Machine learning and Multi-Layer Perceptron (MLP) modeling of Zea Mays L. responses to tillage and soil amendments
محورهای موضوعی : Journal of Computer & Robotics
Iman Ahmadi
1
(Islamic Azad University, Isfahan branch)
کلید واژه: Classification, Machine learning, Model accuracy, Multi-Layer Perceptron, Soil amendments, Tillage,
چکیده مقاله :
To model the effect of two tillage operations (i.e. conventional and minimum tillage) and seven soil amendments (i.e. C, F, RF, RFM, RTiP, RML, RTiM) on the responses of Zea Mays L. (i.e. corn and stover yields, plant height at 6th and 10th leaf phases, and relative chlorophyll content of the crop leaves at 6th and 10th leaf phases), two-class and four-class classification modeling using the machine learning and multi-layer perceptron principles was performed. To examine the effect of different algorithms considered in the models (i.e. Decision Tree Classifier, Support Vector Machine (SVM) Classifier, K-Nearest Neighbors (KNN) Classifier, and Naive Bayes Classifier as standard Machine Learning (ML) algorithms, and Multi-Layer Perceptron (MLP) Classifier as a Deep Learning (DL) algorithm) on the model performance, classification accuracy and confusion matrix, as well as precision, recall and F1 score indicators were used as the model evaluation metrics. According to the results of this study, among the standard ML algorithms considered herein, application of the SVM classifying algorithm led to relatively higher modeling accuracies; therefore, the SVM algorithm was selected as the most appropriate ML algorithm in this research. Furthermore, when the SVM algorithm was used to classify different corn yield values and the number of classes increased from 2 to 4, the accuracy of the model reduced from 0.97 to 0.82; therefore, there is a trade-off between the number of classes and the accuracy of the model. Moreover, similarity between the result of the model developed herein regarding the effect of tillage type and soil amendments on corn yield classes and the ANOVA result of the other study conducted on similar dataset, acted as cross checking for the appropriateness of the model developed in this study. Finally, application of the MLP algorithm to classify each of the dependent variables considered herein, resulted in higher accuracies compared to the accuracies of the other standard ML algorithms.
To model the effect of two tillage operations (i.e. conventional and minimum tillage) and seven soil amendments (i.e. C, F, RF, RFM, RTiP, RML, RTiM) on the responses of Zea Mays L. (i.e. corn and stover yields, plant height at 6th and 10th leaf phases, and relative chlorophyll content of the crop leaves at 6th and 10th leaf phases), two-class and four-class classification modeling using the machine learning and multi-layer perceptron principles was performed. To examine the effect of different algorithms considered in the models (i.e. Decision Tree Classifier, Support Vector Machine (SVM) Classifier, K-Nearest Neighbors (KNN) Classifier, and Naive Bayes Classifier as standard Machine Learning (ML) algorithms, and Multi-Layer Perceptron (MLP) Classifier as a Deep Learning (DL) algorithm) on the model performance, classification accuracy and confusion matrix, as well as precision, recall and F1 score indicators were used as the model evaluation metrics. According to the results of this study, among the standard ML algorithms considered herein, application of the SVM classifying algorithm led to relatively higher modeling accuracies; therefore, the SVM algorithm was selected as the most appropriate ML algorithm in this research. Furthermore, when the SVM algorithm was used to classify different corn yield values and the number of classes increased from 2 to 4, the accuracy of the model reduced from 0.97 to 0.82; therefore, there is a trade-off between the number of classes and the accuracy of the model. Moreover, similarity between the result of the model developed herein regarding the effect of tillage type and soil amendments on corn yield classes and the ANOVA result of the other study conducted on similar dataset, acted as cross checking for the appropriateness of the model developed in this study. Finally, application of the MLP algorithm to classify each of the dependent variables considered herein, resulted in higher accuracies compared to the accuracies of the other standard ML algorithms.
Chivenge, P., B. Vanlauwe, R. Gentile and J. Six. 2011. Comparison of organic versus mineral resource effects on short-term aggregate carbon and nitrogen dynamics in a sandy soil versus a fine textured soil. Agric. Ecosyst. Environ. 140: 361–371. https:// doi.org/10.1016/j.agee.2010.12.004.
Grabowski, P. P., S. Haggblade, S. Kabwe and G. Tembo. 2014. Minimum tillage adoption among commercial smallholder cotton farmers in Zambia, 2002 to 2011. Agric. Syst. 131: 34–44. https://doi.org/10.1016/j.agsy.2014.08.001.
Ibrahim, A., R. Clement, D. Fatondji and A. Opoku. 2015. Hill placement of manure and fertilizer micro-dosing improves yield and water use efficiency in the Sahelian low input millet-based cropping system. F. Crop. Res. 180: 29–36. https://doi.org/10.1016/j.fcr.2015.04.022.
Kiboi, M., A. Fliessbach, A. Muriuki and F. Ngetich. 2022. Data on the response of Zea Mays L. and soil moisture content to tillage and soil amendments in the sub-humid tropics. Data Br. 43: 108381. https://doi.org/10.1016/j.dib.2022.108381
Kiboi, M. N., K. F. Ngetich, A. Fliessbach, A. Muriuki and D. N. Mugendi. 2019. Soil fertility inputs and tillage influence on maize crop performance and soil water content in the Central Highlands of Kenya, Agric. Water Manag. 217: 316–331. https://doi: 10.1016/j.agwat.2019.03.014 .
Kiboi, M. N., K. F. Ngetich, J. Diels, M. Mucheru-Muna, J. Mugwe and D. N. Mugendi. 2017. Minimum tillage, tied ridging and mulching for better maize yield and yield stability in the Central Highlands of Kenya. Soil Tillage Res. 170: 157–166. https://doi.org/10.1016/j.still.2017.04.001.
Lazcano, C., M. Gómez-Brandón, P. Revilla and J. Domínguez. 2013. Short-term effects of organic and inorganic fertilizers on soil microbial community structure and function. Biol. Fertil. Soils. 49: 723–733. https://doi.org/10.1007/s00374-012-0761-7.
Mucheru-Muna, M., D. Mugendi, P. Pypers, J. Mugwe, J. Kung’u, B. Vanlauwe and R. Merckx. 2014. Enhancing maize productivity and profitability using organic inputs and mineral fertilizer in Central Kenya small-hold farms. Exp. Agric. 50: 250–269. https://doi.org/10.1017/S0014479713000525.
Paul, B. K., B. Vanlauwe, F. Ayuke, A. Gassner, M. Hoogmoed, T. T. Hurisso, S. Koalab, D. Leleib, T. Ndabamenyea, J. Six, M. M. Pulleman. 2013. Medium-term impact of tillage and residue management on soil aggregate stability, soil carbon and crop productivity. Agric. Ecosyst. Environ. 164: 14–22. https://doi.org/10.1016/j.agee. 2012.10.003.
Tesfahunegn, G. B. 2015. Short-term effects of tillage practices on soil properties under Tef [Eragrostis tef (Zucc. Trotter)] crop in Northern Ethiopia. Agric. Water Manag. 148: 241–249. https://doi.org/10.1016/j.agwat.2014.10.004.
Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. 2021. Dive into deep learning. Release 0.16.1.
