Abstract
Due to increasing population and rapid industrialisation across countries lead to the enormous demand in food supply chain. Our proposed work results in crop yield prediction based various parameters affecting yield applying machine learning models. Comparative analysis also done with basic machine learning and advanced ensemble technique, in terms of yield accuracy with respect to various parameters such as environmental, soil and crop management factors. Our proposed multi-layer stacked ensemble model outperformed Decision tree regression (DTR), Multiple linear regression (MLR), Support vector regression (SVR), K-nearest neighbour (KNN), Random forest (RT) and Gradient boosting regression (GBR) with an accuracy score of 94.43% along with various accuracy parameter metrics like Mean absolute Error (MAE), Mean square error (MSE), Root mean square error (RMSE).
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Iniyan, S., Jebakumar, R. (2022). Crop Yield Prediction on Soybean Crop Applying Multi-layer Stacked Ensemble Learning Technique. In: Manogaran, G., Shanthini, A., Vadivu, G. (eds) Proceedings of International Conference on Deep Learning, Computing and Intelligence. Advances in Intelligent Systems and Computing, vol 1396. Springer, Singapore. https://doi.org/10.1007/978-981-16-5652-1_29
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