smart plants: intelligent control and management for plant growth. IEEE/CAA J Autom Sinica 4:161–166

    MathSciNet  Article  Google Scholar 

  15. 15.

    Kim W, Cho YB, Lee S (2017) Thermal sensor-based multiple object tracking for intelligent livestock breeding. IEEE Access 5:27453–27463

    Article  Google Scholar 

  16. 16.

    Liu R, Zhang Y, Ge Y, Hu W, Sha B (2020) Precision regulation model of water and fertilizer for alfalfa based on agriculture cyber-physical system. IEEE Access 8:38501–38516

    Article  Google Scholar 

  17. 17.

    Lozoya C, Aguilar A, Mendoza C (2016) Service oriented design approach for a precision agriculture datalogger. IEEE Lat Am Trans 14:1683–1688

    Article  Google Scholar 

  18. 18.

    Shadrin D, Menshchikov A, Ermilov D, Somov A (2019) Designing future precision agriculture: Detection of seeds germination using artificial intelligence on a low-power embedded system. IEEE Sensors J 19:11573–11582

    Article  Google Scholar 

  19. 19.

    Shadrin D, Menshchikov A, Somov A, Bornemann G, Hauslage J, Fedorov M (2019) Enabling precision agriculture through embedded sensing with artificial intelligence. IEEE Trans Instrum Meas 69:4103–4113

    Article  Google Scholar 

  20. 20.

    Taylor GA, Torres HB, Ruiz F, Marín MN, Chaves DM, Arboleda LT, Parra C, Carrillo H, Mouazen AM (2019) ph measurement iot system for precision agriculture applications. IEEE Lat Am Trans 17:823–832

    Article  Google Scholar 

  21. 21.

    Touati F, Al-Hitmi M, Benhmed K, Tabish R (2013) A fuzzy logic based irrigation system enhanced with wireless data logging applied to the state of qatar. Comput Electron Agric 98:233– 241

    Article  Google Scholar 

  22. 22.

    Trappey AJC, Trappey CV, Govindarajan UH, Sun JJ, Chuang AC (2016) A review of technology standards and patent portfolios for enabling cyber-physical systems in advanced manufacturing. IEEE Access 4:7356–7382

    Article  Google Scholar 

  23. 23.

    Wu HT, Zhan JW, Tseng FH (2020) Developing an intelligent agricultural system based on long short-term memory

  24. 24.

    Zhang L, Gui G, Khattak AM, Wang M, Gao W, Jia J (2019) Multi-task cascaded convolutional networks based intelligent fruit detection for designing automated robot. IEEE Access 7:56028–56038

    Article  Google Scholar 

  25. 25.

    Zhang Q, Wang H, Dong J, Zhong G, Sun X (2017) Prediction of sea surface temperature using long short-term memory. IEEE Geosci Remote Sens Lett 14:1745–1749

    Article  Google Scholar 

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Correspondence to Hsin-Te Wu.

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Wu, HT. Developing an Intelligent Agricultural System Based on Long Short-Term Memory. Mobile Netw Appl 26, 1397–1406 (2021).

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  • Long short-term memory
  • Internet of things
  • Intelligent agricultural