A Performance Comparison of Classification Algorithms for Rose Plants

Abstract

One of the key roles of Botanists is to be able to recognize flowers. This role has become highly challenging given that the number of discovered flower types are nearing half a million. To support Botanists, Information Technology offers promising solutions. Specifically, machine learning techniques are intrinsically appealing due to being precise enough as required. To this aim, two observations on flower leaves are relevant and leverage flower identification: one, flower plants exhibit unique features in their leaves thus allow distinction of their co-located flowers; two, leaves have a much longer life than flowers thus preserve identity properties longer. This paper proposes the use of machine learning-based identification of rose types by leveraging the features from their leaves. For this purpose, the performance of Naive Bayes, Generalized Linear Model, Multilayer Perceptron, Decision Tree, Random Forest, Gradient Boosted Trees, and Support Vector Machine has been analyzed. This study optimizes the RF model by investigating and tuning its various parameters such as the number of trees, the depth of trees, and splitting criteria. The best results are achieved with gain ratio because it takes more distinct values to avoid the problems associated with Information Gain. Optimizing the number of trees and the depth of trees of RF yield better accuracy than other models. Extensive experiments are performed to analyze the results of ensemble algorithms by using the voting method for each instance. Results suggest that the performance of ensemble classifiers is superior to that of individual models.

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

“…The success rates for dead/alive plant detection for the LiDAR and light curtain sensors were 93.75% and 94.16%, respectively. Additionally, a few other studies have reported the application of machine vision approaches using different machine learning and deep learning methodologies for detecting and classifying different flower nurseries [ 71 , 80 , 81 , 82 , 83 , 84 ].…”

A Lightweight Attention-Based Convolutional Neural Networks for Fresh-Cut Flower Classification

“…The original classification of fresh-cut flowers is done manually [1], but owing to human subjectivity, visual fatigue, experience differences, and classification efficiency, it is difficult to meet the requirements of standardized production of freshcut flowers and cannot guarantee the efficiency, consistency, and stability of grade classification and quality evaluation of fresh-cut flowers. To effectively reduce the labor intensity of workers, manual intervention, and the loss of post-harvest processing of fresh-cut flowers, achieve high efficiency and quality, and meet the demands of automatic processing and classification line of fresh-cut flowers, it is necessary to use automatic processing and classification systems [2][3][4].…”

Feature extraction of 3D Chinese rose model based on color and shape features

“…Taking disease leaves of Chinese rose as the research object, Yin ( Yin et al., 2021 ) studied the feature extraction method from the disease region and proposed a flower disease classification model. Malik ( Malik et al., 2022 ) analyzed the performance of traditional classification methods, and provided a machine learning-based identification approach of rose types by leveraging the features from their leaves. Tigistu ( Tigistu and Abebe, 2021 ) studied a Chinese rose’s variety classification scheme using color and shape features, in which the color information was described by three statistical moments of the RGB, and the flower shape was described by Fourier coefficients.…”