Groundnut Vegetative Growth Rate Prediction Using IoT and Machine Learning
DOI:
https://doi.org/10.70882/josrar.2026.v3i3.172Keywords:
Groundnut Vegetative Growth, IoT, PredictionAbstract
The fusion of Internet of Things (IoT) technologies within agriculture has revolutionized crop management, enhancing both output volume and quality while minimizing labor expenses and boosting farmer revenues through intelligent modernization. Precise monitoring of environmental conditions, soil properties, and plant physiology is critical for forecasting agricultural outcomes, with IoT sensors providing the necessary high-resolution field data. This research focused on groundnut cultivation across twenty controlled pots over a 120-day cycle, generating 480 data points by continuously tracking temperature, soil moisture, and essential nutrients: nitrogen, phosphorus, and potassium (NPK). The primary objective was to model crop growth rates specifically in response to varying NPK levels using machine learning algorithms enhanced by bagging techniques, including Multi-Layer Perceptron (MLP), Random Forest (RF), and K-Nearest Neighbors (KNN). Model efficacy was assessed via R-squared, MAE, MSE, RMSE, and RMSLE metrics. In the specific context of predicting growth dynamics driven by NPK inputs, the MLP architecture demonstrated distinct superiority over its counterparts. It achieved the highest coefficient of determination (R² = 0.52) alongside the lowest error rates, recording an MAE of 1.3499, MSE of 2.7220, RMSE of 1.6498, and a remarkably minimal RMSLE of 0.0024. Conversely, RF and KNN exhibited comparatively lower predictive accuracy for this specific task. These findings highlight the unique capability of neural network-based approaches like MLP in deciphering complex nutrient-growth relationships, offering farmers a powerful tool for optimizing fertilizer application and resource allocation to maximize crop development.
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Copyright (c) 2026 Abdullahi Ali Bala, Nura Shafi’u, Oyenike Mary Olanrewaju, Eli Adama Jiya, Faith Oluwatosin Echobu, Joseph Nda Ndabula, Emmy Danny Ajik, Yusuf Idris Muhammad, Abubakar Auwal Idris (Author)
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