Prediction of corn variety yield with attribute-missing data via graph neural network

Yang, Feng; Zhang, Dongfeng; Zhang, Yuqing; Zhang, Yong; Han, Yanyun; Zhang, Qiusi; Zhang, Qi; Zhang, Chenghui; Liu, Zhongqiang; Wang, Kaiyi

doi:10.1016/j.compag.2023.108046

Cited by 9 publications

(2 citation statements)

References 35 publications

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“…On the other hand, the authors conducted a comparative study to benchmark current ensemble machine learning methods with novel ones in other domains, such as deep learning, to predict crop yield. Prediction of corn variety yield with missing attribute data can be effectively addressed using Graph Neural Networks (GNNs) [66]. The result of Table 7 shows much worse performance, with Graph Convolutional Networks (GCNs), −0.057997 and Graph Attention Networks (GATs), −0.259505 having negative R 2 values, which means that these models perform worse than a mean baseline.…”

Section: Results Of Ensemble Modelsmentioning

confidence: 99%

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Pukrongta,

Taparugssanagorn,

Sangpradit

2024

Applied Sciences

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This research introduces the PEnsemble 4 model, a weighted ensemble prediction model that integrates multiple individual machine learning models to achieve accurate maize yield forecasting. The model incorporates unmanned aerial vehicle (UAV) imagery and Internet of Things (IoT)-based environmental data, providing a comprehensive and data-driven approach to yield prediction in maize cultivation. Considering the projected growth in global maize demand and the vulnerability of maize crops to weather conditions, improved prediction capabilities are of paramount importance. The PEnsemble 4 model addresses this need by leveraging comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the model effectively analyzes temporal patterns in vegetation indices, in particular CIre and NDRE, which serve as reliable indicators of canopy density and plant height. Notably, the PEnsemble 4 model demonstrates a remarkable accuracy rate of 91%. It advances the timeline for yield prediction from the conventional reproductive stage (R6) to the blister stage (R2), enabling earlier estimation and enhancing decision-making processes in farming operations. Moreover, the model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. By synergistically integrating IoT and machine learning technologies, the PEnsemble 4 model presents a novel and promising solution for maize yield prediction. Its application holds the potential to revolutionize crop management and protection, contributing to efficient and sustainable farming practices.

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Section: Results Of Ensemble Modelsmentioning

confidence: 99%

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Pukrongta,

Taparugssanagorn,

Sangpradit

2024

Applied Sciences

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“…Currently, remote sensing has become the dominant method for crop yield estimation [1,2]. The traditional methods of crop yield estimation generally adopt a sampling survey and field observation approach, which is not suitable for crop yield estimation in large areas [3,4]. Remote sensing yield estimation can macroscopically and dynamically monitor in real time, and provide more scientific monitoring methods for crop growth and yield prediction [5].…”

Section: Introductionmentioning

confidence: 99%

Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau

He,

Yang,

et al. 2023

Agronomy

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Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response.

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Prediction of maize cultivar yield based on machine learning algorithms for precise promotion and planting

Han,

Wang,

Yang

et al. 2024

Agricultural and Forest Meteorology

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Prediction of corn variety yield with attribute-missing data via graph neural network

Cited by 9 publications

References 35 publications

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau

Prediction of maize cultivar yield based on machine learning algorithms for precise promotion and planting

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