Scholarlink

The Internet of Things has transformed the face of farming by making old-style farming smart farming. Despite experts recognizing its value, only a few studies have explored the role IoT can play in making farming sustainable and helping the climate. The paper focuses on a smart farming model and explores how it would fit in the United Nations Sustainable Development Goals. This research identifies that smart farming contributes to different goals that include Goal 1: Clean Water and Sanitation, Goal 2: Cheap and Clean Energy, Goal 3: Good Jobs and Growth, Goal 4: New Ideas and Infrastructure, Goal 5: Sustainable Cities, and Goal 6: Smart Use of Resources. The study explores the challenges faced during application of IoT in farming. IoT systems can increase production, reduce wastage, and are environmentally friendly. However, issues like cost, difficulty in setup, connectivity problems, and data security are major concerns. These problems could be overcome by farmers, technology companies, and the government working in tandem. In addition, the paper does an in-depth study about the efficient use of resources like water, fertilizer, and energy through IoT-enabled sensors. Through the sensors, it becomes possible to monitor the moisture level of soil and analyze the trends of weather for improvement in irrigation and fertilization, thereby reducing waste and conserving resources. IoT sensors facilitate farmers in monitoring weather conditions and crop health to take informed decisions and reduce risks related to crop diseases and pests. The paper also highlights the efficiency of the Decision Tree Regressor (DTR) model in predicting agricultural outcomes, specifically in aquaculture. The model showed superior performance with a Mean Absolute Error (MAE) of 0.76 and Root Mean Squared Error (RMSE) of 1.51 for predicting fish length, and MAE of 12.71 and RMSE of 33.33 for predicting fish weight. The DTR outperformed other models in terms of accuracy, demonstrating its potential to enhance farming practices by offering reliable predictions. The paper also presents various solutions on IoT, including GPS-based remote-controlled monitoring, moisture and temperature sensing, intruder detection, and appropriate irrigation facilities. These solutions use wireless sensor networks to monitor soil properties and environmental factors in real-time, enabling farmers to make informed decisions for better productivity and sustainability.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine Learning Approaches for Predictive Modeling in Wireless Sensor Networks for Soil and Environmental Monitoring

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Shefali Gupta,
  • Reema Thareja,
  • Goransh R. Thareja

摘要

The Internet of Things has transformed the face of farming by making old-style farming smart farming. Despite experts recognizing its value, only a few studies have explored the role IoT can play in making farming sustainable and helping the climate. The paper focuses on a smart farming model and explores how it would fit in the United Nations Sustainable Development Goals. This research identifies that smart farming contributes to different goals that include Goal 1: Clean Water and Sanitation, Goal 2: Cheap and Clean Energy, Goal 3: Good Jobs and Growth, Goal 4: New Ideas and Infrastructure, Goal 5: Sustainable Cities, and Goal 6: Smart Use of Resources. The study explores the challenges faced during application of IoT in farming. IoT systems can increase production, reduce wastage, and are environmentally friendly. However, issues like cost, difficulty in setup, connectivity problems, and data security are major concerns. These problems could be overcome by farmers, technology companies, and the government working in tandem. In addition, the paper does an in-depth study about the efficient use of resources like water, fertilizer, and energy through IoT-enabled sensors. Through the sensors, it becomes possible to monitor the moisture level of soil and analyze the trends of weather for improvement in irrigation and fertilization, thereby reducing waste and conserving resources. IoT sensors facilitate farmers in monitoring weather conditions and crop health to take informed decisions and reduce risks related to crop diseases and pests. The paper also highlights the efficiency of the Decision Tree Regressor (DTR) model in predicting agricultural outcomes, specifically in aquaculture. The model showed superior performance with a Mean Absolute Error (MAE) of 0.76 and Root Mean Squared Error (RMSE) of 1.51 for predicting fish length, and MAE of 12.71 and RMSE of 33.33 for predicting fish weight. The DTR outperformed other models in terms of accuracy, demonstrating its potential to enhance farming practices by offering reliable predictions. The paper also presents various solutions on IoT, including GPS-based remote-controlled monitoring, moisture and temperature sensing, intruder detection, and appropriate irrigation facilities. These solutions use wireless sensor networks to monitor soil properties and environmental factors in real-time, enabling farmers to make informed decisions for better productivity and sustainability.