Autonomous Plant Health Monitoring and Climate Actuation using YOLOv8 and ESP32-based Mobile Rover

Abstract

Maintaining plant health and ensuring suitable environmental conditions are essential aspects for modern agriculture. This work introduces an integrated system that combines artificial intelligence, mobile robotics, and Internet of Things (IoT) technologies to enable automated plant monitoring and greenhouse management. The proposed system employs a mobile robotic rover equipped with a vision unit to capture images of plant leaves. These images are processed using a YOLOv8-based deep learning algorithm to identify and classify plant diseases with high accuracy. In parallel, environmental parameters such as soil moisture, temperature, and humidity are continuously measured through embedded sensor modules via ESP32.The system adopts a distributed control framework using ESP32 microcontrollers, allowing seamless interaction between sensing components and actuators. When the soil moisture level falls below a predefined threshold, the system automatically activates a water pump to watering the plants. Along with, if the ambient temperature exceeds the desired limit, a cooling fan is triggered to lower the temperature. These automated control actions ensure that plants are consistently maintained under optimal growth conditions in disease free environment. Real-time data processing enables intelligent decision-making for irrigation and climate control. Furthermore, a mobile application interface allows users to remotely monitor system status, receive instant notifications, and make informed decisions based on collected data.

Plant phenotyping relevance

葉画像をYOLOv8で解析して植物病害を分類する観測手法が、移動ローバーによる植物健康モニタリングの中心的機能として記述されているため、画像ベースの植物状態 phenotyping として含める。

Code and data availability

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