Crop field monitoring and damage assessment with unmanned aircraft systems and machine learning

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University of New Brunswick


The purpose of this dissertation is to examine and develop novel Machine Learning (ML) pipelines for crop field monitoring and damage assessment using Unmanned Aircraft Systems (UAS) equipped with a multispectral MicaSense RedEdge optical sensor for precision agriculture and insurance purposes. The crop fields were prepared for or planted with barley, corn, potato, oat, and soybean crops. The multispectral imagery from the UAS was radiometrically corrected and mosaicked. The multispectral reflectance orthomosaics from each surveyed field were used as input features in various algorithms along with associated vegetation index rasters. Firstly, field areas and boundaries were delineated over multiple bare soil fields with the two following ML pipelines: A supervised pixel-based Random Forests (RF) classifier and an unsupervised clustering process using the Mean Shift algorithm. The vectorization process of the resulting maps resulted in mean Area Goodness of Fit (AGoF) greater than 99% and mean Boundary Mean Positional Error (BMPE) lower than 0.6 m, indicating that both ML pipelines are excellent. Secondly, fully planted fields with barley, corn, and oat were surveyed in order to delineate crop areas and boundaries using Pixel-Based Image Analysis (PBIA) and Geographic Object-Based Image Analysis (GEOBIA) with the RF classifier. Both methodologies were highly successful, with a mean AGoF greater than 98% and a mean BMPE lower than 0.8 m. Thirdly, lodging damage on barley crop fields was mapped from two UAS surveys. An RF model was utilized in order to classify lodged and standing barley with an overall validation accuracy of 99.7%. The average AGoF was 97.95%, and the average BMPE was 0.235 m. Finally, the crop health status was assessed through the Green Area Index (GAI) for barley and oat fields. Multiple Linear Models, Support Vector Machines, RF, and Artificial Neural Networks regression algorithms were used in order to produce Green Area Index (GAI) maps of the fields, with RF performing best for GAI prediction. The GAI maps and the regression feature space were used with an RF classifier to generate health status maps of the crop fields with a mean overall accuracy of 94%.