Machine Learning-Based Prediction and Multispectral Analysis for Precision Irrigation Management

Abstract

The objective of this work is to build a prediction system for normalized indices such as NDVI (Normalized Difference Vegetation Index), NDRE (Normalized Difference RedEdge index) and NDWI (Normalized Difference Water Index). Based on machine learning techniques, this prediction will allow us to compare various methods. Additionally, this prediction will allow us to precisely comprehend these three indices with a small amount of data. Multiple machine learning algorithms were trained and evaluated using appropriate parameters. For NDRE and NDWI prediction, the Support Vector Machine approach produced good results with Mean Squared Errors (MSE) of 0.0006 and 0.0012, respectively. On the other hand, the Random Forest approach performed better with a lower MSE of 0.0033 for predicting NDVI. Furthermore, patterns and trends in crop health, nutrient needs and water requirements were found by clustering analysis. The process of calculating and importing indices from TIFF data was made easier with the creation of a Graphical User Interface (GUI). The system provides an innovative approach for irrigation management, that support farmers in making well-informed decisions regarding irrigation and crop health.