Sensors 2013, 13(9), 12698-12743; doi:10.3390/s130912698

Crop Biometric Maps: The Key to Prediction

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Received: 24 June 2013; in revised form: 6 September 2013 / Accepted: 17 September 2013 / Published: 23 September 2013
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2013)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: The sustainability of agricultural production in the twenty-first century, both in industrialized and developing countries, benefits from the integration of farm management with information technology such that individual plants, rows, or subfields may be endowed with a singular “identity.” This approach approximates the nature of agricultural processes to the engineering of industrial processes. In order to cope with the vast variability of nature and the uncertainties of agricultural production, the concept of crop biometrics is defined as the scientific analysis of agricultural observations confined to spaces of reduced dimensions and known position with the purpose of building prediction models. This article develops the idea of crop biometrics by setting its principles, discussing the selection and quantization of biometric traits, and analyzing the mathematical relationships among measured and predicted traits. Crop biometric maps were applied to the case of a wine-production vineyard, in which vegetation amount, relative altitude in the field, soil compaction, berry size, grape yield, juice pH, and grape sugar content were selected as biometric traits. The enological potential of grapes was assessed with a quality-index map defined as a combination of titratable acidity, sugar content, and must pH. Prediction models for yield and quality were developed for high and low resolution maps, showing the great potential of crop biometric maps as a strategic tool for vineyard growers as well as for crop managers in general, due to the wide versatility of the methodology proposed.
Keywords: precision farming; global positioning; yield prediction; crop monitoring; vineyard management; precision viticulture; agricultural robotics; information technology
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MDPI and ACS Style

Rovira-Más, F.; Sáiz-Rubio, V. Crop Biometric Maps: The Key to Prediction. Sensors 2013, 13, 12698-12743.

AMA Style

Rovira-Más F, Sáiz-Rubio V. Crop Biometric Maps: The Key to Prediction. Sensors. 2013; 13(9):12698-12743.

Chicago/Turabian Style

Rovira-Más, Francisco; Sáiz-Rubio, Verónica. 2013. "Crop Biometric Maps: The Key to Prediction." Sensors 13, no. 9: 12698-12743.

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