Noack, BenjaminHerzog, AndreasMenz, PatrickElkhouly, Ahmed MagdyAhmed MagdyElkhouly2023-10-182023-10-182023-03-30https://publica.fraunhofer.de/handle/publica/451908Currently, one of the biggest challenges in the agricultural field is the identification and classification of plant health-related problems, specifically stress caused by drought. Consequently, analyzing the ground field-based hyperspectral data covering the visible, near-infrared and mid-infrared ranges of the electromagnetic spectrumprovides us with valuable information about the plant’s physiological conditions even before any significant changes in its physical appearance (shape or colour) are observed. Consequently, hyperspectral technology has become increasingly crucial for emerging agricultural applications. The thesis focus on how these emerging technologies (hyperspectral field-based sensing and machine learning) are being utilized to solve these challenging issues. The hyperspectral sensing applications, as well as the machine learning various techniques, have helped a lot in investigating the plant’s health, classification of the vegetation or crop types, plant’s biophysical detection (e.g., biomass), disease and stress detection (e.g., drought) and also nutrition components detection (e.g., Nitrogen). Themain focus of themaster’s thesis is integrating the hyperspectral sensed data with these multiple data resources (e.g., photosynthesis traits, metabolic traits and water uptake traits). Following that, producing more consistent and valuable information than the hyperspectral sensed data provided independently, leading to improving the classification of stressed plants caused by drought. In this way, we can increase crop health by improving the accuracy of the built machine learning algorithm by providing it with more information about the drought-stressed vs treated barley plants. In other words, we improve the machine learning algorithm’s accuracy by combining the physiological laboratory measurements with the field-based hyperspectral data of barley plants. Using means of feature engineering (e.g., Sequential Forward Selection and Sequential Backward Elimination) to choose the added features as well as feature extraction techniques(PCA,t-SNE) to overcome the curse of dimensionality of the hyperspectral imaging data and accurately represent it. Initially, the thesis research involved performing a preprocessing phase to ensure the samples’ quality and scaling the dataset before executing any predictions. Additionally, selecting the best-performing machine learning models based on the group cross-validation (Leave-one-out) method results. Moreover, evaluating the performance of the model’s predictions after adding these measurements, compared to the model with the monomodal hyperspectral data for the classification task.enMulti-modal featuresspectralmachine learningmaster thesis