K-Fold Validation of Multi Models for Crop Yield Prediction with Improved Sparse Data Clustering Process
Keywords:
Crop yield, Multi Model ensemble, K-Fold validation, Sparse Data clustering processAbstract
Modern crop yield prediction helps farmers and policymakers maximize agricultural operations. Predicting crop yields is difficult, especially given scant agricultural datasets. This paper proposes a novel method that combines K-Fold validation and multi-model ensemble approaches to improve crop production forecast accuracy and address sparse data. Our technique starts with an improved sparse data clustering process that efficiently groups comparable data points and mitigates the impact of missing or limited information. Clustering helps us find patterns and trends in data, reducing the impact of data sparsity on crop production projections. K-Fold validation, a strong cross-validation method, is used to evaluate various prediction models. We test each model on different folds by partitioning the data into K subsets. K-Fold validation validates the generalizability of our multi-model ensemble strategy, improving crop production estimates. 5-fold validation of multi-models like SVM, CNN, DT, NN, and NB predicts. Predictions depend on "log of" performance. Our methodology works on real-world agricultural datasets through considerable experimentation and comparison with existing methods. In scarce data, crop yield forecast accuracy improved significantly. Our ensemble of models beats individual models, demonstrating the value of many approaches for prediction. In conclusion, K-Fold validation and multi-model ensembles improve crop production prediction accuracy, especially with scarce agricultural data. This research can improve agricultural decision-making and sustainability by developing more precise predictions.
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M. Rashid, B. S. Bari, Y. Yusup, M. A. Kamaruddin and N. Khan, "A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction," in IEEE Access, vol. 9, pp. 63406-63439, 2021, doi: 10.1109/ACCESS.2021.3075159.
Y. Alebele et al., "Estimation of Crop Yield From Combined Optical and SAR Imagery Using Gaussian Kernel Regression," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 10520-10534, 2021, doi: 10.1109/JSTARS.2021.3118707.
A. Mateo-Sanchis, J. E. Adsuara, M. Piles, J. Munoz-Marí, A. Perez-Suay and G. Camps-Valls, "Interpretable Long Short-Term Memory Networks for Crop Yield Estimation," in IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023, Art no. 2501105, doi: 10.1109/LGRS.2023.3244064.
D. Elavarasan and P. M. D. Vincent, "Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications," in IEEE Access, vol. 8, pp. 86886-86901, 2020, doi: 10.1109/ACCESS.2020.2992480.
Y. Ma, Z. Yang and Z. Zhang, "Multisource Maximum Predictor Discrepancy for Unsupervised Domain Adaptation on Corn Yield Prediction," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-15, 2023, Art no. 4401315, doi: 10.1109/TGRS.2023.3247343.
M. Qiao et al., "Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian Process," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 4476-4489, 2021, doi: 10.1109/JSTARS.2021.3073149.
R. Luciani, G. Laneve and M. JahJah, "Agricultural Monitoring, an Automatic Procedure for Crop Mapping and Yield Estimation: The Great Rift Valley of Kenya Case," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 7, pp. 2196-2208, July 2019, doi: 10.1109/JSTARS.2019.2921437.
A. Reyana, S. Kautish, P. M. S. Karthik, I. A. Al-Baltah, M. B. Jasser and A. W. Mohamed, "Accelerating Crop Yield: Multisensor Data Fusion and Machine Learning for Agriculture Text Classification," in IEEE Access, vol. 11, pp. 20795-20805, 2023, doi: 10.1109/ACCESS.2023.3249205.
S. M. M. Nejad, D. Abbasi-Moghadam, A. Sharifi, N. Farmonov, K. Amankulova and M. Lászlź, "Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 254-266, 2023, doi: 10.1109/JSTARS.2022.3223423.
Y. Ma and Z. Zhang, "A Bayesian Domain Adversarial Neural Network for Corn Yield Prediction," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 5513705, doi: 10.1109/LGRS.2022.3211444.
S. P. Raja, B. Sawicka, Z. Stamenkovic and G. Mariammal, "Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers," in IEEE Access, vol. 10, pp. 23625-23641, 2022, doi: 10.1109/ACCESS.2022.3154350.
L. Martínez-Ferrer, M. Piles and G. Camps-Valls, "Crop Yield Estimation and Interpretability With Gaussian Processes," in IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 12, pp. 2043-2047, Dec. 2021, doi: 10.1109/LGRS.2020.3016140.
A. F. Haufler, J. H. Booske and S. C. Hagness, "Microwave Sensing for Estimating Cranberry Crop Yield: A Pilot Study Using Simulated Canopies and Field Measurement Testbeds," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-11, 2022, Art no. 4400411, doi: 10.1109/TGRS.2021.3050171.
N. Farmonov et al., "Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 1576-1588, 2023, doi: 10.1109/JSTARS.2023.3239756.
M. D. Maas, M. Salvia, P. C. Spennemann and M. E. Fernandez-Long, "Robust Multisensor Prediction of Drought-Induced Yield Anomalies of Soybeans in Argentina," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-4, 2022, Art no. 2504804, doi: 10.1109/LGRS.2022.3171415.
J. Jiang, F. Xing, X. Zeng and Q. Zou, "Investigating Maize Yield-Related Genes in Multiple Omics Interaction Network Data," in IEEE Transactions on NanoBioscience, vol. 19, no. 1, pp. 142-151, Jan. 2020, doi: 10.1109/TNB.2019.2920419.
N. Rasheed, S. A. Khan, A. Hassan and S. Safdar, "A Decision Support Framework for National Crop Production Planning," in IEEE Access, vol. 9, pp. 133402-133415, 2021, doi: 10.1109/ACCESS.2021.3115801.
M. A. Z. Abidin, M. N. Mahyuddin and M. A. A. M. Zainuri, "Optimal Efficient Energy Production by PV Module Tilt-Orientation Prediction Without Compromising Crop-Light Demands in Agrivoltaic Systems," in IEEE Access, vol. 11, pp. 71557-71572, 2023, doi: 10.1109/ACCESS.2023.3293850.
C. A. Martínez Félix, G. E. Vázquez Becerra, J. R. Millán Almaraz, F. Geremia-Nievinski, J. R. Gaxiola Camacho and Á. Melgarejo Morales, "In-Field Electronic Based System and Methodology for Precision Agriculture and Yield Prediction in Seasonal Maize Field," in IEEE Latin America Transactions, vol. 17, no. 10, pp. 1598-1606, October 2019, doi: 10.1109/TLA.2019.8986437.
H. R. Seireg, Y. M. K. Omar, F. E. A. El-Samie, A. S. El-Fishawy and A. Elmahalawy, "Ensemble Machine Learning Techniques Using Computer Simulation Data for Wild Blueberry Yield Prediction," in IEEE Access, vol. 10, pp. 64671-64687, 2022, doi: 10.1109/ACCESS.2022.3181970.
L. He, C. A. Coburn, Z. -J. Wang, W. Feng and T. -C. Guo, "Reduced Prediction Saturation and View Effects for Estimating the Leaf Area Index of Winter Wheat," in IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 3, pp. 1637-1652, March 2019, doi: 10.1109/TGRS.2018.2868138..
Dr. Avinash Pawar. (2020). Development and Verification of Material Plasma Exposure Concepts. International Journal of New Practices in Management and Engineering, 9(03), 11 - 14. https://doi.org/10.17762/ijnpme.v9i03.90
Dr. Nitin Sherje. (2020). Biodegradable Material Alternatives for Industrial Products and Goods Packaging System. International Journal of New Practices in Management and Engineering, 9(03), 15 - 18. https://doi.org/10.17762/ijnpme.v9i03.91
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