Agriculture needs 85 percent of the available freshwater and its requirement may increase in future. Hence, a system is needed to utilize water e?ciently in agriculture. The automatic irrigation control system is used to achieve this aim.The modern drip irrigation system lessens a signi?cant amount of water usage compared to the traditional methods. And some crops need variable amounts of water as it grow e.g. paddy. This paper proposes an automation of drip irrigation in which the smartphone initially captures soil image, calculates its wetness level and transmits the data onto the microcontroller through GSM module intermittently. The microcontroller decides the irrigation and sends the status of the ?eld to the Farmer’s mobile phone. The system is tested for paddy ?eld for over a period of three months. It is observed from the experimental setup, that it saves nearly 41.5percentage and 13percentage of water compared to the conventional ?ood and drip irrigation methods respectively.

Android application, Drip irrigation, GSM module, Microcontroller

[1] Bindu G, Shubham A, Jatin S. Crop yield forecasting using fuzzy logic and regression model. Comput. Electr. Eng. 2018;67:383–403.
[2] Jian G, Guanghua Y, Pengfei H, Jinlu G, Lijun C. An improved back propagation neural network prediction model for subsurface drip irrigation system. Comput. Electr. Eng. 2017;60:58–65.
[3] Jino RSR, Jackuline MD. Application of wireless sensor networks- A survey. In: IEEE international conference on Innovations in Electrical, Electronics, Instrumentation and media technology ICIEEIMT; 2017. p. 325–9.
[4] Shuangyin L, Longqin X, Daoliang L. Multi-scale prediction of water temperature using empirical mode decomposition with back-propagation neural networks. Comput. Electr. Eng. 2016;49:1–8.
[5] Navarro HH, Martínez-del RJ, Domingo MR, Soto VF, Torres SR. A decision support system for managing irrigation in agriculture. Comput. Electron. Agric. 2016;124:121–31.
[6] Roshan ZA, Rajashekhar CB, Shilpa SC. Cluster-based data aggregation for pest identi?cation in coffee plantations using wireless sensor networks. Comput. Electr. Eng. 2016;56:591–607.
[7] Yuttana I, Sarun S. Android-based rice leaf color analyzer for estimating the needed amount of nitrogen fertilizer. Comput. Electron. Agric. 2015;116:228–33.
[8] Guodong S, Tao H, Gaoxiang Y, Jianbo J. Real-time and clock-shared rainfall monitoring with a wireless sensor network. Comput. Electron. Agric. 2015;119:1–11.
[9] Jose P, Gemma H, Coen D, Alberto G, Oscar C. Design of a low-cost wireless sensor network with UAV mobile node for agricultural applications. Comput. Electron. Agric. 2015;119:19–32.
[10] Farshad V, Mahmoud O, Amy K, Hossein M. Development of an android app to estimate chlorophyll content of corn leaves on contact imaging. Comput. Electron. Agric. 2015;116:211–20.
[11] Joaquin G, Juan FVM, Alejandra NG, Miguel APG. Automated irrigation system using a wireless sensor network and GPRS module. IEEE Trans. Instrum. meas. 2014;63(1):166–76.
[12] Kumar V, Vimal BK, Kumar R, Kumar M. Determination of soil PH by using digital image processing technique. J. Appl. Nat. Sci. 2014;6(1):14–18.
[13] Vyasa S, Marlin HM. Exploring energy e?cient architectures in passive wireless nodes for IoT applications. IEEE Circuits Syst. Mag. 2014;14(2):48–54.
[14] Vyasa S, Marlin HM. Low power 8051-MISA based remote execution unit architecture for IoT and RFID applications. Int. J. Circuits Architect. Des. 2013;1(1):4–19.
[15] Nilesh RP, Rahul BL, Swarup SM, Ashwin AB. Microcontroller based drip irrigation system using smart sensor. In: Annual IEEE India Conference (INDICON); 2013. p. 1–5.
[16] Aiping G, Xiang W, Zhengjun Q, Yong H. A handheld device for leaf area measurement. Comput. Electron. Agric. 2013;98:74–80.
[17] Jeroen B, Dieter C, Marloes P, Thomas B, Stijn D, Thomas JC, Patrick W, Ronald T, Ward DC. Mobile application for impedance-based biomimetic sensor readout. IEEE Sens. J. 2013;13(7):2659–65.
[18] Nicholas JC, Evan WC, John WH, Graham AM. Using a mobile phone Short Messaging Service (SMS) for irrigation scheduling in Australia – Farmers’ participation and utility evaluation. Comput. Electron. Agric. 2012;84:132–43.
[19] Vyasa S, Ajay O, Marlin HM. Low power solutions for wireless passive sensor network node processor architecture. In: Fei H, Qi H, editors. Intelligent sensor Networks: the integration of sensor Networks, signal processing and machine learning. Boca Raton, FL: CRC Press; 2012. p. 515–28.
[20] Boon GL, Wan YC. Driver alertness monitoring using fusion of facial features and bio-signals. IEEE Sens, J. 2012;12(7):2416–22.
[21] Daniel KF, Hirut K. A low-cost microcontroller-based system to monitor crop temperature and water status. Comput. Electron. Agric. 2010;74(1):168–73.
[22] Kim Y, Evans RG. Software design for wireless sensor-based site-speci?c irrigation. Comput. Electron. Agric. 2009;66(2):159–65.
[23] Mark H, Michael JL, David B, Gu-Yeon W. Survey of hardware systems for wireless sensor networks. J. Low Power Electron. 2008;4:1–10.
[24] Jury WA, Vaux HJ. The emerging global water crisis: managing scarcity and con?ict between water users. Adv. Agron. 2007;95:1–76.
[25] Xu N. A survey of sensor network applications. IEEE Commun. Mag. 2002;40(8):1–9.
[26] http://www.thehindu.com/news/national/tamil-nadu/where-paddy-requires-only-drops-of-water/article4743549.ece.
[27] Studio encoding parameters of digital television for standard 4:3 and wide screen 16:9 aspect ratios, International Telecommunication Union Recom- mendation BT.