AN AUTOMATED CROP MONITORING AND IRRIGATION SYSTEM WITH PREDICTIVE ANALYSIS

Nilotpal Deka, Devaj Neogi, Atowar-Ul Islam, Sangeeta Borkakoty
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Abstract

Background: The rising global population and decreasing agricultural land challenge the food supply, worsened by land degradation and water scarcity. Using IoT solutions, precision farming offers promise for addressing these issues by enhancing productivity, profitability, and environmental sustainability. The paper proposes an IoT-based automated monitoring and irrigation system with predictive analysis as a solution. Aims: To develop an IoT-based irrigation model with cloud computing, a mobile app for remote monitoring and irrigation control, and a predictive analysis method for forecasting weather conditions. Methods: IoT sensors are connected to a node MCU, and they monitor real-time temperature, humidity, and soil moisture. A water pump adjusts irrigation accordingly. Data is sent to ThingSpeak for visualization and Firebase for storage and is accessible via a mobile app. Predictive analysis combines sensor and historical weather data using the Random Forest algorithm to optimize irrigation. This determines the ideal irrigation frequency, duration, and timing based on predicted temperature and soil moisture levels, ensuring optimal soil moisture for plant growth. Results: The system exhibited encouraging outcomes. It displayed transformative potential by harnessing IoT, cloud computing, and predictive analytics. Offering precise soil moisture monitoring, instant data access, and tailored advice, the system can elevate crop management, streamline water consumption, and boost productivity among Indian farmers. Discussion: The automated crop monitoring and irrigation system has transformative potential in farming. Some future scope and enhancements may include broadening crop compatibility, integrating satellite data, enhancing pest monitoring, improving connectivity, enriching the mobile app, scaling up through partnerships, and refining the predictive models. Conclusions: The IoT-driven crop monitoring system revolutionizes Indian agriculture by optimizing irrigation and integrating weather forecasts. With mobile app enhancements and future improvements, it promises sustainable farming and empowerment for farmers.
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具有预测分析功能的自动作物监测和灌溉系统
背景:全球人口的不断增长和农业用地的不断减少给粮食供应带来了挑战,而土地退化和水资源短缺又加剧了这一挑战。利用物联网解决方案,精准农业有望通过提高生产率、盈利能力和环境可持续性来解决这些问题。本文提出了一种基于物联网的自动监测和灌溉系统,并将预测分析作为一种解决方案。目的:利用云计算开发基于物联网的灌溉模型、用于远程监控和灌溉控制的移动应用程序,以及预测天气状况的预测分析方法。方法:物联网传感器连接到节点 MCU,实时监测温度、湿度和土壤湿度。水泵会相应地调整灌溉。数据发送到 ThingSpeak 进行可视化,Firebase 进行存储,并可通过移动应用程序访问。预测分析结合传感器和历史天气数据,使用随机森林算法优化灌溉。根据预测的温度和土壤湿度水平,确定理想的灌溉频率、持续时间和时机,确保植物生长所需的最佳土壤湿度。结果该系统取得了令人鼓舞的成果。通过利用物联网、云计算和预测分析技术,它显示出了变革潜力。该系统提供精确的土壤湿度监测、即时数据访问和量身定制的建议,可提升作物管理水平、简化用水流程并提高印度农民的生产率。讨论:自动化作物监测和灌溉系统具有变革农业的潜力。未来的一些范围和改进措施可能包括扩大作物兼容性、整合卫星数据、加强病虫害监测、改善连接性、丰富移动应用程序、通过合作扩大规模以及完善预测模型。结论物联网驱动的作物监测系统通过优化灌溉和整合天气预报彻底改变了印度的农业。随着移动应用程序的增强和未来的改进,该系统有望实现可持续耕作并增强农民的能力。
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