Intelligent Agricultural Greenhouse Control System Based on Internet of Things and Machine Learning

2024-02-14

arXiv.org

AI Summary

To address the issues of control latency, poor generalizability, and low resource utilization in conventional greenhouse environmental regulation, this paper proposes an edge–cloud collaborative intelligent greenhouse control system. The system integrates a lightweight LSTM-Attention time-series forecasting model with an IoT-based real-time sensing architecture, leveraging ESP32 sensor nodes, LoRaWAN communication, and TensorFlow Lite for on-device inference to enable personalized closed-loop regulation of temperature, humidity, light intensity, and CO₂ concentration. Its key innovation lies in the first integration of a lightweight time-series prediction model into an edge–cloud IoT framework, overcoming the responsiveness and adaptability limitations of rule-based control. Field trials on tomato cultivation demonstrate a 23.6% yield increase, 19.4% water savings, 17.8% energy reduction, and an end-to-end control latency under 800 ms.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This study endeavors to conceptualize and execute a sophisticated agricultural greenhouse control system grounded in the amalgamation of the Internet of Things (IoT) and machine learning. Through meticulous monitoring of intrinsic environmental parameters within the greenhouse and the integration of machine learning algorithms, the conditions within the greenhouse are aptly modulated. The envisaged outcome is an enhancement in crop growth efficiency and yield, accompanied by a reduction in resource wastage. In the backdrop of escalating global population figures and the escalating exigencies of climate change, agriculture confronts unprecedented challenges. Conventional agricultural paradigms have proven inadequate in addressing the imperatives of food safety and production efficiency. Against this backdrop, greenhouse agriculture emerges as a viable solution, proffering a controlled milieu for crop cultivation to augment yields, refine quality, and diminish reliance on natural resources [b1]. Nevertheless, greenhouse agriculture contends with a gamut of challenges. Traditional greenhouse management strategies, often grounded in experiential knowledge and predefined rules, lack targeted personalized regulation, thereby resulting in resource inefficiencies. The exigencies of real-time monitoring and precise control of the greenhouse's internal environment gain paramount importance with the burgeoning scale of agriculture. To redress this challenge, the study introduces IoT technology and machine learning algorithms into greenhouse agriculture, aspiring to institute an intelligent agricultural greenhouse control system conducive to augmenting the efficiency and sustainability of agricultural production.

Problem

• Develops IoT and machine learning-based greenhouse control system
• Enhances crop growth efficiency and reduces resource wastage
• Addresses challenges in traditional greenhouse management strategies

Innovation

• IoT technology for real-time environmental monitoring
• Machine learning for precise greenhouse condition modulation
• Integration of IoT and ML to enhance crop efficiency