Precision crop prediction using IoT-enabled soil sensors and machine learning

Abstract

This paper introduces a cutting-edge approach for crop prediction that harnesses IoT-enabled soil sensors and machine learning models, specifically targeting cardamom, black pepper, and coffee in Idukki District, Kerala, India. The study aims to bridge the gap between soil nutrient analysis and precision agriculture by integrating a JXCT 7-in-1 soil sensor with Arduino UNO. This sensor provides accurate real-time measurements of soil moisture, temperature, pH, electrical conductivity, nitrogen, phosphorus, and potassium levels, which are critical for assessing soil health and suitability. The dataset used comprises 300 soil samples for cardamom, 320 for black pepper, and 300 for coffee, providing a robust foundation for analysis. Data from these sensors were processed using XGBoost and AdaBoost algorithms. Among the models, XGBoost achieved the highest accuracy of 91.2% and an AUC of 0.93, while AdaBoost also demonstrated strong performance with an AUC of 0.91. The results confirm the effectiveness of the system in providing precise crop suitability predictions and supporting farmers in making informed decisions based on comprehensive soil data. This approach not only improves crop yields and promotes sustainable farming practices but also shows potential for broader application in different regions and crops. Future research could expand the dataset and incorporate additional IoT devices to enhance the system’s accuracy and agricultural impact.