Real-time monitoring of water requirement in protected farms by using polynomial neural networks and image processing
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The monitoring of water requirement in irrigation areas is mostly performed by on-farm methods like utilization of soil probes, tensiometers, or neutron probes. The probes are placed into the soil collected from different depths of the root zone of the crop. But such procedures are found to be time-consuming. As a result, non-portable capacitance-based probes were nowadays utilized for monitoring of soil moisture. However, the sensor-based non-portable system is expensive and out of reach of ordinary farmers. But an absence of on-time monitoring of soil moisture in the root zone of the soil often results in crop failure and incurs a substantial loss on the cultivators. In the present investigation, a real-time inexpensive water monitoring system was proposed to monitor soil moisture in the root zone of a crop such that both time and expenditure can be reduced. The present study is an attempt to develop a real-time monitoring process for crop water requirement (CWR) in protected farm irrigation systems as a function of the significant parameters such as soil porosity (SP), water availability, crop biomass equivalent (CBE), frequency of nutrient application, frequency of irrigation, and CWR. A systematic literature review was performed to identify parameters for CWR, which were then selected by a relevant group of experts on the field. A two-step methodology was followed to develop a function that can automatically estimate water requirement in the root zone of the crop. In the first step, a new probability optimization technique (POT) was proposed for the identification of the priority value of the selected parameters to generate an ideal scenario. In the second step, the index, developed from the parameters and respective priorities selected in the first step, was predicted recurring to polynomial neural network models. The implementation of the nonlinear transfer function in the development of the neural network framework ensures generation of a platform-independent model, which can be embedded to monitor watering requirement for crops cultivated in a protected farm concept. The data of SP and CBE were retrieved from two separate indices (index of soil porosity and biomass index) calculated from images captured from the root and surface areas of the crops. Here, the POT method was used followed by the z score of priority function of the selected parameters estimated by polynomial networks and was fed for the calculation of the water requirement index (WRI). The normalized relative difference of the WRI of two consecutive days provides the information about the necessity of watering and accordingly, the crops in the system are irrigated. The results from the decision-making method indicated that the most significant parameter among the compared factors is CWR. The peak pixel value of each column of the image, for retrieving information from captured images and to identify soil porosity and biomass, was found to be the most contributing factor. The polynomial neural network (PNN) model trained with the information from POT method was found to be the best predictive variant among all the considered configuration of the model having a mean absolute accuracy of 99.08% during the testing phase of the PNN model. This real-time system, when implemented in a real-life scenario, can conserve both water and energy expended in running the watering networks of protected farms.
KeywordsAutomatic irrigation system Crop yield optimization High-tech irrigation system Multi-story protected farms
- Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998a). Crop evapotranspiration-guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9), D05109.Google Scholar
- Allen, Richard G., Pereira, Luis S., Raes, Dirk, & Smith, Martin. (1998b). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9), D05109.Google Scholar
- Anonymous. (2013a). Renewable energy resources. 63 Southshire Drive, Bennington, United States. http://www.rer-biomass.com/grass-biomass/what-is-crop-biomass/. Accessed November 20, 2016.
- Anonymous (2013b). What is a CCD? Overview. Spectral Instruments, Inc, 420 N. Bonita Ave. Tucson, AZ 85745 USA. http://www.specinst.com/What_Is_A_CCD.html. Accessed November 20, 2016.
- Barron, R. L., Cellucci, R. L., Jordan, P. R., Beam, N. E., Hess, P., & Barron, A. R. (1990). Applications of polynomial neural networks to FDIE and reconfigurable flight control. In Proceedings of the IEEE national on aerospace and electronics conference, 1990. NAECON 1990 (pp. 507–519). IEEE.Google Scholar
- Brouwer, C., &Heibloem, M. (1986). Irrigation water management: Irrigation water needs. Training Manual, 3. FAO. http://www.fao.org/docrep/s2022e/s2022e07.htm Accessed November 20, 2016.
- David P. K. (2014). Different soils and how they help plants grow, ecosystem restoration, analytical methods, tech guide, physical properties: porosity, Montana State University Bozeman. http://ecorestoration.montana.edu/mineland/guide/analytical/physical/porosity.htm. Accessed November 20, 2016.
- Dursun, M., & Ozden, S. (2011). A wireless application of drip irrigation automation supported by soil moisture sensors. Scientific Research and Essays, 6(7), 1573–1582.Google Scholar
- Garel, E. (2017).Collection and dissemination of data from environmental monitoring systems in estuaries. In Experiment@ International Conference (exp. at’17) (4th ed., pp. 61–64). IEEE.Google Scholar
- Ghazali, R., Hussain, A., & El-Deredy, W. (2006). Application of ridge polynomial neural networks to financial time series prediction. In International joint conference on neural networks, 2006, IJCNN’06 (pp. 913–920). IEEE.Google Scholar
- Ghazali, R., Hussain, A. J., & Salleh, M. M. (2008). Application of polynomial neural networks to exchange rate forecasting. In 8th international conference on intelligent systems design and applications, 2008 ISDA’08 (Vol. 2, pp. 90–95). IEEE.Google Scholar
- Hanks, R. J. (1983). Yield and water-use relationships: An overview. In Limitations to efficient water use in crop production, limitationstoef (pp. 393–411).Google Scholar
- Hochmuth, G. J. (1992). Fertilizer management for drip-irrigated vegetables in Florida. HortTechnology, 2(1), 27–32.Google Scholar
- MAIB. (2015). Frequency of irrigation, water management including micro irrigation, Agriculture Information Bank (MAIB). http://agriinfo.in/default.aspx?page=topic&superid=1&topicid=29.
- MDA. (2017). Irrigation management, conservation practices. Minnesota Conservation Funding Guide, Minnesota Department of Agriculture (MDA), Saint Paul, USA. http://www.mda.state.mn.us/protecting/conservation/practices/irrigation.aspx. Accessed November 20, 2017.
- Meade, Maureen O., Guyatt, Gordon H., Cook, Richard J., Groll, Ryan, Kachura, John R., Wigg, Melanie, et al. (2001). Agreement between alternative classifications of acute respiratory distress syndrome. American Journal of Respiratory and Critical Care Medicine, 163(2), 490–493.CrossRefGoogle Scholar
- Mizoguchi, M., Mitsuishi, S., Ito, T., Oki, K., Ninomiya, S., Hirafuji, M., Fukatsu, T., Kiura, T., Tanaka, K., Toritani, H. and Hamada, H., (2008). Real-time monitoring of soil information in agricultural fields in Asia using Field server. In Proceedings of 1st global workshop on high resolution digital soil sensing and mapping (Vol. 2, pp. 19–24). http://www.iai.ga.a.u-tokyo.ac.jp/mizo/pocket/DSSM08firstname.lastname@example.org_final_.pdf. Accessed November 20, 2016.
- Mosavi, M. R. (2008). Recurrent polynomial neural networks for enhancing performance of GPS based line fault location. In 9th International conference on signal processing, 2008, ICSP. (pp. 1668–1672). IEEE.Google Scholar
- Nagle, L. K. (2016). Protected Food Production: Applications and Modeling (Doctoral dissertation, The Pennsylvania State University).Google Scholar
- Nimmo, J. R., & Hillel, D. (2004). Porosity and pore size distribution. Encyclopedia of Soils in the Environment, 3, 295–303.Google Scholar
- Sheppard, J., & Hoyle, F. (2016). Water availability, fact sheets, soil quality Pty Ltd, The University of Western Australia. http://soilquality.org.au/factsheets/water-availability Accessed November 20, 2016.
- SMART Fertilizer management (2008). Timing and frequency of fertilizer application. http://www.smart-fertilizer.com/articles/timing-fertilizer-application.
- Sprenger, M., Tetzlaff, D., & Soulsby, C. (2017). Stable isotopes reveal evaporation dynamics at the soil-plant-atmosphere interface of the critical zone. Hydrology and Earth System Sciences Discussion. https://doi.org/10.5194/hess-2017-87 (In review).
- Steduto, P., Hsiao, T. C., Fereres, E., & Raes, D. (2012). Crop yield response to water. Rome: FAO.Google Scholar
- Stephen, G. (2002). Best management practices for irrigating vegetables, extension specialist in vegetable crops, rutgers cooperative extension. New Jersey Agricultural Experiment Station, Rutgers, The State University of New Jersey.Google Scholar
- Tetko, I. V., Aksenova, T. I., Volkovich, V. V., Kasheva, T. N., Filipov, D. V., Welsh, W. J., et al. (2000). Polynomial neural network for linear and non-linear model selection in quantitative-structure activity relationship studies on the internet. SAR and QSAR in Environmental Research, 11(3–4), 263–280.CrossRefGoogle Scholar
- Tran, N. (2016). Irrigation scheduling based on cumulative vapour pressure deficit to predict nursery tree water stress (Doctoral dissertation).Google Scholar
- Treftz, C., & Omaye, S. T. (2016). Comparison between hydroponic and soil systems for growing strawberries in a greenhouse. International Journal of Agricultural Extension, 3(3), 195–200.Google Scholar
- Velasquez, M., & Hester, P. T. (2013). An analysis of multi-criteria decision making methods. International Journal of Operations Research, 10(2), 56–66.Google Scholar
- Wang, X., Li, L., Lockington, D., Pullar, D., & Jeng, D. S. (2005). Self-organizing polynomial neural network for modelling complex hydrological processes. Research Report No R861, Department of Civil Engineering.Google Scholar
- Yin, H.Y., Huang, C.J., Chen, C.Y., Fang, Y.M., Lee, B.J. and Chou, T.Y., (2011). The present development of debris flow monitoring technology in Taiwan—A case study presentation. In Genevois, R., Hamilton, D. L., & Prestininzi, A. (Eds.) 5th International conference on debris-flow hazards mitigation: Mechanics, prediction and assessment. Casa Editrice Universita La Sapienza, Roma (pp. 623–631). http://www.interpraevent.at. Accessed November 20, 2016.