LOW-COST IRRIGATION MANAGEMENT SYSTEM: IMPROVING DATA CONFIDENCE THROUGH ARTIFICIAL INTELLIGENCE

Author:

Cruz Thiago A. C. da1ORCID,Marques Patricia A. A.1

Affiliation:

1. University of São Paulo, Brazil

Publisher

FapUNIFESP (SciELO)

Subject

Agricultural and Biological Sciences (miscellaneous)

Reference32 articles.

1. A review on monitoring and advanced control strategies for precision irrigation;Abioye EA;Computers and Electronics in Agriculture,2020

2. Filling gaps in evapotranspiration measurements for water budget studies: Evaluation of a Kalman filtering approach;Alavi N;Agricultural and Forest Meteorology,2006

3. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56;Allen RG;Fao,1998

4. Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables;Antonopoulos VZ;Computers and Electronics in Agriculture,2017

5. Transfer learning augmented enhanced memory network models for reference evapotranspiration estimation;Bedi J;Knowledge-Based Systems,2022

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