Browsing by Author "Guimaraes, Nathalie"
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- Almond cultivar identification using machine learning classifiers applied to UAV-based multispectral dataPublication . Guimaraes, Nathalie; Padua, Luis; Sousa, Joaquim J.; Bento, Albino; Couto, PedroIn Portugal, almonds are a very important crop, due to their nutritional properties. In the northeastern part of the country, the almond sector has endured over time, with strong cultural traditions and key economic significance. In these areas, several cultivars are used. In effect, the presence of various almond cultivars implies differentiated management in irrigation, disease control, pruning system, and harvest planning. Therefore, cultivar classification is essential over large agricultural areas. Over the last decades, remote-sensing data have led to important breakthroughs in the classification of different cultivars for several crops. Nonetheless, for almonds, studies are incipient. Thus, this study aims to fill this knowledge gap and explore the classification of almond cultivars in an almond orchard. High-resolution multispectral data were acquired by an unmanned aerial vehicle (UAV). Vegetation indices (VIs) and tree structural parameters were, subsequently, estimated. To obtain an accurate cultivar identification, four machine learning classifiers, such as K-nearest neighbour (kNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were applied and optimized through the fine-tuning process. The accuracy of machine learning classifiers was analysed. SVM and RF performed best with OAs of 76% and 74% using VIs and spectral bands (GREEN, GRVI, GN, REN, ClRE). Adding the canopy height model (CHM) improved performance, with RF and XGBoost having OAs of 88% and 84%. kNN performed worst with an OA of 73% using only VIs and spectral bands, 80% with VIs, spectral bands and CHM, and 93% with VIs, CHM, and tree crown area (TCA). The best performance was achieved by RF and XGBoost with OAs of 99% using VIs, CHM, and TCA. These results demonstrate the importance of the feature selection process. Moreover, this study reveals the feasibility of remote-sensing data and machine learning classifiers in the classification of almond cultivars.
- Almond orchard management using multi-temporal UAV data: a proof of conceptPublication . Guimaraes, Nathalie; Padua, Luis; Sousa, Joaquim J.; Bento, Albino; Couto, PedroIn the last decade Unmanned Aerial Systems (UAS) have become a reference tool for agriculture applications. The integration of multispectral sensors that can capture near infrared (NIR) and red edge spectral reflectance allows the creation of vegetation indices, which are fundamental for crop monitoring process. In this study, we propose a methodology to analyze the vegetative state of almond crops using multi-temporal data acquired by a multispectral sensor accoupled to an Unmanned Aerial Vehicle (UAV). The methodology implemented allowed individual tree parameters extraction, such as number of trees, tree height, and tree crown area. This also allowed the acquisition of Normalized Difference Vegetation Index (NDVI) information for each tree. The multitemporal data showed significant variations in the vegetative state of almond crops.