The Application of Unmanned Aerial Vehicles (UAVs) and Extreme Gradient Boosting (XGBoost) to Crop Yield Estimation: A Case Study of Don Tum District, Nakhon Pathom, Thailand

doi:10.52939/ijg.v19i2.2569

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…This indicates that the XGBoost model effectively captures the underlying patterns and relationships between the variables, resulting in more accurate estimates for average wheat yield. These findings are in agreement with previous studies by Mariadass et al [114], Kulpanich et al [115], and Noorunnahar et al [116], which have also highlighted the superior performance of the XGBoost algorithm compared to other machine learning models. A recent study by Huber et al [117] explored the potential of XGBoost for soybean yield estimation in the United States, yielding an average R 2 of 0.79, which was outperformed by our XGBoost-based model.…”

Section: Accuracy Assessment and Influence Of Featuressupporting

confidence: 93%

Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal

Sahbeni,

Székely,

Musyimi

et al. 2023

AgriEngineering

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Effective crop monitoring and accurate yield estimation are fundamental for informed decision-making in agricultural management. In this context, the present research focuses on estimating wheat yield in Nepal at the district level by combining Sentinel-3 SLSTR imagery with soil data and topographic features. Due to Nepal’s high-relief terrain, its districts exhibit diverse geographic and soil properties, leading to a wide range of yields, which poses challenges for modeling efforts. In light of this, we evaluated the performance of two machine learning algorithms, namely, the gradient boosting machine (GBM) and the extreme gradient boosting (XGBoost). The results demonstrated the superiority of the XGBoost-based model, achieving a determination coefficient (R2) of 0.89 and an RMSE of 0.3 t/ha for training, with an R2 of 0.61 and an RMSE of 0.42 t/ha for testing. The calibrated model improved the overall accuracy of yield estimates by up to 10% compared to GBM. Notably, total nitrogen content, slope, total column water vapor (TCWV), organic matter, and fractional vegetation cover (FVC) significantly influenced the predicted values. This study highlights the effectiveness of combining multi-source data and Sentinel-3 SLSTR, particularly proposing XGBoost as an alternative tool for accurately estimating yield at lower costs. Consequently, the findings suggest comprehensive and robust estimation models for spatially explicit yield forecasting and near-future yield projection using satellite data acquired two months before harvest. Future work can focus on assessing the suitability of agronomic practices in the region, thereby contributing to the early detection of yield anomalies and ensuring food security at the national level.

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Section: Accuracy Assessment and Influence Of Featuressupporting

confidence: 93%

Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal

Sahbeni,

Székely,

Musyimi

et al. 2023

AgriEngineering

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XGBoost sequential system for the prediction of Persian lemon crop yield

López Segura,

Aguilar Lasserre,

Fernández Lámbert

et al. 2023

Crop Science

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Today, Mexico ranks second worldwide in total lemon production; however, the yield per hectare is only ranked eighth, which makes evident a low productive potential in the country. This work provides an impact analysis of certain physical and climatic variables on the yield of Persian lemon (Citrus latifolia) harvest; its objective is to determine which variables are controllable to guarantee a higher crop yield in different uncontrollable climatic conditions. For this, we developed a yield prediction system. The system consists of three sequential Extreme Gradient Boosting (XGBoost) algorithms fed with historical information, which allow for prediction of Persian lemon crop in its three stages: flowering, budding, and fruiting, having as input variables planting density, number of trees in production, pruning and cleaning, and pest control, in addition to uncontrollable weather elements such as rain and wind. The variables with the greatest impact on crop yield are pruning, cleaning, and pest control. The algorithms have a performance of 0.87, 0.93, and 0.97, respectively, for each stage. The analysis provides the farmer with a tool that allows him to make data‐driven decisions based on the manipulation of good agricultural practices considering the current weather conditions, thus promoting a higher harvest yield and thus a higher profit margin.

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The Application of Unmanned Aerial Vehicles (UAVs) and Extreme Gradient Boosting (XGBoost) to Crop Yield Estimation: A Case Study of Don Tum District, Nakhon Pathom, Thailand

Cited by 2 publications

References 22 publications

Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal

Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal

XGBoost sequential system for the prediction of Persian lemon crop yield

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