首页|New Machine Learning Data Have Been Reported by Researchers at University of Cal ifornia Davis (Mapping Almond Stem Water Potential Using Machine Learning and Mu ltispectral Imagery)
New Machine Learning Data Have Been Reported by Researchers at University of Cal ifornia Davis (Mapping Almond Stem Water Potential Using Machine Learning and Mu ltispectral Imagery)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of Davis, California, by NewsRx editors, research stated, "Almonds are a major crop in California which p roduces 80% of all the world's almonds. Widespread drought and str ict groundwater regulations pose significant challenges to growers." Financial supporters for this research include National Institute of Food and Ag riculture, Almond Board of California Grant Project, USDA NIFA Award, AI Institu te for Next Generation Food Systems at UC Davis. Our news journalists obtained a quote from the research from the University of C alifornia Davis, "Irrigation regimes based on observed crop water status can hel p to optimize water use efficiency, but consistent and accurate measurement of w ater status can prove challenging. In almonds, crop water status is best represe nted by midday stem water potential measured using a pressure chamber, which des pite its accuracy is impractical for growers to measure on a regular basis. This study aimed to use machine learning (ML) models to predict stem water potential in an almond orchard based on canopy spectral reflectance, soil moisture, and d aily evapotranspiration. Both artificial neural network and random forest models were trained and used to produce high-resolution spatial maps of stem water pot ential covering the entire orchard. Also, for each ML model type, one model was trained to predict raw stem water potential values, while another was trained to predict baseline-adjusted values. Together, all models resulted in an average c oefficient of correlation of R2 = 0.73 and an average root mean squared error (R MSE) of 2.5 bars. Prediction accuracy decreased significantly when models were e xpanded to spatial maps (R2 = 0.33, RMSE = 3.31 [avg] ). These results indicate that both artificial neural networks and random forest frameworks can be used to predict stem water potential, but both approaches wer e unable to fully account for the spatial variability observed throughout the or chard. Overall, the most accurate maps were produced by the random forest model (raw stem water potential R2 = 0.47, RMSE = 2.71). The ability to predict stem w ater potential spatially can aid in the implementation of variable rate irrigati on."
DavisCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversit y of California Davis
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