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<p>The global population is expected to exceed 10&#xa0;billion by 2050, requiring substantial increases in agricultural productivity while maintaining sustainable resource management. However, crop diseases, soil deterioration, water scarcity, fertilizer abuse, and lack of real-time soil data limit efficient crop production. The objective of this research is to develop an Internet of Things (IoT)- and artificial intelligence (AI)-enabled smart farming system for real-time soil monitoring and crop recommendation. The system integrates NPK, soil moisture, temperature, and humidity sensors with an ATmega328P microcontroller on an Arduino UNO board for field-level data acquisition. Sensor data are transmitted through a&#xa0;MAX485 communication module and an ESP8266 node to a&#xa0;cloud API, where a&#xa0;Random Forest (RF) model analyses soil parameters and provides crop-specific recommendations. Field deployment recorded a&#xa0;temperature of 29 °C, relative humidity of 42%, and soil moisture content of 11%. The soil nutrient content measured nitrogen at 242 mg/kg, phosphorus at 87 mg/kg, and potassium at 121 mg/kg. Based on a&#xa0;calculated potassium deficiency of 179 mg/kg, the system recommended banana cultivation and the application of muriate of potash (MOP) at a&#xa0;dosage of 698.1 kg/ha to address the deficiency. The RF model achieved 96.11% accuracy, 99.02% precision, 98.98% recall, and an F1-score of 98.8%. These findings demonstrate the practical applicability of IoT-AI technologies for site-specific nutrient management and crop selection in fruit production systems, contributing to improved orchard productivity, efficient fertilizer use, and sustainable fruit cultivation.</p>

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Internet of Things- and Machine Learning-Based Soil Parameter Monitoring System for Optimal Crop Recommendation

  • 期刊论文

  • 发表日期：

    2026年4月17日

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  • Venkata Sudhakar Chowdam,
  • M. Venkata Naresh,
  • Ganjikunta Ganesh Kumar

摘要

The global population is expected to exceed 10 billion by 2050, requiring substantial increases in agricultural productivity while maintaining sustainable resource management. However, crop diseases, soil deterioration, water scarcity, fertilizer abuse, and lack of real-time soil data limit efficient crop production. The objective of this research is to develop an Internet of Things (IoT)- and artificial intelligence (AI)-enabled smart farming system for real-time soil monitoring and crop recommendation. The system integrates NPK, soil moisture, temperature, and humidity sensors with an ATmega328P microcontroller on an Arduino UNO board for field-level data acquisition. Sensor data are transmitted through a MAX485 communication module and an ESP8266 node to a cloud API, where a Random Forest (RF) model analyses soil parameters and provides crop-specific recommendations. Field deployment recorded a temperature of 29 °C, relative humidity of 42%, and soil moisture content of 11%. The soil nutrient content measured nitrogen at 242 mg/kg, phosphorus at 87 mg/kg, and potassium at 121 mg/kg. Based on a calculated potassium deficiency of 179 mg/kg, the system recommended banana cultivation and the application of muriate of potash (MOP) at a dosage of 698.1 kg/ha to address the deficiency. The RF model achieved 96.11% accuracy, 99.02% precision, 98.98% recall, and an F1-score of 98.8%. These findings demonstrate the practical applicability of IoT-AI technologies for site-specific nutrient management and crop selection in fruit production systems, contributing to improved orchard productivity, efficient fertilizer use, and sustainable fruit cultivation.