[Paper Review] Integration of IoT- AI powered local weather forecasting: A Game-Changer for Agriculture

Suman R. Das, Parv Nayak|ArXiv.org|Dec 22, 2024

Smart Agriculture and AI4 citations

TL;DR

This paper proposes an IoT-AI integrated framework for high-resolution, real-time local weather forecasting to enhance agricultural decision-making. By combining low-cost IoT sensors with machine learning models across five layers—data acquisition, storage, processing, application, and decision-making—the system enables precise, localized weather predictions, advancing smart farming and sustainability, though challenges like digital literacy gaps remain unaddressed.

ABSTRACT

The dynamic environment context necessitates harnessing digital technologies, including artificial intelligence and the Internet of Things, to supply high-resolution, real-time meteorological data to support agricultural decision-making and improve overall farm productivity and sustainability. This study investigates the potential application of various AI-powered, IoT-based, low-cost platforms for local weather forecasting to enable smart farming. Despite the increasing demand for this topic, a few promising studies have explored this area. This paper developed a conceptual research framework based on a systematic review of relevant literature and employed a case study method to validate the framework. The framework comprised five key components: the Data Acquisition Layer, Data Storage Layer, Data Processing Layer, Application Layer, and Decision-Making Layer. This paper contributes to the literature by exploring the integration of AI-ML and IoT techniques for weather prediction tasks to support agriculture, and the incorporation of IoT technologies that provide real-time, high-resolution meteorological data, representing a step forward. Furthermore, this paper discusses key research gaps, such as the significant obstacles impeding the adoption of AI in agriculture and local weather forecasting, including the lack of straightforward solutions and the lack of digital skills among farmers, particularly those in rural areas. Further empirical research is needed to enhance the existing frameworks and address these challenges.

Motivation & Objective

• To develop a conceptual framework integrating IoT and AI for localized, real-time weather forecasting in agriculture.
• To address the gap in high-resolution, accessible weather data for smallholder and rural farmers.
• To identify key barriers—such as lack of digital skills and scalable solutions—that hinder AI adoption in agriculture.
• To propose a structured, layered system for data flow and decision support in farming contexts.
• To stimulate further empirical research on scalable, farmer-friendly AI-IoT weather forecasting systems.

Proposed method

• The framework is built on five layers: Data Acquisition Layer using low-cost IoT sensors for real-time meteorological data collection.
• Data Storage Layer organizes and secures collected sensor data using cloud or edge-based storage systems.
• Data Processing Layer applies machine learning models (e.g., regression, neural networks) to analyze and predict weather patterns from sensor inputs.
• The Application Layer visualizes forecasts via mobile or web interfaces for farmer accessibility.
• The Decision-Making Layer integrates predictions into actionable agricultural recommendations (e.g., irrigation, planting schedules).
• A systematic literature review and case study validation were used to develop and test the framework’s feasibility.

Experimental results

Research questions

• RQ1 How can IoT and AI be integrated to deliver accurate, localized weather forecasts for agricultural use?
• RQ2 What are the key technical and operational components required for a scalable, low-cost weather forecasting system in rural farming?
• RQ3 What barriers—technical, social, or infrastructural—limit the adoption of AI-powered weather forecasting among smallholder farmers?
• RQ4 How does the proposed five-layer framework improve data flow and decision-making in agriculture compared to traditional methods?
• RQ5 What role do digital literacy and system accessibility play in the real-world deployment of such AI-IoT systems?

Key findings

• The proposed five-layer framework effectively integrates IoT sensor data with AI-driven weather prediction, enabling real-time, localized forecasting for agricultural applications.
• The integration of low-cost IoT devices with machine learning models offers a scalable, cost-effective solution for high-resolution weather data in remote farming areas.
• Despite technical promise, significant adoption barriers remain, including limited digital skills among rural farmers and the absence of user-friendly, turnkey solutions.
• The framework demonstrates potential for improving farm productivity and sustainability through timely, data-driven agricultural decisions.
• The study identifies a critical need for future empirical research to refine the framework and address usability and accessibility challenges in real-world farming environments.