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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Predicting Grape Sugar Content under Quality Attributes Using Normalized Difference Vegetation Index Data and

Aikaterini Kasimati1, Borja Espejo-García1, Nicoleta Darra1

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Automated machine learning (AutoML) combined with Normalized Difference Vegetation Index (NDVI) data from Unmanned Aerial Vehicle (UAV) and Spectrosense+ GPS sensors accurately predicts wine grape quality. This approach offers improved efficiency and potential for long-term performance in viticulture.

Keywords:
AutoMLBayesian optimizationNDVIcorrelationensemble methodsquality predictionsugars

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Area of Science:

  • Precision viticulture
  • Agricultural remote sensing
  • Machine learning in agriculture

Background:

  • Accurate wine grape quality and yield prediction are crucial for viticulture.
  • Non-destructive sensing methods, including spectral vegetation indices (VIs), are vital for site-specific crop monitoring.
  • Traditional laboratory analysis for grape quality is time-consuming and costly, driving the need for advanced predictive techniques.

Purpose of the Study:

  • To develop a robust approach for predicting grape quality attributes using automated machine learning (AutoML).
  • To combine open-source AutoML with Normalized Difference Vegetation Index (NDVI) data from multiple platforms (proximal sensors, UAV, Sentinel-2).
  • To compare the performance of AutoML with manually fine-tuned machine learning methods for grape quality prediction.

Main Methods:

  • Utilized Normalized Difference Vegetation Index (NDVI) data from vehicle-mounted sensors, Unmanned Aerial Vehicle (UAV) orthomosaics, and Sentinel-2 imagery.
  • Applied open-source automated machine learning (AutoML) techniques, including regression models (Ordinary Least Square, Theil-Sen, Huber) and tree-based methods.
  • Investigated Support Vector Machines (SVMs) and Automatic Relevance Determination (ARD), comparing various sensor combinations and data across two growing seasons (2019-2020).

Main Results:

  • Unmanned Aerial Vehicle (UAV) and Spectrosense+ GPS data showed promising performance in predicting grape sugars, particularly during mid to late season.
  • AutoML regression models achieved slightly improved prediction accuracy (R² = 0.65) compared to manually fine-tuned models (R² = 0.61).
  • Combining multiple sensors and growth stages across both seasons further enhanced prediction accuracy, with UAV and Spectrosense+ GPS data yielding the highest R² values.

Conclusions:

  • Automated machine learning (AutoML) demonstrates significant potential for robust and efficient grape quality prediction in viticulture.
  • Integrating data from Unmanned Aerial Vehicle (UAV) and proximal sensors provides valuable insights for mid to late-season grape quality assessment.
  • A balance between expert-driven manual methods and AutoML is recommended to optimize crop quality prediction efficiency and long-term performance.