Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 17 April,2026 Pages:2133-2138
Inefficient irrigation practices and the absence of real-time field monitoring contribute significantly to water wastage and reduced agricultural productivity. Manual irrigation methods rely heavily on human judgment, often resulting in over-irrigation or water stress conditions for crops. This paper presents an IoT based smart agriculture system designed for automatic irrigation, soil moisture analysis, and crop health monitoring. The proposed system integrates soil moisture sensors, water level sensors, and an ESP32 microcontroller to automate irrigation based on real-time field conditions. A relay-controlled pump motor ensures efficient water usage while preventing dry-run damage. In addition, an ESP32-CAM module enables real-time remote crop monitoring through live video streaming. Experimental implementation demonstrates that the system reduces water wastage, minimizes human intervention, and enhances irrigation efficiency. The proposed solution offers a lowcost, scalable, and sustainable approach suitable for modern smart farming applications.
Smart agriculture, IoT, automatic irrigation, soil moisture monitoring, ESP32
[1] R. C. Lambe, Ornamental and ower diseases: Camellia ower blight, Plant Dis. Control Notes, Extension Division, Virginia Polytech. Inst. State Univ., Tech. Rep., 1979, vol. 99. [2]M. Mehrabi, E. M. Goltapeh, and K. B. Fotouhifar, Studies on cytospora canker disease of apple trees in semirom region of Iran, J. Agricult. Tech nol., vol. 7, no. 4, pp. 967982, 2011. [3]E. E. Wilson, F. M. Zeitoun, and D. L. Fredrickson, Bacterial phloem canker, a new disease of Persian walnut trees, Phytopathology, vol. 57, no. 9, pp. 618621, 1967. [4]K. C. Eastwell and M. G. Bernardy, Relationship of cherry virus a to little cherry disease in British Columbia, in Proc. 17th Int. Symp. Virus Virus-Like Diseases Temperate Fruit Crops, 1997, pp. 305314, doi: 10.17660/ActaHortic.1998.472.36. [5]F. Oiv, Table and dried grapes: World data available, in Proc. Int. Organisation Vine Wine, Paris, France, Jul. 2021. Crone, S. F., Lessmann, S., & Stahlbock, R. (2006). The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing. European Journal of Operational Research, 173(3), 781-800. [6]A. E. Ibrahimi and N. E. Akchioui, ‘‘A review on plant diseases detec tion using artificial intelligence techniques,’’ in Proc. AIP Conf., 2023, p. 40018. [7]L. Jia, T. Wang, Y. Chen, Y. Zang, X. Li, H. Shi, and L. Gao, ‘‘MobileNet CA-YOLO: An improved YOLOv7 based on the MobileNetV3 and attention mechanism for rice pests and diseases detection,’’ Agriculture, vol. 13, no. 7, p. 1285, Jun. 2023. [8]Z.Salman,A.Muhammad,M.J.Piran,andD.Han,‘ ‘Crop-savingwithAI: Latest trends in deep learning techniques for plant pathology,’’ Frontiers Plant Sci., vol. 14, Aug. 2023, Art. no. 1224709. [9]A. Thakur, S. Venu, and M. Gurusamy, ‘‘An extensive review on agricul tural robots with a focus on their perception systems,’’ Comput. Electron. Agricult., vol. 212, Sep. 2023, Art. no. 108146. [10]Z. Jia, J. Hao, Y. Hou, R. Wang, R. Zhang, and S. Yao, ‘‘Study on rapid detection and identification of multi category apple leaf disease,’’ INMATEH Agricult. Eng., vol. 67, no. 6, pp. 67–76, Aug. 2022