Year: 2026
Type: article
Abstract: Agricultural drying is affected by temperature, relative humidity, sunlight and changing weather.Traditional open-air drying requires frequent supervision and exposes harvested produce to unexpected rainfall and variable environmental conditions.This paper presents NESAR, an Internet of Things (IoT)-based agricultural drying monitoring and decision-support prototype for arecanut, black pepper and cocoa.The system uses an ESP32 microcontroller, a DHT11 temperature and humidity sensor, and a light-dependent resistor (LDR) for ambient-light and day/night classification.Sensor data are transmitted over Wi-Fi to Firebase Cloud Firestore, while a Flutter mobile application displays live measurements, batch progress, estimated drying duration and weather-related alerts.Open-Meteo provides forecast information used to assess rainfall risk and environmental conditions.The drying estimator uses crop profiles and empirical rules rather than a trained machine-learning model.Separate batch records are intended to preserve progress across stops and resumptions.Since the prototype has no physical rain sensor or crop-moisture probe, estimated completion time is advisory and requires validation against actual crop-moisture measurements.
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W7225271912