Usage of Machine Learning Algorithms on Precision Agriculture Applications

Abstract

Agricultural monitoring and analysis of data to be used in management decisions to increase the quality, profitability, sufficiency, continuity and efficiency of agricultural production is called Precision Agriculture.[1]Precision Agriculture technologies aim to help the farmers with the decision making process by providing them information and control over their land, crop status and environment using remote sensing systems. Remote sensing systems use multispectral cameras to gather information, which filter different wavelengths of light in separate bands. Vegetation indices derived from the spectral bands of the remote sensing systems carry useful information about crop characteristics such as nitrogen content, chlorophyll content and water stress which supports the farmers to plan irrigation and pesticide spraying processes without the need of manual examination, providing a cost and time-efficient solution. This study aims to explore three specific Precision Agriculture applications, such as crop segmentation, illness detection and yield prediction on olive trees in Manisa, Turkey by using machine learning algorithms. Using the spectral band information gathered from an Orange-Cyan-NIR (OCN) camera embedded UAV system, vegetation health index was calculated and the data was preprocessed by segmentating the tree pixels from background based on those values using MiniBatchKMeans algorithm. Optimal features were selected based on accuracy comparison for yield and disease predictions. A Decision Tree Regressor (DTR) model was trained for yield prediction while a Random Forest Classifier (RFC) model was trained for disease prediction. The results showed that crop segmentation had an accuracy rate of 0.85-0.95, while DTR and RFC models had an R2 score of 0.99 and accuracy rate of 0.98 respectively, which displayed the importance and usefulness of vegetation indices.