California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach-Reference-Cited by-同舟云学术

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California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach

Published:2019-07-18 Issue: Volume:10 Page:
ISSN:1664-462X
Container-title:Frontiers in Plant Science
language:
Short-container-title:Front. Plant Sci.

Author:

Zhang Zhou,Jin Yufang,Chen Bin,Brown Patrick

Publisher

Frontiers Media SA

Subject

Plant Science

Reference64 articles.

1. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics.;Bolton;Agric. For. Meteorol.,2013

2. Support vector machines with features contribution analysis for agricultural yield prediction.;Brdar;EcoSense,2011

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2. The use of predictive technology to estimate yield from flower counts in high density almond (Prunus dulcis [Mill.] D.A. Webb) orchards;Acta Horticulturae;2024-05

3. Remote Sensing Applications in Almond Orchards: A Comprehensive Systematic Review of Current Insights, Research Gaps, and Future Prospects;Applied Sciences;2024-02-21

4. Comparison of yield characteristics of Independence, a self-compatible almond variety, as affected by the presence of honey bee pollinators;2024-02-02

5. Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction;AgriEngineering;2024-01-22

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