🎯 Executive Summary
Frost prediction and warning for orchardists and farmers based on weather data and sensors — reducing frost damage.
💰 Estimated ROI: 40–70% less crop frost damage, billions of Tomans saved nationwide.
🏭 Target Industries: Agriculture, horticulture, crop insurance
⏱ Implementation Time: MVP in 6 weeks, network expansion 3–4 months
📊 Key KPIs: Prediction accuracy, geographic‑coverage rate, damage prevented, user satisfaction
📋 Technical Project Details
This system is designed to predict and issue timely warnings to prevent damage from frost in agricultural products. The main challenge was the precise analysis of local climate conditions and prediction of the critical temperature at the field level, because general weather data did not have sufficient accuracy to detect cold in specific spots within orchards.
The system consists of a network of low‑power temperature and humidity sensors that send data wirelessly and continuously to a central server. These sensors are installed on trees and at various points in the field to record minor temperature differences across heights and different areas. The collected data is analyzed by machine‑learning models to predict the likelihood of frost over short time windows.
On the technical side, the focus was on developing prediction models based on local data and combining them with nationwide meteorological data. Time‑series forecasting and regression models were used to predict temperature and humidity, raising warning accuracy to over 92%. When a frost risk is detected, the system sends an alert to farmers via SMS, app notification or the management dashboard so that protective measures — such as covering trees or activating heating systems — can be taken.
In designing this system, energy sustainability and self‑sufficiency were also considered; the sensors use solar panels and an internal battery so they remain active in all seasons without a direct power connection. This feature makes the system usable even in remote areas.
Ultimately, the intelligent frost‑detection‑and‑warning system is a key tool for smart agriculture that, by accurately predicting critical conditions, makes it possible to reduce crop damage and increase economic productivity. This system is a successful example of combining IoT, data analytics and artificial intelligence in service of sustainability and smart management of agricultural resources.