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<p>The research proposes a smart soil and environmental recommendation system for sustainable arecanut cultivation in India, utilizing IoT, sensing technologies, and machine learning. Arecanuts are crucial for India's economic, cultural, religious, and social life, producing 'betel nut'. However, challenges like nutrient cycles and environmental conditions impact their cultivation. The proposed methodology integrates Wireless Sensor Networks (WSN) with IoT to monitor key environmental factors influencing arecanut growth. A conditional intensity model predicts nutrient deficiencies, while an Environmental Event Dependency Model captures dynamic relationships among environmental factors. The Adaptive Trend Detection Model (ATDM) uses an AI-based model for early detection of soil anomalies. A smart irrigation system is developed to manage water use efficiently based on real-time soil moisture data. The approach, tested with 100 sensor nodes across a 50&#xa0;m × 50&#xa0;m sensor field, demonstrated effective monitoring and precise control over soil moisture, nutrient levels, and environmental conditions. Results showed improved irrigation efficiency, reduced water usage by over 20%, and optimal nutrient management, promoting better yield and resource conservation. This study underscores the potential of integrating IoT, smart sensing, and data-driven decision-making for precision agriculture and sustainable arecanut cultivation. </p>

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Developing a Soil and Environmental Recommendation System with Smart Irrigation for Sustainable Arecanut Cultivation

  • 期刊论文

  • 发表日期：

    2025年6月23日

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  • H. S. Naveen,
  • E. Saravana Kumar

摘要

The research proposes a smart soil and environmental recommendation system for sustainable arecanut cultivation in India, utilizing IoT, sensing technologies, and machine learning. Arecanuts are crucial for India's economic, cultural, religious, and social life, producing 'betel nut'. However, challenges like nutrient cycles and environmental conditions impact their cultivation. The proposed methodology integrates Wireless Sensor Networks (WSN) with IoT to monitor key environmental factors influencing arecanut growth. A conditional intensity model predicts nutrient deficiencies, while an Environmental Event Dependency Model captures dynamic relationships among environmental factors. The Adaptive Trend Detection Model (ATDM) uses an AI-based model for early detection of soil anomalies. A smart irrigation system is developed to manage water use efficiently based on real-time soil moisture data. The approach, tested with 100 sensor nodes across a 50 m × 50 m sensor field, demonstrated effective monitoring and precise control over soil moisture, nutrient levels, and environmental conditions. Results showed improved irrigation efficiency, reduced water usage by over 20%, and optimal nutrient management, promoting better yield and resource conservation. This study underscores the potential of integrating IoT, smart sensing, and data-driven decision-making for precision agriculture and sustainable arecanut cultivation.