Machine learning and Multi-Layer Perceptron (MLP) modeling of Zea Mays L. responses to tillage and soil amendments
محورهای موضوعی : Journal of Computer & Robotics
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.
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