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Light Acquisition02:16

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Crop biometric maps: the key to prediction.

Francisco Rovira-Más1, Verónica Sáiz-Rubio

  • 1Agricultural Robotics Laboratory, Universidad Politécnica de Valencia, Camino de Vera s/n 3F, Valencia 46022, Spain. frovira@dmta.upv.es.

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Summary

Crop biometrics integrates farm management with IT for sustainable agriculture. This study introduces crop biometric maps, using specific traits to predict yield and quality in vineyards.

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Area of Science:

  • Agricultural Science
  • Information Technology
  • Geospatial Analysis

Background:

  • Sustainable agriculture requires integrating farm management with information technology.
  • Variability in nature necessitates advanced analytical approaches for agricultural production.
  • Crop biometrics is defined as the scientific analysis of agricultural observations in defined spaces for prediction modeling.

Purpose of the Study:

  • To develop the principles of crop biometrics.
  • To discuss the selection and quantization of biometric traits.
  • To analyze mathematical relationships for prediction models.

Main Methods:

  • Defined crop biometrics principles and trait selection.
  • Quantified biometric traits including vegetation amount, soil compaction, and grape composition.
  • Developed prediction models using high and low-resolution crop biometric maps.

Main Results:

  • Applied crop biometric maps to a vineyard case study.
  • Selected traits included vegetation, altitude, soil compaction, berry size, yield, pH, and sugar.
  • Developed prediction models for grape yield and enological potential (quality index).

Conclusions:

  • Crop biometric maps offer a versatile strategic tool for vineyard growers and crop managers.
  • The methodology demonstrates significant potential for improving agricultural production management.
  • Integration of precise measurements and predictive modeling enhances farm management efficiency and sustainability.