A county-level soybean yield prediction framework coupled with XGBoost and multidimensional feature engineering

Li, Yuanchao; Zeng, Hongwei; Zhang, Miao; Wu, Bingfang; Zhao, Yaxian; Yao, Xia; Cheng, Tao; Qin, Xingli; Wu, Fangming

doi:10.1016/j.jag.2023.103269

Cited by 21 publications

(21 citation statements)

References 73 publications

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“…Consequently, the model's generalization ability was significantly enhanced, leading to more accurate predictions (Nagaraju, 2021). This research result has been corroborated by Li et al, who confirmed that the XGboost model outperforms other models in soybean yield prediction when utilizing the same input data (Li et al, 2023). Furthermore, in the prediction of winter wheat yield, the XGboost model not only marginally exceeded the RF model in terms of prediction accuracy but also demonstrated significant superiority in computational efficiency in most scenarios.…”

Section: Application Of Basic Model In Wheat Yield Estimationsupporting

confidence: 59%

Wheat Yield Estimation Using Machine Learning Method Based on UAV Remote Sensing Data

Yang,

Li,

Fei

et al. 2024

Preprint

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Accurate forecasting of crop yields holds paramount importance in guiding decision-making processes related to breeding efforts. This study focused on the application of multi-sensor data fusion and machine learning algorithms based on unmanned aerial vehicles (UAVs) in wheat yield prediction. Five machine learning (ML) algorithms namely random forest (RF), partial least squares (PLS), ridge regression (RR), K-Nearest Neighbor (KNN) and eXtreme Gradient Boosting Decision Tree (XGboost) were utilized for multi-sensor data fusion, and three ensemble methods including the second-level ensemble methods (stacking and feature-weighted) and the third-level ensemble method (simple average) for wheat yield prediction. The 270 wheat hybrids were used as planting materials under full and limited irrigation treatments. A cost-effective multi-sensor UAV platform, equipped with red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors, was utilized to gather remote sensing data. The results revealed that the XGboost algorithm exhibited outstanding performance in multi-sensor data fusion, with the RGB+MS+Texture+TIR combination demonstrating the highest fusion performance (R2=0.660, RMSE= 0.754). Compared with the single ML model, the employment of three ensemble methods significantly enhanced the prediction accuracy of wheat yield. Notably, the third-layer simple average ensemble method demonstrated superior performance (R2 = 0.733, RMSE= 0.668 t ha-1). It significantly outperformed both the second-layer ensemble methods of Stacking (R2= 0.668, RMSE= 0.673 t ha-1) and feature-weighted (R2= 0.667, RMSE= 0.674 t ha-1), thereby exhibiting superior predictive capabilities. This finding demonstrated that the third-layer ensemble method not only augments the predictive ability of the model but also fine-tuned the accuracy of wheat yield prediction through the employment of simple average ensemble learning. Consequently, it offers a novel perspective for crop yield prediction and breeding selection.

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Section: Application Of Basic Model In Wheat Yield Estimationsupporting

confidence: 59%

Wheat Yield Estimation Using Machine Learning Method Based on UAV Remote Sensing Data

Yang,

Li,

Fei

et al. 2024

Preprint

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“…It is necessary to consider the main factors and key growth periods that affect crop However, the correlation of each variable with the crop yield varied among the different growth stages [30,53]. Combining the UAV images and field experimental features, good yield co-relationships with the AGB, GNDVI, OSAVI, NDVI, LCI, and NDRE are found in the early stage, with r values of 0.824, 0.590, 0.588, 0.586, 0.542, and 0.540, respectively.…”

Section: Correlations Between Aerial Imaging Features and Plant Field...mentioning

confidence: 99%

“…Although the water content changed during the vegetation growth process, the relationship between the water content and yield was not significant, which was in line with our expectations. However, the correlation of each variable with the crop yield varied among the different growth stages [30,53]. Combining the UAV images and field experimental features, good yield co-relationships with the AGB, GNDVI, OSAVI, NDVI, LCI, and NDRE are BGB sampling is typically a very laborious and time-consuming multistep process that is prone to errors.…”

Section: Correlations Between Aerial Imaging Features and Plant Field...mentioning

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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“…In global agricultural and economic contexts, soybeans serve as a crucial source for food and feed, and additionally, as a fundamental raw material for diverse industrial products. The stability of soybean supplies exerts considerable influence on global markets and food security concerns 1 , 2 . In light of ongoing climate change and increasing global population, the assurance of a stable supply of key crops, including soybeans, has garnered international attention 3 , 4 .…”

Section: Introductionmentioning

confidence: 99%

GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data

Lu,

Fu,

Tang

et al. 2024

Sci Rep

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Accurately estimating large-area crop yields, especially for soybeans, is essential for addressing global food security challenges. This study introduces a deep learning framework that focuses on precise county-level soybean yield estimation in the United States. It utilizes a wide range of multi-variable remote sensing data. The model used in this study is a state-of-the-art CNN-BiGRU model, which is enhanced by the GOA and a novel attention mechanism (GCBA). This model excels in handling intricate time series and diverse remote sensing datasets. Compared to five leading machine learning and deep learning models, our GCBA model demonstrates superior performance, particularly in the 2019 and 2020 evaluations, achieving remarkable R2, RMSE, MAE and MAPE values. This sets a new benchmark in yield estimation accuracy. Importantly, the study highlights the significance of integrating multi-source remote sensing data. It reveals that synthesizing information from various sensors and incorporating photosynthesis-related parameters significantly enhances yield estimation precision. These advancements not only provide transformative insights for precision agricultural management but also establish a solid scientific foundation for informed decision-making in global agricultural production and food security.

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A county-level soybean yield prediction framework coupled with XGBoost and multidimensional feature engineering

Cited by 21 publications

References 73 publications

Wheat Yield Estimation Using Machine Learning Method Based on UAV Remote Sensing Data

Wheat Yield Estimation Using Machine Learning Method Based on UAV Remote Sensing Data

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

GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data

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