Wild blueberry yield prediction using a combination of computer simulation and machine learning algorithms

Obsie, Efrem Yohannes; Qu, Hongchun; Drummond, Francis A.

doi:10.1016/j.compag.2020.105778

Cited by 77 publications

(56 citation statements)

References 41 publications

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“…XGBoost had the worst performance among the machine learning models tested, placing the samples into yield groups. This behavior suggests that the tabular dataset had an insufficient number of features for XGBoost to separate low and high-yielding hybrids, as previous studies showed XGBoost as competent when dealing with larger datasets [54,55]. Tab-DNN was selected as a module for the multimodal architecture, due to its potential for improvement within the co-learning framework.…”

Section: Discussionmentioning

confidence: 99%

Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection

et al. 2021

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Assessing crop production in the field often requires breeders to wait until the end of the season to collect yield-related measurements, limiting the pace of the breeding cycle. Early prediction of crop performance can reduce this constraint by allowing breeders more time to focus on the highest-performing varieties. Here, we present a multimodal deep learning model for predicting the performance of maize (Zea mays) at an early developmental stage, offering the potential to accelerate crop breeding. We employed multispectral images and eight vegetation indices, collected by an uncrewed aerial vehicle approximately 60 days after sowing, over three consecutive growing cycles (2017, 2018 and 2019). The multimodal deep learning approach was used to integrate field management and genotype information with the multispectral data, providing context to the conditions that the plants experienced during the trial. Model performance was assessed using holdout data, in which the model accurately predicted the yield (RMSE 1.07 t/ha, a relative RMSE of 7.60% of 16 t/ha, and R2 score 0.73) and identified the majority of high-yielding varieties, outperforming previously published models for early yield prediction. The inclusion of vegetation indices was important for model performance, with a normalized difference vegetation index and green with normalized difference vegetation index contributing the most to model performance. The model provides a decision support tool, identifying promising lines early in the field trial.

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Section: Discussionmentioning

confidence: 99%

Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection

et al. 2021

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“…Since 1990, climate change has resulted in an increased prevalence of wet spring weather in Maine wild-blueberry-growing regions, causing a reduction in the average number of pollination days (days in which bees will fly) by almost 50% compared to historical weather data [76]. Simulation suggests that this increased precipitation results in a reduction in the level of pollination given a typical average density of bees in fields [90]. Unusual hot spring weather can also have an effect on pollination.…”

Section: Appendix a Literature Review Of Wild-blueberry Reproductive Biology And Pollinationmentioning

confidence: 99%

“…It can reduce bee activity by bumble-bee queens [8], as well as increase the development rate of wild blueberry, thereby reducing the number of days flowers are available for pollination [70]. Osbie et al [90] also found that hot wet weather during bloom reduced pollination, but that the effect was not equal among bees. Fruit set attributed to bumble bees declined most as a result of a combination of a shorter flowering period and foraging behavior that exhibited fewer of the jump dispersal events that result in outcrossing.…”

Section: Appendix a Literature Review Of Wild-blueberry Reproductive Biology And Pollinationmentioning

confidence: 99%

“…Fruit set, defined as the proportion of flowers that have the potential after pollination to develop into marketable fruit, increases with increasing density of pollinators [18,41,44,78]. An increase in fruit set results in an increase in yield [90,106], which is the direct economic measure for farmers. Measured by Asare et al [18], a 1% increase in fruit set will result in a 44.1 kg/ha increase in yield, and 60% of the variance in yield can be explained by fruit set and production practices.…”

Section: Appendix a Literature Review Of Wild-blueberry Reproductive Biology And Pollinationmentioning

confidence: 99%

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Analysis of Pollination Services Provided by Wild and Managed Bees (Apoidea) in Wild Blueberry (Vaccinium angustifolium Aiton) Production in Maine, USA, with a Literature Review

Bushmann¹,

Drummond

2020

Agronomy

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Maine is the largest producer of wild blueberry (Vaccinium angustifolium Aiton) in the United States. Pollination comes from combinations of honey bees (Apis mellifera (L.)), commercial bumble bees (Bombus impatiens Cresson), and wild bees. This study addresses (1) previous research addressing wild-blueberry pollination, (2) effects of wild-bee and honey-bee activity densities on fruit set, yield, and crop value, (3) the economic value of wild-bee communities, and (4) economic consequences of pollinator loss. Bee communities were sampled in 40 fields over three years (2010–2012) and bee activity densities were estimated for bumble bees, honey bees, and other wild bees. These data were applied to an economic model to estimate the value of bee taxa. Bumble bees and honey bees predicted fruit set and reduced its spatial heterogeneity. Other wild bees were not significant predictors of fruit set. Yield was predicted by fruit set and field size, but not pest management tactics. Our analysis showed that disruption in supply of honey bees would result in nearly a 30% decrease in crop yield, buffered in part by wild bees that provide “background” levels of pollination. Honey-bee stocking density and, thus, the activity density of honey bees was greater in larger fields, but not for wild bees. Therefore, a decrease in crop yield would be greater than 30% for large fields due to the proportionally greater investment in honey bees in large fields and a relatively lower contribution by wild bees.

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“…The drive-through model developed in this study is a meta-model (or models of models) that as described by Obsie et al [ 33 ], represents a deterministic proxy for stochastic simulation models. However, developing this type of meta-models requires running complex simulation for many combinations and then using the data generated by each run to create a simpler machine learning model that is a reasonable approximation of the initial simulation model.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Model of Drive-Through Vaccination Simulation

Asgary

Valtchev

Chen

et al. 2020

IJERPH

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Planning for mass vaccination against SARS-Cov-2 is ongoing in many countries considering that vaccine will be available for the general public in the near future. Rapid mass vaccination while a pandemic is ongoing requires the use of traditional and new temporary vaccination clinics. Use of drive-through has been suggested as one of the possible effective temporary mass vaccinations among other methods. In this study, we present a machine learning model that has been developed based on a big dataset derived from 125K runs of a drive-through mass vaccination simulation tool. The results show that the model is able to reasonably well predict the key outputs of the simulation tool. Therefore, the model has been turned to an online application that can help mass vaccination planners to assess the outcomes of different types of drive-through mass vaccination facilities much faster.

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Wild blueberry yield prediction using a combination of computer simulation and machine learning algorithms

Cited by 77 publications

References 41 publications

Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection

Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection

Analysis of Pollination Services Provided by Wild and Managed Bees (Apoidea) in Wild Blueberry (Vaccinium angustifolium Aiton) Production in Maine, USA, with a Literature Review

Artificial Intelligence Model of Drive-Through Vaccination Simulation

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