Frontiers | California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach | Plant Science

Frontiers | California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach | Plant Science

California’s almond growers face challenges with nitrogen management as new legislatively mandated nitrogen management strategies for almond have been implemented. These regulations require that growers apply nitrogen to meet, but not exceed, the annual N demand for crop and tree growth and nut production. To accurately predict seasonal nitrogen demand, therefore, growers need to estimate block-level almond yield early in the growing season so that timely N management decisions can be made. However, methods to predict almond yield are not currently available. To fill this gap, we have developed statistical models using the Stochastic Gradient Boosting, a machine learning approach, for early season yield projection and mid-season yield update over individual orchard blocks. We collected yield records of 185 orchards, dating back to 2005, from the major almond growers in the Central Valley of California. A large set of variables were extracted as predictors, including weather and orchard

1 mentions: @ChristophMolnar
Date: 2020/09/14 08:21

Referring Tweets

@ChristophMolnar 4/n Forecasting almond yield ML model: Random Forest Target: Almond yield Features: Field data, weather, satellite data IML: Feature importance, Partial dependence plots, decision tree t.co/o3Ddm4U7ov t.co/mvaayjJpS1

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