Model Budidaya Tanaman Adaptif Berbasis Digital Twin dan Kecerdasan Buatan untuk Pertanian Presisi
DOI:
https://doi.org/10.54367/jtiust.v11i1.6687Keywords:
Digital Twin, Precision Agriculture, Artificial Intelligence, Adaptive Crop Cultivation, Smart AgricultureAbstract
Precision agriculture requires a system capable of integrating agronomic data, simulations, and artificial intelligence to support adaptive crop management decision-making. However, most previous research has focused on monitoring or simulation without combining predictive and recommendation functions within a single integrated framework. This study aims to develop an Adaptive Crop Cultivation Model Based on Digital Twins and Artificial Intelligence for Precision Agriculture using the Design Science Research (DSR) approach. The proposed model integrates crop cultivation data from the Crop Recommendation Dataset (2,200 data points) and soil characteristic data from SoilGrids (5,000 data points) through the stages of data acquisition, data cleaning, normalization, and feature engineering. The developed framework consists of a Digital Twin Layer, an AI and Simulation Layer, and an Adaptive Recommendation Layer to support real-time simulation, prediction, and crop cultivation recommendations. The results demonstrate that the integration of Digital Twin and artificial intelligence can create a virtual representation of crops, support predictions of water requirements, fertilization needs, and disease risks, and generate adaptive cultivation recommendations based on agronomic conditions. The proposed model has the potential to improve resource management efficiency, enhance the quality of decision-making, and support the implementation of smarter and more sustainable precision agriculture.References
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