Unverified paper record
AI-Powered Automated Hydroponic System for Smart Agriculture
MethodsX · 1 Jan 2025
Abstract
This research presents an AI-powered automated hydroponic system designed to enhance the efficiency and sustainability of modern agriculture. The system integrates real-time environmental monitoring, automated nutrient management, and AI-based disease detection to optimize plant growth and minimize manual intervention. An ESP32 microcontroller collects data from specialized sensors measuring Total Dissolved Solids (TDS), pH, temperature, and light intensity. Data is wirelessly transmitted via MQTT to an EMQX broker, subsequently processed by an ExpressJS backend, and stored in a Firebase Realtime Database. A NextJS web application provides a user-friendly dashboard for visualization, alerts, and remote control. Automation is achieved using relay-controlled peristaltic and water pumps that adjust nutrient dosing and circulation based on sensor readings. A camera module captures plant images, which are analyzed by a CNN model running on a separate AI server to detect common spinach diseases like Anthracnose and Downy Mildew, enabling early intervention. This integrated system combines IoT, cloud data management, automation, and AI-based visual inspection to offer a comprehensive solution for precision hydroponic farming. Evaluation demonstrates high accuracy in disease detection, robust system performance, and significant potential for improving crop health, yield, and reducing manual labor in diverse agricultural settings. The system, along with its full codebase, has been made publicly available to promote reproducibility.•Automated Precision Hydroponics:Combines real-time environmental monitoring, automated nutrient management, and AI-powered disease detection for optimized spinach cultivation.•Reproducible and Scalable Method:Provides a detailed, step-by-step protocol for constructing and operating the system, adaptable to various hydroponic setups and crop types.•Sustainable and Efficient Agriculture:Minimizes resource consumption, reduces manual labour, and promotes environmentally friendly practices.
Plant phenotyping relevance
植物画像をCNNで解析して病害状態を推定する手法がシステムの主要機能として記述・評価されており、植物フェノタイピングを含む統合プラットフォーム研究である。
abstractA camera module captures plant images, which are analyzed by a CNN model running on a separate AI server to detect common spinach diseases like Anthracnose and Downy Mildew, enabling early intervention.
abstractEvaluation demonstrates high accuracy in disease detection, robust system performance, and significant potential for improving crop health, yield, and reducing manual labor in diverse agricultural settings.
Code and data availability
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No evidence-backed public reproduction asset is currently recorded.
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