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A novel approach to identify the spectral bands that pbkp_redict moisture content in canola and wheat

Abstract:

Due to the relevance of agriculture in economy and human development, the inclusion of technology in this activity is of utmost importance, and moisture content pbkp_rediction is relevant for assessing the degree of maturity of a crop, which relates to efficient harvesting and quality control. This paper presents an accurate deep learning model for the pbkp_rediction of the moisture content of canola and wheat crops, based on hyperspectral images taken by several drone flights. This model serves as the starting point for a supervised band selection process that involves a novel approach based on a game-theory model-interpretability analysis. The deep learning model for moisture content pbkp_rediction included a final ensemble of two branches for analysis of spatial and spectral features, and it reached a coefficient of determination of 0.916 and 0.818 for the canola and wheat test datasets, respectively. SHapley Additive exPlanations analysis allowed us to study the individual pbkp_redictions of the models, which is the most important contribution of this paper because this approach could eventually lead to the design and implementation of more tailored software and hardware for the analysis of spectral information. The obtained results validate the idea that using this approach actually obtains the spectral bands that are important for this task, since they are similar to PCA results, and they fall on the NIR part of the spectrum, which is widely used in moisture measurement of agricultural products and vegetation analysis.