Usage of machine learning methods on precision agriculture applications

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. Vegetation indices derived from multispectral bands of the remote sensing systems carry useful information about the crops 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. For this study, the aim was to explore the usage of machine learning on Precision Agriculture applications and the focus was on olive trees in Manisa, Turkey. Using the spectral band information gathered from an Orange-Cyan-NIR (OCN) camera embedded unmanned aerial vehicle (UAV) system, Normalized Difference Vegetation Index (NDVI) was calculated and the data was preprocessed by segmentating the tree pixels from background based on those values using MiniBatchKMeans algorithm. NDVI, Normalized Nitrogen Index (NNI), Normalized Difference Water Index (NDWI) and tree pixel sizes were selected as optimal features 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 0.95 and accuracy rate of 0.98 respectively, which displayed the importance and usefulness of vegetation indices.