首页|Study Findings from University of Tras-os-Montes e Alto Douro Advance Knowledge in Machine Learning (Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction)
Study Findings from University of Tras-os-Montes e Alto Douro Advance Knowledge in Machine Learning (Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction)
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Investigators publish new report on artificial intelligence. According to news originating from Vila Real, Portugal, by NewsRx correspondents, research stated, “Almonds are becoming a central element in the gastronomic and food industry worldwide. Over the last few years, almond production has increased globally.” Financial supporters for this research include Fct-portuguese Foundation For Science And Technology; Doctoral Programme “agricultural Production Chains-from Fork To Farm”; European Social Funds; Regional Operational Programme Norte 2020; Citab Research Unit; Inov4agro; Cimo. Our news journalists obtained a quote from the research from University of Tras-os-Montes e Alto Douro: “Portugal has become the third most important producer in Europe, where this increasing trend is particularly evident. However, the susceptibility of almond trees to changing climatic conditions presents substantial risks, encompassing yield reduction and quality deterioration. Hence, yield forecasts become crucial for mitigating potential losses and aiding decisionmakers within the agri-food sector. Recent technological advancements and new data analysis techniques have led to the development of more suitable methods to model crop yields. Herein, an innovative approach to predict almond yields in the Tras-os- Montes region of Portugal was developed, by using machine learning regression models (i.e., the random forest regressor, XGBRegressor, gradient boosting regressor, bagging regressor, and AdaBoost regressor), coupled with remote sensing data obtained from different satellite platforms. Satellite data from both proprietary and free platforms at different spatial resolutions were used as features in the study (i.e., the GSMP: 11.13 km, Terra: 1 km, Landsat 8: 30 m, Sentinel-2: 10 m, and PlanetScope: 3 m). The best possible combination of features was analyzed and hyperparameter tuning was applied to enhance the prediction accuracy. Our results suggest that high-resolution data (PlanetScope) combined with irrigation information, vegetation indices, and climate data significantly improves almond yield prediction.”
University of Tras-os-Montes e Alto DouroVila RealPortugalEuropeCyborgsEmerging TechnologiesMachine LearningRemote Sensing
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Mdpi
