IoT Enabled Plant Growth and Health Monitoring and Prediction

Abstract

The rapid advancement of the Internet of Things (IoT) has significantly transformed traditional methods of environmental monitoring by enabling intelligent, automated, and real-time data acquisition systems. In agriculture and plant care, continuous monitoring of temperature, humidity, and soil moisture is essential for ensuring optimal plant health. This paper presents a cost-effective IoT-based plant growth and health monitoring system using the NodeMCU ESP8266 platform, integrated with a DHT11 and soil moisture sensor. A machine learning model further classifies plant leaf conditions into four health categories: Healthy, Rust, Slug damage, and Powdery Mildew. Sensor data is processed and served through an embedded web server, enabling remote real-time monitoring via any standard web browser. Experimental results validate system accuracy, reliability, and suitability for smart agriculture applications

Plant phenotyping relevance

植物の生育・健康状態を継続取得するIoTシステムと、葉の状態を4分類する機械学習手法が研究の中心であり、植物の健康・病害状態を直接推定するため。

Code and data availability

The paper describes an IoT plant monitoring system with a CNN leaf disease classifier, but contains no public dataset, code, model, or data availability statement. Only generic tool references (Espressif ESP8266, Arduino IDE) are cited, which are not paper-specific assets.

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