Predictions of greenhouse soil moisture using artificial neural network and wireless network sensing

Document Type : Research Paper

Authors

1 PhD student, Faculty of Agricultural Engineering and Rural Civil Engineering, Agricultural Science and Natural Resources University of Khuzestan, Mollasani, Iran

2 Associate Professor, Faculty of Agricultural Engineering and Rural Civil Engineering, Agricultural Science and Natural Resources University of Khuzestan, Mollasani, Iran

3 Associate Professor, Faculty of Agricultural Engineering and Rural Civil Engineering, Agricultural Science and Natural Resources University of Khuzestan, Mollasani , Iran

4 Associate Professor, Faculty of Agricultural Engineering and Rural Civil Engineering, Agricultural Science and Natural Resources University of Khuzestan, Mollasani,, Iran

5 Associate Professor, Faculty of Agriculture,, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran

6 Assistance Professor, Faculty of Agriculture, Agricultural Science and Natural Resources University of Khuzestan, Mollasani, Iran

Abstract

Soil moisture is one of the main factors determining the better growth of plants which are widely well-received today, especially in greenhouses. Measuring the soil moisture and the environmental factors has high costs continuously and annually, in addition to being time-consuming. Therefore, one of the intelligent predictive tools that have a vast position in agricultural science is the neural network tool with the least amount of error. In this study, soil moisture and temperature percentage, light, ambient temperature, and humidity in a greenhouse located in northeastern Khuzestan were Measured and recorded during two seasons of winter and spring to control soil moisture by a moisture prediction map based on an artificial neural network. The results show an accurate forecast of soil moisture map in winter and spring between actual values that were measured and values that were predicted with the lowest standard error (1.12 and 1.71) and the highest coefficient of determination (R2) of 0.94 and 0.78, respectively, and the highest coefficient of determination were 0.87 and 0.93, respectively, by the artificial neural network in the experimental stage for winter and spring. Therefore, the remarkable accuracy in the prediction of soil moisture by this software shows its importance and high reliability in agriculture and greenhouses which makes it easier to control soil moisture and less moisture stress occurs for soil and the plant consequently.

Keywords

Main Subjects

References

Ahmad, S., Kalra, A. & Stephen, H. (2010). Estimating soil moisture using remote sensing data: a machine learning approach. Advances in Water Resource. 33 (1). 69–80.
Akbarzadeh, A., Mehrjardi, R.T., Lake, H.R. & Ramezanpour, H. (2009). Application of artificial intelligence in modeling of soil properties (Case study: Roodbar Region, North of Iran). Environmental Research Journal. 3 (2). 19–24.
Alavi, A.H., Gandomi, A.H., Mollahassani, A., Heshmati, A.A. & Rashed, A. (2010). Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks. Journal of Plant Nutrition and Soil Science. 173 (3). 368–379.
Angelopoulos, C.M., Nikoletseas, S. & Theofanopoulos, G.C. (2011). A smart system for garden watering using wireless sensor networks. In: 9th Association for Computing Machinery (ACM) International Symposium on Mobility Management and Wireless Access. 167–170.
Aqeel-ur, R., Shaikh, Z.A., Yousuf, H., Nawaz, F., Kirmani, M. & Kiran, S. (2014). Crop irrigation control using Wireless Sensor and Actuator Network (WSAN). In: International Conference on Information and Emerging Technologies (ICIET). 1–5.
Baaghideh, M., Entezari, A. & Kordi, A. (2019). Investigation of the Relationship between Soil Temperature and Climate Parameters in the Northwest of Iran (1992-2015). Journal of Geography and Regional Development Research. 16(1). 279-307. (In Farsi).
Barikloo, A., Alamdari, P., Moravej, K. & Servati, M. (2017). Prediction of Irrigated Wheat Yield by using Hybrid Algorithm Methods of Artificial Neural Networks and Genetic Algorithm. Journal of Water and Soil. 31(3). 715-726. (In Farsi).
Cai, W., Ruihua, W., Xu, L. & Ding, X. (2020). A method for modelling greenhouse temperature using gradient boost decision tree. Information Processing in Agriculture. 1-12.  
Charoenhirunyingyos, S., Honda, K., Kamthonkiat, D. & Ines, A.V.M. (2011). Soil moisture estimation from inverse modeling using multiple criteria functions. Computers and Electronics in Agriculture. 75(2). 278–287.
Cordeiro, M., Markert, C., S.Araujo, S., G.S. Campos, N., S.Gondim, R., L.Coelho da silva, T. & R. da Rocha, A. (2022). Towards smart farming: fog-enabled intelligent irrigation system using deep neural networks. Future Generation computer systems. 129. 115-124.
Dursun, M. &Özden, S. (2014). An efficient improved photovoltaic irrigation system with artificial neural network based modeling of soil moisture distribution – A case study in Turkey. Computer and Electronics in Agriculture. 102. 120- 126.
Dursun, M. & Karaman, M.R. (2009). Artificial neural network based modeling of spatial distribution of phosphorus on the tomato area. Asian Journal of Chemistry. 21 (1). 239–247.
Hafezi, N.,  Sheikhdavoodi, M. J., Sajadiye, A. M. & Khorasani Ferdavani, M. E. (2014). Neural modeling for predicting the moisture content of potato slices in a vacuum-radiant dryer. Journal of Agricultural Engineering. 39(1). 39-53. (In Farsi).
Izgi, E., Oztopal, A., Yerli, B., Kaymak, M.K. & Sahin, A.D. (2012). Short-mid-term solar power prediction by using artificial neural networks. Journal of the International Solar Energy. 86 (2). 725– 733.
Karthikeyan, L. & Mishra, A. K. (2021). Multi-layer high-resolution soil moisture estimation using machine learning over the United States. Remote Sensing of Environment. 266(112706). 1-19.
Khanali, M., Mobli, H., Ghasemi Mobtaker, H. & Sherafat, M. (2018). Modeling of Energy Consumption and Environmental Indices of Production and Processing of Tea with Regression and Artificial Neural Network Models. Journal of Agricultural Mechanization. 4(1). 15-25. (In Farsi).
Kim, Y. & Evans, R.G. (2009). Software design for wireless sensor-based site-specific irrigation. Computer and Electronics in Agriculture. 66 (2). 159–165.
Merdun, H., Çınar, Ö., Meral, R. & Apan, M. (2006). Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil and Tillage Research. 90. 108–116.
Moravejalahkami, B. & Baghshahi, M. (2020). Feasibility Study of the Construction and
Evaluation of a Soil Moisture Sensor in Different Soil Textures. Iran- Water Resources Research. 16(1). 135-145. (In Farsi).
Moreno, A., Gilabert, M.A. & Martinez, B. (2011). Mapping daily global solar irradiation over Spain: a comparative study of selected approaches. Journal of the International Solar Energy. 85(9). 2072–2084.
Ozden, S. & Dursun, M. (2011). Remote monitoring and control of PV powered drip irrigation system with soil moisture sensors. In: The Third International Conference on Computer Engineering and Technology (ICCET). 239–244.
Ramirez-Beltran, N.D., Calderon-Arteaga, C., Harmsen, E., Vasquez, R. & Gonzalez, J. (2010). An algorithm to estimate soil moisture over vegetated areas based on in situ and remote sensing information. International Journal of Remote Sensing. 31 (10). 2655–2679.
Sabziparvar, A. A., Tabari, H. & Aeini, A. (2010). Estimation of Mean Daily Soil Temperature by Means of Meteorological Data in Some Selected Climates of Iran. Journal of Science and Technology of Agriculture and Natural Resources (Water and Soil Science). 14(52). 125-137. (In Farsi).
Sanuade, O. A., Hassan, A. M., Akanji, A. O., Olaojo, A. A., Oladunjoye, M. A. & Abdulraheem, A. (2020). New empirical equation to estimate the soil moisture content based on thermal properties using machine learning techniques. Arabian Journal of Geosciences. 13-377.
Taki, M., Ajabshirchi, Y., Ranjbar, S. F., Rohani, A. & Matloobi, M. (2016). Prediction of Soil Temperature and Inside air Humidity in a SemiSolar Greenhouse Equipped with Cement North Wall by Artificial Neural Network; Case study: Tabriz city. Journal of Agricultural Mechanization. 3(1). 76-89. (In Farsi).
Taneja, P., Vasava, H. K., Daggupati, P. & Biswas, A. (2021). Multi-algorithm comparison to predict soil organic matter and soil moisture content from cell phone images. Geoderma. 385 (114863). 1-15.
Veronez, M.R., Wittmann, G., Reinhardt, A.O. & Da Silva, R.M. (2010). Surface temperature estimation using artificial neural network. In: International Society for Photogrammetry and Remote Sensing (ISPRS) TC VII Symposium. 612–617.
Wang, N., Zhang, N.Q. & Wang, M.H. (2006). Wireless sensors in agriculture and food industry – recent development and future perspective. Computer and Electronics in Agriculture. 50 (1). 1–14.
Yamaç, S. S., Şeker, C. & Negiş, H. (2020). Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semiarid area. Agricultural Water Management. 234(106121). 1-11.
Yang, C.C., Prasher, S.O. & Mehuys, G.R. (1997). An artificial neural network to estimate soil temperature. Canadian Journal of Soil Science. 77 (3). 421–429.
Zhang, J., Liu, K. & Wang, M. (2021). Downscaling groundwater storage data in China to a 1-km resolution using machine learning methods. Remote Sensing. 13(3). 1-19.
Zhao, W., Sanchez, ´ N., Lu, H. & Li, A. (2018). A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression. Journal of Hydrology. 563. 1009–1024.
Iranian Journal of Biosystems Engineering
Volume 53, Issue 4
February 2023
Pages 341-356

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History

  • Receive Date: 22 April 2022
  • Revise Date: 21 February 2023
  • Accept Date: 11 March 2023

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