Dataset Information

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

ABSTRACT: 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 characteristics from remote sensing imagery. Our results showed that the predicted orchard-level yield agreed well with the independent yield records. For both the early season (March) and mid-season (June) predictions, a coefficient of determination (R 2) of 0.71, and a ratio of performance to interquartile distance (RPIQ) of 2.6 were found on average. We also identified several key determinants of yield based on the modeling results. Almond yield increased dramatically with the orchard age until about 7 years old in general, and the higher long-term mean maximum temperature during April-June enhanced the yield in the southern orchards, while a larger amount of precipitation in March reduced the yield, especially in northern orchards. Remote sensing metrics such as annual maximum vegetation indices were also dominant variables for predicting the yield potential. While these results are promising, further refinement is needed; the availability of larger data sets and incorporation of additional variables and methodologies will be required for the model to be used as a fertilization decision support tool for growers. Our study has demonstrated the potential of automatic almond yield prediction to assist growers to manage N adaptively, comply with mandated requirements, and ensure industry sustainability.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC6656960 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Zhang Zhou Z Jin Yufang Y Chen Bin B Brown Patrick P

Frontiers in plant science 20190718

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. Howe ...[more]

PMID: 31379888

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Project description:Concerns over the availability of honeybees (Apis mellifera L.) to meet pollination demands have elicited interest in alternative pollinators to mitigate pressures on the commercial beekeeping industry. The blue orchard bee, Osmia lignaria (Say), is a commercially available native bee that can be employed as a copollinator with, or alternative pollinator to, honeybees in orchards. To date, their successful implementation in agriculture has been limited by poor recovery of bee progeny for use during the next spring. This lack of reproductive success may be tied to an inadequate diversity and abundance of alternative floral resources during the foraging period. Managed, supplementary wildflower plantings may promote O. lignaria reproduction in California almond orchards. Three wildflower plantings were installed and maintained along orchard edges to supplement bee forage. Plantings were seeded with native wildflower species that overlapped with and extended beyond almond bloom. We measured bee visitation to planted wildflowers, bee reproduction, and progeny outcomes across orchard blocks at variable distances from wildflower plantings during 2015 and 2016. Pollen provision composition was also determined to confirm O. lignaria wildflower pollen use. Osmia lignaria were frequently observed visiting wildflower plantings during, and after, almond bloom. Most O. lignaria nesting occurred at orchard edges. The greatest recovery of progeny occurred along the orchard edges having the closest proximity (80 m) to managed wildflower plantings versus edges farther away. After almond bloom, O. lignaria nesting closest to the wildflower plantings collected 72% of their pollen from Phacelia spp., which supplied 96% of the managed floral area. Phacelia spp. pollen collection declined with distance from the plantings, but still reached 17% 800 m into the orchard. This study highlights the importance of landscape context and proximity to supplementary floral resources in promoting the propagation of solitary bees as alternative managed pollinators in commercial agriculture.

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Project description:BackgroundThe objective of this study is twofold. First, ascertain the important variables that predict tomato yields from plant height (PH) and vegetation index (VI) maps. The maps were derived from images taken by unmanned aerial vehicles (UAVs). Second, examine the accuracy of predictions of tomato fresh shoot masses (SM), fruit weights (FW), and the number of fruits (FN) from multiple machine learning algorithms using selected variable sets. To realize our objective, ultra-high-resolution RGB and multispectral images were collected by a UAV on ten days in 2020's tomato growing season. From these images, 756 total variables, including first- (e.g., average, standard deviation, skewness, range, and maximum) and second-order (e.g., gray-level co-occurrence matrix features and growth rates of PH and VIs) statistics for each plant, were extracted. Several selection algorithms (i.e., Boruta, DALEX, genetic algorithm, least absolute shrinkage and selection operator, and recursive feature elimination) were used to select the variable sets useful for predicting SM, FW, and FN. Random forests, ridge regressions, and support vector machines were used to predict the yield using the top five selected variable sets.ResultsFirst-order statistics of PH and VIs collected during the early to mid-fruit formation periods, about one month prior to harvest, were important variables for predicting SM. Similar to the case for SM, variables collected approximately one month prior to harvest were important for predicting FW and FN. Furthermore, variables related to PH were unimportant for prediction. Compared with predictions obtained using only first-order statistics, those obtained using the second-order statistics of VIs were more accurate for FW and FN. The prediction accuracy of SM, FW, and FN by models constructed from all variables (rRMSE = 8.8-28.1%) was better than that from first-order statistics (rRMSE = 10.0-50.1%).ConclusionsIn addition to basic statistics (e.g., average and standard deviation), we derived second-order statistics of PH and VIs at the plant level using the ultra-high resolution UAV images. Our findings indicated that our variable selection method reduced the number variables needed for tomato yield prediction, improving the efficiency of phenotypic data collection and assisting with the selection of high-yield lines within breeding programs.

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Dataset Information

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

Publications

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

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