Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

Document Outline

• Introduction 
• Materials and Methods 
  • Data Sources 
  • Machine Learning Task Design 
    • Task 1—Forecasting Future Data (Istat Dataset) 
    • Task 2—Comparison between Machine Learning Algorithms on Missing Data (CNR Scientific Dataset) 
    • Task 3—Reconstruction of Missing Data from Monitoring Stations Exploiting Neural Network, and Linear and Polynomial Regression models (IoT Sensors Dataset) 
    • Task 4—Reconstruction of Missing Data from Monitoring Stations Exploiting the Decision Tree, and Polynomial and K-Nearest Neighbors (KNN) Models (IoT Sensors Dataset) 
    • Task 5—Detection of Faulty Monitoring Stations by Sensor Values (IoT Sensors Dataset) 
  • Software Tools 
    • Task 1 (Istat Dataset) Components 
    • Task 2 (CNR Scientific Dataset) 
    • Task 3 (IoT Sensors Dataset) Components 
    • Task 4 (IoT Sensors Dataset) 
    • Task 5 (IoT Sensors Dataset) Components 
• Results and Discussion 
  • Task 1—Forecast of Future Data (Istat Dataset—Results) 
  • Task 2—Comparison between Machine Learning Algorithms on Missing Data (CNR Dataset—Results) 
  • Task 3—Reconstruction of Missing Data from Monitoring Stations Exploiting Neural Network, and Linear and Polynomial Regression Techniques (IoT Dataset—Results) 
  • Task 4—Reconstruction of Missing Data from Monitoring Stations Exploiting Decision Tree, Polynomial Model, and KNN (IoT Dataset—Results) 
  • Task 5—Detection of Faulty Monitoring Stations by Analyzing Their Sensor Values (IoT Dataset—Results) 
• Conclusions 
• References