Precision Agriculture 4.0: Implementation of IoT, AI, and Sensor Networks for Tomato Crop Prediction - Pérez, Miguel Ángel Giménez; González, Antonio Guerrero; Rodríguez, Francisco Javier Cánovas; Leon, Inocencia María Martínez; Abrisqueta, Francisco Antonio Lloret (Universitas Ahmad Dahlan, 2024)
Precision agriculture introduces an innovative approach to farm management by involving the use of technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and sensor networks to optimize resources and increase crop yields. In this context, the present study aimed to develop a tomato crop prediction system using IoT, AI, and sensor networks. A system architecture was designed, including distributed sensors, IoT gateways, and a cloud platform running AI models based on recurrent neural networks. These AI models were trained with environmental data and validated using actual harvest data. The results showed up that the model could predict weekly harvest volumes with an average error of 3.2% during the best 4-week period. The integration of IoT, AI, and sensor networks proved to be effective for accurate crop prediction and has potential for other applications in precision agriculture.