Crop Yield Prediction using Gradient Boosting Regression

,; Mishra*, Pratyush; Khan, Rahil; ,; Baranidharan, Dr. B.; ,

doi:10.35940/ijitee.c8879.019320

Cited by 9 publications

(2 citation statements)

References 7 publications

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“…The limitation of the paper in its reliance on a single predictive model and data from a single country, suggesting the need for future studies to explore other machine learning algorithms and expand the scope of the research to other regions. P. Mishra et al [34] used Gradient Boosting Regression to improve the prediction of crop yields for districts in France. The model showed a R-squared score of 0.51 which was significantly better than other models, namely Ada Boosting, KNN, Linear and Random Forests.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

Nikhil,

Pandiyan,

Raja

et al. 2024

Computers

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Agriculture is one of the most important activities that produces crop and food that is crucial for the sustenance of a human being. In the present day, agricultural products and crops are not only used for local demand, but globalization has allowed us to export produce to other countries and import from other countries. India is an agricultural nation and depends a lot on its agricultural activities. Prediction of crop production and yield is a necessary activity that allows farmers to estimate storage, optimize resources, increase efficiency and decrease costs. However, farmers usually predict crops based on the region, soil, weather conditions and the crop itself based on experience and estimates which may not be very accurate especially with the constantly changing and unpredictable climactic conditions of the present day. To solve this problem, we aim to predict the production and yield of various crops such as rice, sorghum, cotton, sugarcane and rabi using Machine Learning (ML) models. We train these models with the weather, soil and crop data to predict future crop production and yields of these crops. We have compiled a dataset of attributes that impact crop production and yield from specific states in India and performed a comprehensive study of the performance of various ML Regression Models in predicting crop production and yield. The results indicated that the Extra Trees Regressor achieved the highest performance among the models examined. It attained a R-Squared score of 0.9615 and showed lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 21.06 and 33.99. Following closely behind are the Random Forest Regressor and LGBM Regressor, achieving R-Squared scores of 0.9437 and 0.9398 respectively. Moreover, additional analysis revealed that tree-based models, showing a R-Squared score of 0.9353, demonstrate better performance compared to linear and neighbors-based models, which achieved R-Squared scores of 0.8568 and 0.9002 respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning-Based Crop Yield Prediction in South India: Performance Analysis of Various Models

Nikhil,

Pandiyan,

Raja

et al. 2024

Computers

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“…XGBoost incorporates enhancements that contribute to its robust performance, including regularization techniques, better processing of missing values, and the ability to handle non-linear relationships more effectively [85]. For example, Khan et al [119] evaluated the performance of GBM and XGBoost, along with other machine learning algorithms, in maize yield prediction across France using meteorological data. The study demonstrated that XGBoost outperformed GBM, achieving an R 2 of 0.51 compared to an R 2 of 0.17 for GBM.…”

Section: Accuracy Assessment and Influence Of Featuresmentioning

confidence: 99%

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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