Sensor Enabled - Soil and Plant Health Portable Device

Abstract

Abstract. The rapid advancement of smart agriculture has created a need for efficient and real-time monitoring systems to improve crop productivity and sustainability. This project presents a sensor- enabled portable device designed to monitor soil and plant health using Internet of Things and Machine Learning techniques. The system integrates sensors such as soil moisture and temperature sensors with a microcontroller to collect environmental data continuously. The collected data is processed and displayed on a user-friendly web dashboard, enabling farmers to make informed decisions regarding irrigation and crop management. Additionally, a Convolutional Neural Network (CNN) model is implemented for plant disease detection using leaf images, achieving high accuracy. The device is designed to be low-cost, portable, and easy to use, making it suitable for small and medium-scale farmers. Experimental results demonstrate reliable performance in monitoring soil conditions and detecting plant diseases. The proposed system contributes to precision agriculture by reducing resource wastage, improving crop health, and enhancing overall agricultural efficiency. Keywords: Plant Health, Machine Learning, Precision Agriculture, Plant Disease Detection, Smart Farming

Plant phenotyping relevance

葉画像による植物病害検出をCNNで実装した携帯型センシングシステムが研究の中心であり、植物状態の取得・判定手法を含むため。

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