The agricultural sector plays a significant role in the economy of many countries, and irrigation is a critical component of successful agriculture. However, traditional irrigation methods can be time-consuming and labor-intensive, and often result in the over or under-watering of crops, which can negatively impact crop yields. To overcome these challenges, smart irrigation systems have been developed to assist farmers in managing their crops and increasing their yield. This research article presents an IoT-based smart irrigation system that uses four sensors - moisture content, temperature, humidity, and ultrasonic - to collect data from the irrigation area and transmit it to a central control system. The central control system uses the data to automatically turn the irrigation pump on and off, based on the moisture level of the soil. The system also includes a mobile application that allows farmers to monitor the system remotely and control the motor pump from their smartphones. The proposed system has several advantages, including reducing the hard work of farmers, providing essential strength to crops, and ensuring that plants receive the adequate amount of water at the required time. Additionally, the system's remote monitoring capabilities allow farmers to monitor the atmospheric temperature, humidity, and moisture content from anywhere at any time, and make adjustments as necessary. Overall, the findings of this research will help farmers to control their irrigation systems remotely, reduce labor costs, and increase crop yields. By improving the efficiency of irrigation and reducing water waste, this IoT-based smart irrigation system has the potential to significantly impact the agriculture sector and promote sustainable farming practices.
Keywords
Smart Irrigation, Sensors, Internet of Things, Cloud Storage, Mobile Integration.
V. R. Pathmudi, N. Khatri, S. Kumar, A. S. H. Abdul-Qawy, and A. K. Vyas, “A systematic review of IoT technologies and their constituents for smart and sustainable agriculture applications,” Scientific African, vol. 19, p. e01577, Mar. 2023, doi: 10.1016/j.sciaf.2023.e01577.
N. Chamara, M. D. Islam, G. (Frank) Bai, Y. Shi, and Y. Ge, “Ag-IoT for crop and environment monitoring: Past, present, and future,” Agricultural Systems, vol. 203, p. 103497, Dec. 2022, doi: 10.1016/j.agsy.2022.103497.
Engr. A. H. Fernando et al., “Design of a fuzzy logic controller for a vent fan and growlight in a tomato growth chamber,” 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Dec. 2017, doi: 10.1109/hnicem.2017.8269526.
M. Mahbub, M. M. Hossain, and Md. S. A. Gazi, “Cloud-Enabled IoT-based embedded system and software for intelligent indoor lighting, ventilation, early stage fire detection and prevention,” Computer Networks, vol. 184, p. 107673, Jan. 2021, doi: 10.1016/j.comnet.2020.107673.
A. S. Zamani et al., “Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection,” Journal of Food Quality, vol. 2022, pp. 1–7, Apr. 2022, doi: 10.1155/2022/1598796.
X. Cui, “Cyber-Physical System (CPS) architecture for real-time water sustainability management in manufacturing industry,” Procedia CIRP, vol. 99, pp. 543–548, 2021, doi: 10.1016/j.procir.2021.03.074.
J. A. Barriga, F. Blanco-Cipollone, E. Trigo-Córdoba, I. García-Tejero, and P. J. Clemente, “Crop-water assessment in Citrus (Citrus sinensis L.) based on continuous measurements of leaf-turgor pressure using machine learning and IoT,” Expert Systems with Applications, vol. 209, p. 118255, Dec. 2022, doi: 10.1016/j.eswa.2022.118255.
Daniel Ashlock, “Modeling Frameworks for Knowledge Engineering Approaches”, Journal of Computing and Natural Science, vol.2, no.1, pp. 015-020, January 2022. doi: 10.53759/181X/JCNS202202003.
A. Chinasho, B. Bedadi, T. Lemma, T. Tana, T. Hordofa, and B. Elias, “Response of maize to irrigation and blended fertilizer levels for climate smart food production in Wolaita Zone, southern Ethiopia,” Journal of Agriculture and Food Research, vol. 12, p. 100551, Jun. 2023, doi: 10.1016/j.jafr.2023.100551.
A. K. Sangaiah, S. Rezaei, A. Javadpour, and W. Zhang, “Explainable AI in big data intelligence of community detection for digitalization e-healthcare services,” Applied Soft Computing, vol. 136, p. 110119, Mar. 2023, doi: 10.1016/j.asoc.2023.110119.
A. Katimbo et al., “Evaluation of artificial intelligence algorithms with sensor data assimilation in estimating crop evapotranspiration and crop water stress index for irrigation water management,” Smart Agricultural Technology, vol. 4, p. 100176, Aug. 2023, doi: 10.1016/j.atech.2023.100176.
A. A. Junior, T. J. A. da Silva, and S. P. Andrade, “Smart IoT lysimetry system by weighing with automatic cloud data storage,” Smart Agricultural Technology, vol. 4, p. 100177, Aug. 2023, doi: 10.1016/j.atech.2023.100177.
R. K. Jain, “Experimental performance of smart IoT-enabled drip irrigation system using and controlled through web-based applications,” Smart Agricultural Technology, vol. 4, p. 100215, Aug. 2023, doi: 10.1016/j.atech.2023.100215.
J. G. M. A et al., “Microclimate monitoring system for irrigation water optimization using IoT,” Measurement: Sensors, vol. 27, p. 100727, Jun. 2023, doi: 10.1016/j.measen.2023.100727.
E. Delpiazzo et al., “The economic value of a climate service for water irrigation. A case study for Castiglione District, Emilia-Romagna, Italy,” Climate Services, vol. 30, p. 100353, Apr. 2023, doi: 10.1016/j.cliser.2023.100353.
E. Bojago and Y. Abrham, “Small-scale irrigation (SSI) farming as a climate-smart agriculture (CSA) practice and its influence on livelihood improvement in Offa District, Southern Ethiopia,” Journal of Agriculture and Food Research, vol. 12, p. 100534, Jun. 2023, doi: 10.1016/j.jafr.2023.100534.
Marco Scotini and Hussein Abdullah, “Integration of Environment and Content Knowledge into STEM Education”, Journal of Computing and Natural Science, vol.2, no.1, pp. 021-026, January 2022. doi: 10.53759/181X/JCNS202202004.
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M Senthil Vadivu
M Senthil Vadivu
Department of Electronics and Communication Engineering, Sona College of Technology, Salem, Tamil Nadu, India.
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M Senthil Vadivu, M Purushotham Reddy, Kantilal Rane, Narendra Kumar, A. Karthikayen and Nitesh Behare, “An IoT-Based System for Managing and Monitoring Smart Irrigation through Mobile Integration”, Journal of Machine and Computing, vol.3, no.3, pp. 196-205, July 2023. doi: 10.53759/7669/jmc202303018.