An efficient spectral band selection trustworthy Machine Learning (ML) framework for vineyard monitoring from UAV hyperspectral data is introduced. The UAV, equipped with Specim AFX-10, is used to capture data beyond the visible spectrum within the $400-1000$ nm wavelength range for a total of 224 bands. Popular supervised Machine Learning (ML) algorithms are utilized for detecting vegetation canopy in vineyards and distinguishing it from existing land uses, namely ground and shadow. Explainable AI (XAI) results accompany those from ML to identify the most important bands, and understand the contribution of their reflectance levels to the ML models. By doing so, the number of spectral bands is narrowed while maintaining the granularity of the HS data. Experimental results on UAVINE, a publicly available dataset within the context of this work, demonstrate excelling classification performance of Random Forest (RF) with an overall accuracy of 97.06%, and with precision, recall and F1-scores following accordingly. With the use of the computationally efficient Tree SHAP algorithm applied on the RF, the bands B106 (677nm- Red), B186 (897nm- NIR) and B211 (967nm- NIR) and B39 (498nm- Green) were identified as the most important ones to the model, enabling better visualization of the vineyard and band-based analysis for each one of the classes existing within the vineyard.

Viticulture requires constant and meticulous care throughout the year to ensure a high-quality harvest. Precision agriculture has significantly advanced, enabling the early detection of potential threats or diseases without harming the crops. Understanding the distinct features of various grapevine varieties, such as chlorophyll content, canopy growth, stress, and interactions with specific soil elements, is crucial. To address these challenges, this study employs multispectral images captured by unmanned aerial vehicles (UAVs), providing a method for exploiting the spectral features of the vine canopies on a field with numerous vine varieties. The primary objective is to cluster different grapevine varieties in the same area based on common spectral characteristics.