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

Zhang, Zhou; Jin, Yufang; Чэн, Бин; Brown, Patrick H.

doi:10.3389/fpls.2019.00809

Cited by 54 publications

(60 citation statements)

References 50 publications

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“…However, the tabular style dataset used in this analysis has no spatial-related patterns for the convolutional kernels of CNN, although the sequential features might have favored LSTM, explaining its slightly better performance over CNN. This observation is consistent with previous studies (Wang et al 2017, Fernández-Delgado et al 2019, Zhang et al 2019.…”

Section: Comparison Of Machine Learning Models and Featuressupporting

confidence: 94%

“…In recent years, machine learning algorithms, especially deep neural networks, have received increased attention given their ability to describe complex relationships (Yang et al 2019, Zhong et al 2019. Compared to statistical models, machine learning algorithms require no prior assumption about the relationships between response and predictor variables and allow for higher-order interactions, resulting in improved predictive power , Zhang et al 2019. Several studies have examined the performance of machine learning algorithms for county or state/province-level crop yield estimation mainly using climate variables, VIs, and Land Surface Temperature (LST) derived from satellite with mixed results (Johnson 2014, Kuwata and Shibasaki 2015, You et al 2017, Wang et al 2018, Kaneko et al 2019.…”

Section: Introductionmentioning

confidence: 99%

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Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

Kang

Özdoğan

Zhu

et al. 2020

Environ. Res. Lett.

132

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Crop yield estimates over large areas are conventionally made using weather observations, but a comprehensive understanding of the effects of various environmental indicators, observation frequency, and the choice of prediction algorithm remains elusive. Here we present a thorough assessment of county-level maize yield prediction in U.S. Midwest using six statistical/machine learning algorithms (Lasso, Support Vector Regressor, Random Forest, XGBoost, Long-short term memory (LSTM), and Convolutional Neural Network (CNN)) and an extensive set of environmental variables derived from satellite observations, weather data, land surface model results, soil maps, and crop progress reports. Results show that seasonal crop yield forecasting benefits from both more advanced algorithms and a large composite of information associated with crop canopy, environmental stress, phenology, and soil properties (i.e. hundreds of features). The XGBoost algorithm outperforms other algorithms both in accuracy and stability, while deep neural networks such as LSTM and CNN are not advantageous. The compositing interval (8-day, 16-day or monthly) of time series variable does not have significant effects on the prediction. Combining the best algorithm and inputs improves the prediction accuracy by 5% when compared to a baseline statistical model (Lasso) using only basic climatic and satellite observations. Reasonable county-level yield foresting is achievable from early June, almost four months prior to harvest. At the national level, early-season (June and July) prediction from the best model outperforms that of the United States Department of Agriculture (USDA) World Agricultural Supply and Demand Estimates (WASDE). This study provides insights into practical crop yield forecasting and the understanding of yield response to climatic and environmental conditions.

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Section: Comparison Of Machine Learning Models and Featuressupporting

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

Kang

Özdoğan

Zhu

et al. 2020

Environ. Res. Lett.

132

View full text Add to dashboard Cite

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“…For modeling the winter wheat in the CONUS, a uniform growing season is needed and, therefore, we defined it as from the preceding October to the end of July of the current year. We used the previous two years' yields as one type of the model inputs since the historical yields were found to be useful for crop yield prediction [20].…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

“…Two types of approaches have been used for wheat yield prediction, including the crop simulation models and statistical models [20]. The crop models forecast yield by simulating the entire crop growth, when considering the physiological characteristics of plants and a number of environmental factors, and representative wheat simulation models include CERES [21], ARCWHEAT1 [22], DAISY [23], SIRIUS [24], and AFRCWHEAT2 model [25].…”

Section: Introductionmentioning

confidence: 99%

Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States

et al. 2020

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Winter wheat (Triticum aestivum L.) is one of the most important cereal crops, supplying essential food for the world population. Because the United States is a major producer and exporter of wheat to the world market, accurate and timely forecasting of wheat yield in the United States (U.S.) is fundamental to national crop management as well as global food security. Previous studies mainly have focused on developing empirical models using only satellite remote sensing images, while other yield determinants have not yet been adequately explored. In addition, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study used advanced machine learning algorithms to establish within-season yield prediction models for winter wheat using multi-source data to address these issues. Specifically, yield driving factors were extracted from four different data sources, including satellite images, climate data, soil maps, and historical yield records. Subsequently, two linear regression methods, including ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO), and four well-known machine learning methods, including support vector machine (SVM), random forest (RF), Adaptive Boosting (AdaBoost), and deep neural network (DNN), were applied and compared for estimating the county-level winter wheat yield in the Conterminous United States (CONUS) within the growing season. Our models were trained on data from 2008 to 2016 and evaluated on data from 2017 and 2018, with the results demonstrating that the machine learning approaches performed better than the linear regression models, with the best performance being achieved using the AdaBoost model (R2 = 0.86, RMSE = 0.51 t/ha, MAE = 0.39 t/ha). Additionally, the results showed that combining data from multiple sources outperformed single source satellite data, with the highest accuracy being obtained when the four data sources were all considered in the model development. Finally, the prediction accuracy was also evaluated against timeliness within the growing season, with reliable predictions (R2 > 0.84) being able to be achieved 2.5 months before the harvest when the multi-source data were combined.

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“…Higher light interceptions usually lead to higher yields, but the yield also varies significantly with other environmental stressors (Lobell et al, 2007;Tombesi et al, 2010;Zhang et al, 2019). To understand the maximum yield potential that almond could reach at a given light interception, we grouped all plot-year samples by the associated light interception with an interval of 5%, and selected the upper 10-percentile samples within each light interception bin, as a proxy for the yield potential.…”

Section: Yield Potentialmentioning

confidence: 99%

Advancing Agricultural Production With Machine Learning Analytics: Yield Determinants for California’s Almond Orchards

et al. 2020

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Agricultural productivity is subject to various stressors, including abiotic and biotic threats, many of which are exacerbated by a changing climate, thereby affecting long-term sustainability. The productivity of tree crops such as almond orchards, is particularly complex. To understand and mitigate these threats requires a collection of multi-layer large data sets, and advanced analytics is also critical to integrate these highly heterogeneous datasets to generate insights about the key constraints on the yields at tree and field scales. Here we used a machine learning approach to investigate the determinants of almond yield variation in California's almond orchards, based on a unique 10-year dataset of field measurements of light interception and almond yield along with meteorological data. We found that overall the maximum almond yield was highly dependent on light interception, e.g., with each one percent increase in light interception resulting in an increase of 57.9 lbs/acre in the potential yield. Light interception was highest for mature sites with higher long term mean spring incoming solar radiation (SRAD), and lowest for younger orchards when March maximum temperature was lower than 19 • C. However, at any given level of light interception, actual yield often falls significantly below full yield potential, driven mostly by tree age, temperature profiles in June and winter, summer mean daily maximum vapor pressure deficit (VPD max), and SRAD. Utilizing a full random forest model, 82% (±1%) of yield variation could be explained when using a sixfold cross validation, with a RMSE of 480 ± 9 lbs/acre. When excluding light interception from the predictors, overall orchard characteristics (such as age, location, and tree density) and inclusive meteorological variables could still explain 78% of yield variation. The model analysis also showed that warmer winter conditions often limited mature orchards from reaching maximum yield potential and summer VPD max beyond 40 hPa significantly limited the yield. Our findings through the machine learning approach improved our understanding of the complex interaction between climate, canopy light interception, and almond nut production, and demonstrated a relatively robust predictability of almond yield. This will ultimately benefit data-driven climate adaptation and orchard nutrient management approaches.

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

Cited by 54 publications

References 50 publications

Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States

Advancing Agricultural Production With Machine Learning Analytics: Yield Determinants for California’s Almond Orchards

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