外文摘要：Nowadays, many image processing and machine learning (ML) methods are used in mango-quality classification systems. Existing algorithms perform independently to capture the relationships between features in the dataset. Nevertheless, each method has its advantages and disadvantages. As a result, this study presents an ensemble-learning framework that combines the strengths of multiple ML algorithms to make predictions. Initially, different image processing algorithms are used to extract external mango features. Next, the dataset is constructed by combining those features with weight values from sensor signals. Following that, different ML algorithms are evaluated on the dataset to determine which ones are robust. Subsequently, various ensemble-learning approaches are deployed, such as bagging, boosting, and stacking. Finally, those models are evaluated and compared, to decide which model is suited for this study's dataset. In the experimental part, the assessment of errors demonstrates the usefulness of image processing algorithms. Furthermore, evaluation of the training models revealed that the stacking model, which integrates several methods in both the base learner and meta-learner, produced the highest results in precision, recall, F1-score, and accuracy, with values of 0.9855, 0.9901, 0.9876, and 0.9863, respectively. These experimental results confirm the robustness of the proposed methodologies for classifying mango quality.
外文关键词：machine learning;Feature extraction;fruit classification;mango grading;mango sorting;Vietnam agriculture
作者：Tai, Nguyen Duc;Lin, Wei Chih;Trieu, Nguyen Minh;Thinh, Nguyen Truong
作者单位：Natl Sun Yat Sen Univ;Univ Econ Ho Chi Minh City UEH
期刊名称：AGRONOMY-BASEL
期刊影响因子：0.0
出版年份：2024
出版刊次：14(4)
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台