Benchmarking AI-Based Precision Farming Models for Agricultural Decision Making Using FAOSTAT Data
Journal of Smart and Sustainable Farming
Received On : 10 May 2025
Revised On : 16 July 2025
Accepted On : 30 July 2025
Published On : 12 August 2025
Volume 01, 2025
Pages : 131-139
Abstract
This paper evaluates the AI-based Precision Farming (PF) models with the benchmark data set of FAOSTAT combined with simulated IoT sensor data from 30 borehole and field variables. Feedforward Artificial Neural Network (ANN), Random Forest (RF), k-Nearest Neighbors (kNN), Naive Bayes (NB), and the proposed hybrid Graph Neural Network (GNN) machine learning algorithms were trained and tested using an 80:20 data split. To achieve this, we used measures of evaluation, which included integrated recall, execution time, and precision. From the information gathered regarding the experiment, ANN achieved an accuracy of 98.65, the highest precision of 98.32, and the best recall rate of 97.65, which were the best measurement of the models. Integration of the three (i.e., soil moisture, temperature, and humidity) IoT-enabled sensors improved the environmental realism and generalizability of the model, enabling the real-time monitoring and predictive analytics of the crops.
Keywords
Precision Farming, Artificial Intelligence, IoT Sensors, Machine Learning, Graph Neural Networks, Crop Yield Prediction, FAOSTAT Dataset.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Amelia Maria Laura and Minu Balakrishnan; Methodology: Amelia Maria Laura; Data Curation: Minu Balakrishnan; Writing- Original Draft Preparation: Amelia Maria Laura and Minu Balakrishnan; Visualization: Minu Balakrishnan; Investigation: Amelia Maria Laura; Supervision: Minu Balakrishnan; Writing-Reviewing and Editing: Amelia Maria Laura and Minu Balakrishnan; The author reviewed the results and approved the final version of the manuscript.
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Cite this Article
Amelia Maria Laura and Minu Balakrishnan, “Benchmarking AI-Based Precision Farming Models for Agricultural Decision Making Using FAOSTAT Data”, Journal of Smart and Sustainable Farming, pp. 131-139, 2025, doi: 10.64026/JSSF/2025013.