Harnessing IoT for Real-Time Plant Health Monitoring: Challenges and Opportunities

,. P. T. C. S. Roja D, N Venkatesh, S Maheswari
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Abstract

The "Plant Health Monitoring Project" is an intelligent system designed to monitor and maintain optimal conditions for plant growth. This project incorporates sensors such as LDR (Light Dependent Resistor), DHT11 (Temperature and Humidity Sensor), Soil Moisture Sensor, a Water Motor, and a Relay. The collected data, including light intensity, temperature, humidity, and soil moisture, is sent to Thing Speak for real-time monitoring. The system automatically activates the water motor through the relay if the soil moisture level is low, ensuring the plants receive adequate water. This project aims to promote efficient plant care and ensure a healthy growth environment. This project aims to develop an advanced plant health monitoring system by integrating Internet of Things (IoT) technology with machine learning algorithms. The system will utilize various sensors to collect real-time data on environmental conditions such as temperature, humidity, soil moisture, and light intensity, as well as plant health parameters including leaf color, size, and shape. The collected data will be transmitted wirelessly to a central server for analysis. Machine learning algorithms will then be employed to analyze the data and identify patterns indicative of plant stress, diseases, or nutrient deficiencies. The system will provide timely alerts to farmers or gardeners, enabling them to take proactive measures to maintain the health and productivity of their plants. This innovative approach to plant health monitoring has the potential to revolutionize agricultural practices and contribute to increased crop yields and stainability.
利用物联网进行实时植物健康监测:挑战与机遇
植物健康监测项目 "是一个智能系统,旨在监测和维持植物生长的最佳条件。该项目集成了 LDR(光敏电阻)、DHT11(温湿度传感器)、土壤湿度传感器、水马达和继电器等传感器。收集到的数据,包括光照强度、温度、湿度和土壤湿度,都会发送到 Thing Speak 进行实时监控。如果土壤湿度较低,系统会通过继电器自动启动水马达,确保植物获得充足的水分。本项目旨在促进高效的植物护理,确保植物有一个健康的生长环境。本项目旨在通过整合物联网技术和机器学习算法,开发先进的植物健康监测系统。该系统将利用各种传感器收集温度、湿度、土壤水分和光照强度等环境条件的实时数据,以及叶片颜色、大小和形状等植物健康参数。收集到的数据将通过无线方式传输到中央服务器进行分析。然后将采用机器学习算法来分析数据,找出表明植物压力、疾病或养分缺乏的模式。该系统将及时向农民或园艺师发出警报,使他们能够采取积极措施,保持植物的健康和生产力。这种创新的植物健康监测方法有可能彻底改变农业实践,并有助于提高作物产量和可渍化性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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