A Precision Agriculture Framework for Strawberry Cultivation Using IoT-based Soil Moisture Monitoring and Tsukamoto Fuzzy Inference

Abstract

Strawberry cultivation requires precise soil moisture management to maintain optimal growth conditions and prevent yield reduction. Traditional manual monitoring and irrigation methods are labor-intensive, time-consuming, and prone to inefficiencies that may result in crop failure. This study presents a precision agriculture framework that integrates Internet of Things (IoT) technology with Tsukamoto fuzzy inference to enable real-time soil moisture monitoring and intelligent irrigation control for strawberry farming. The system employs a capacitive soil moisture sensor connected to a NodeMCU ESP8266 microcontroller, which transmits data to the ThingSpeak cloud platform for remote access and visualization. The Tsukamoto fuzzy method processes sensor readings to determine the optimal irrigation duration based on predefined moisture thresholds. Experimental results show that the system achieved a soil moisture range of 44–85%, with an average of 80.9%, and a user satisfaction score of 69.01%. This approach demonstrates the potential to reduce water usage, minimize crop failure risks, and improve overall efficiency in high-value crop production. The proposed framework offers a scalable solution for sustainable agriculture, particularly in resource-constrained farming environments.