A commendable classification was made at 8 DAA where the random trees classifier attained an accuracy of 87.2%. The maximum likelihood classifier provided the highest performing result out of all classification methods with an accuracy of 75.2%. The random trees classifier performed the best at 4 days after application (DAA) for kochia with 62.9% accuracy. The classification accuracy between the sensors and classification methods of maximum likelihood, random trees, and Support Vector Machine (SVM) were compared. The data were processed in ArcGIS for raster classification as well as spectral comparison of glyphosate-resistant and glyphosate-susceptible weeds. The objective of this study was to identify herbicide resistance after glyphosate application in true field conditions by analyzing the UAV-acquired thermal and multispectral response of kochia, waterhemp, redroot pigweed, and common ragweed. Unmanned Aerial Vehicle (UAV)-assisted thermal and multispectral remote sensing has potential for detecting biophysical characteristics of weed biotypes during the growing season, which includes distinguishing glyphosate-susceptible and glyphosate-resistant weed populations based on canopy temperature and deep learning driven weed identification algorithms. In order to provide a cost-effective weed management strategy that does not promote glyphosate-resistant weed biotypes, differences between resistant and susceptible biotypes have to be identified accurately in the field conditions. However, dependency on the effectiveness of herbicide practices has led to overuse through continuous growth of crops resistant to a single mode of action. The foundation of contemporary weed management practices in many parts of the world is glyphosate. The canopy temperature-based classification of susceptible and resistant weeds resulted in the discrimination accuracies of 88%, 93% and 92% in glyphosate-resistant kochia, waterhemp and ragweed, respectively. The lowest accuracy and Kappa coefficient (67% and 0.58) were observed for pixel-based Mahalanobis Distance algorithm. The object-based algorithms developed with mosaicked imagery effectively classified weed species with the overall accuracy and Kappa coefficient values greater than 86% and 0.77, respectively. Thermal infrared imagery was also used to assess the canopy temperature variance within the weed species to identify the glyphosate-resistance status in detected weeds. The accuracy of six classification algorithms namely Parallelepiped, Mahalanobis Distance, Maximum Likelihood, Spectral Angle Mapper, Support Vector Machine and Decision Tree implemented at pixel and object-based levels in weed species classification were evaluated. A field experiment was conducted to evaluate supervised classification methods to identify three-weed species including waterhemp (Amaranthus rudis), kochia (Kochia scoparia), and ragweed (Ambrosia artemisiifolia L.). This study focuses on validating the potential application of high resolution multispectral and thermal UAS images in classification of weed species and glyphosate-resistant weeds at early phenological stages. Accurate weed mapping in early growing season is an essential step in the site-specific weed management system.

Gpx viewer mikrokopter