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Interpretability of artificial intelligence models that use data fusion to pbkp_redict yield in aeroponics

Abstract:

There is an increasing demand for healthy and fresh foods, and pbkp_redicting yield effectively is important to improve production, especially in methods like aeroponics. This paper has two main goals: (i) use data fusion to improve yield pbkp_rediction in aeroponics, and (ii) find which features are more relevant for yield pbkp_rediction of six different crops. To reach these goals, a number of artificial intelligence models and an interpretability analysis based on SHapley Additive exPlanations (SHAP) have been implemented. The models were trained using 200 samples that were collected in a nine-month period, including information from different air and water quality sensors in addition to manually recorded data, reaching in the end a coefficient of determination value R2 = 0.752 for the validation dataset in the best case (CNN-based model). As a result, two main features were identified in the dataset: Room CO2 and Reservoir Temperature, along with other useful insights of how these features influence pbkp_redictions. SHAP values also provided important information for feature selection. These results could be the first steps towards the full automation of an aeroponics crop production system.