Machine learning technology for early prediction of grain yield at the field scale: A systematic review

Leukel, Joerg; Zimpel, Tobias; Stumpe, Christoph

doi:10.1016/j.compag.2023.107721

Cited by 20 publications

(11 citation statements)

References 61 publications

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“…The grains were categorized into seven colour classes. Among these, genotypes with gray grain colour showed the highest number of unique metabolic markers (52), followed by brown-gray (42), a mixture of yellow and gray grains (29), deep gray (17), yellow (11), brown (10), and cream (7) (Fig. 4h).…”

Section: Selection Distribution and Classi Cation Of The Top Metaboli...mentioning

confidence: 99%

“…Predictive modelling using machine learning technology is increasing towards early predictions before harvest, predictions at the scale of eld or region, and predictions for different types of crops. This great diversity of prediction tasks requires a proper choice of speci c machine learning techniques to attain high levels of performance (Leukel et al 2023). The signi cance of predicting yield and other phenotypic traits has already been emphasised.…”

Section: Into Plant Phenotypic Plasticity: a Comprehensive Exploratio...mentioning

confidence: 99%

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Predictive metabolomics of pearl millet phenotypic traits using a germplasm panel of genetic diversity

Pimentel,

Passos,

Prigent

et al. 2024

Preprint

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Introduction Pearl millet, dubbed a “Nutri-cereal”, has a high content of protein, starch, fiber, mineral and fatty acids. Its resilience in adverse agro-climatic conditions sets it apart from major cereals. Despite this, understanding how its genetic diversity affects physiological traits and metabolic responses remains limited. Predictive metabolomics, merging metabolomics with artificial intelligence, allows for the comprehensive top-down modelling —from phenotype to the mechanism— of various phenotypic traits.Objectives To discover predictive biomarkers for phenotypic traits in the Brazilian germplasm core collection of 203 genotypes of pearl millet through the combination of predictive metabolomics with machine learning.Methods Untargeted metabolomics was conducted using UHPLC-LTQ-Orbitrap-HRMS to obtain metabolite profiles, from the central and specialised metabolism of the pearl millet core collection. Generalised linear modelling with penalisation (GLMNET) was applied to explore the correlation between metabolism and phenotypic traits.Results Our model successfully predicted eight qualitative traits from the pearl millet core collection, with accuracy ranging between 74% and 87%. From, 834 potential unique biomarkers (575 annotated-ion features and 259 unknowns) have been annotated as top metabolic predictors. It is noteworthy that the majority of the top metabolic predictors were from the carbohydrate, amino acid, flavonoid, and terpene subclasses.Conclusions This is the first report on leveraging a germplasm bank of pearl millet for metabolome characterisation and subsequent predictive modelling of important agronomic traits. These outcomes hint at the robustness of employing GLMNET for predicting metabolic biomarkers crucial in selecting genotypes for future breeding programmes.

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Section: Selection Distribution and Classi Cation Of The Top Metaboli...mentioning

confidence: 99%

Section: Into Plant Phenotypic Plasticity: a Comprehensive Exploratio...mentioning

confidence: 99%

Predictive metabolomics of pearl millet phenotypic traits using a germplasm panel of genetic diversity

Pimentel,

Passos,

Prigent

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…The linear model is used as the baseline method because it is one of the simplest forms of modeling to construct the relationship between crop yield and multiple agronomic characteristics [48]. Stepwise multiple linear regression (SMLR) models were established using the plot-wise field phenotypic trait and VI data as independent variables to evaluate whether the linear model had the ability to directly indicate the end-of-season yield of tiger nut tubers.…”

Section: Smlr Yield Predicting Model Constructionmentioning

confidence: 99%

Individualized Indicators and Estimation Methods for Tiger Nut (Cyperus esculentus L.) Tubers Yield Using Light Multispectral UAV and Lightweight CNN Structure

2023

Drones

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Tiger nuts are a non-genetically modified organism crop with high adaptability and economic value, and they are being widely promoted for cultivation in China. This study proposed a new yield-estimation method based on a lightweight convolutional neural network (CNN) named Squeeze Net to provide accurate production forecasts for tiger nut tubers. The multispectral unmanned aerial vehicle (UAV) images were used to establish phenotypic datasets of tiger nuts, comprising vegetation indices (VIs) and plant phenotypic indices. The Squeeze Net model with a lightweight CNN structure was constructed to fully explore the explanatory power of the spectral UAV-derived information and compare the differences between the parametric and nonparametric models applied in tiger nut yield predictions. Compared with stepwise multiple linear regression (SMLR), both algorithms achieved good yield prediction performances. The highest obtained accuracies reflected an R2 value of 0.775 and a root-mean-square error (RMSE) value of 688.356 kg/ha with SMLR, and R2 = 0.780 and RMSE = 716.625 kg/ha with Squeeze Net. This study demonstrated that Squeeze Net can efficiently process UAV multispectral images and improve the resolution and accuracy of the yield prediction results. Our study demonstrated the enormous potential of artificial intelligence (AI) algorithms in the precise crop management of tiger nuts in the arid sandy lands of northwest China by exploring the interactions between various intensive phenotypic traits and productivity.

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“…Recently, adopting machine learning (ML) models in conjunction with images of high spatiotemporal resolution captured by unmanned aerial vehicles (UAVs) is the dominant approach for field-scale crop yield prediction [1,9,11,[17][18][19]. Many yield prediction studies followed the pipelined procedures of canopy feature extraction and selection and ML model construction [1,9,11,12,[20][21][22], proposing the feature-based method, wherein feature selection is essential to the feature-based method [5,9,13,23,24], which aims to select the most relevant features for yield prediction.…”

Section: Introductionmentioning

confidence: 99%

Wheat Yield Prediction Using Unmanned Aerial Vehicle RGB-Imagery-Based Convolutional Neural Network and Limited Training Samples

Ma,

Wu,

Liu

et al. 2023

Remote Sensing

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Low-cost UAV RGB imagery combined with deep learning models has demonstrated the potential for the development of a feasible tool for field-scale yield prediction. However, collecting sufficient labeled training samples at the field scale remains a considerable challenge, significantly limiting the practical use. In this study, a split-merge framework was proposed to address the issue of limited training samples at the field scale. Based on the split-merge framework, a yield prediction method for winter wheat using the state-of-the-art Efficientnetv2_s (Efficientnetv2_s_spw) and UAV RGB imagery was presented. In order to demonstrate the effectiveness of the split-merge framework, in this study, Efficientnetv2_s_pw was built by directly feeding the plot images to Efficientnetv2_s. The results indicated that the proposed split-merge framework effectively enlarged the training samples, thus enabling improved yield prediction performance. Efficientnetv2_s_spw performed best at the grain-filling stage, with a coefficient of determination of 0.6341 and a mean absolute percentage error of 7.43%. The proposed split-merge framework improved the model ability to extract indicative image features, partially mitigating the saturation issues. Efficientnetv2_s_spw demonstrated excellent adaptability across the water treatments and was recommended at the grain-filling stage. Increasing the ground resolution of input images may further improve the estimation performance. Alternatively, improved performance may be achieved by incorporating additional data sources, such as the canopy height model (CHM). This study indicates that Efficientnetv2_s_spw is a promising tool for field-scale yield prediction of winter wheat, providing a practical solution to field-specific crop management.

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Machine learning technology for early prediction of grain yield at the field scale: A systematic review

Cited by 20 publications

References 61 publications

Predictive metabolomics of pearl millet phenotypic traits using a germplasm panel of genetic diversity

Predictive metabolomics of pearl millet phenotypic traits using a germplasm panel of genetic diversity

Individualized Indicators and Estimation Methods for Tiger Nut (Cyperus esculentus L.) Tubers Yield Using Light Multispectral UAV and Lightweight CNN Structure

Wheat Yield Prediction Using Unmanned Aerial Vehicle RGB-Imagery-Based Convolutional Neural Network and Limited Training Samples

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