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Multi-Sensor Comparison for Nutritional Diagnosis in Olive Plants: A Machine Learning Approach
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Catarina Manuelito, João de Deus, Miguel Damásio, André Leitão, Luís Alcino Conceição, Rocío Arias-Calderón, Carla Inês, António Manuel Cordeiro, Eduardo Fernandes, Luís Albino, Miguel Barbosa, Filipe Fonseca and José Silvestre
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Abstract
The intensification of olive growing has raised environmental concerns, particularly regarding nutrient loss from excessive fertiliser use. In line with the European Union’s Farm to Fork strategy, which aims to halve the soil nutrient losses by 2030, this study evaluates the effectiveness of
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The intensification of olive growing has raised environmental concerns, particularly regarding nutrient loss from excessive fertiliser use. In line with the European Union’s Farm to Fork strategy, which aims to halve the soil nutrient losses by 2030, this study evaluates the effectiveness of two sensor-based approaches—proximal sensing with a FLAME spectrometer and remote sensing via UAV-mounted multispectral imaging—compared with foliar chemical analyses as the reference standard, for diagnosing the nutritional status of olive trees. The research was conducted in Elvas, Portugal, between 2022 and 2023, across three olive cultivars (‘Azeiteira’, ‘Arbequina’, and ‘Koroneiki’) subjected to different fertilisation regimes. Machine learning (ML) models showed strong correlations between sensor data and nutrient levels: the multispectral sensor performed best for phosphorus (P) (determination coefficient [
] = 0.75) and potassium (K) (
= 0.73), while the FLAME spectrometer was more accurate for nitrogen (N) (
= 0.64). These findings underscore the potential of sensor-based technologies for non-destructive, real-time nutrient monitoring, with each sensor offering specific strengths depending on the target nutrient. This work contributes to more sustainable and data-driven fertilisation strategies in precision agriculture.
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