Ripeness Classification of Cacao Using Cepstral-Based Statistical Features and Support Vector Machine

Author:

Galindo Juvy Amor M.1,Rosal Jamie Eduardo C.1,Villaverde Jocelyn Flores2

Affiliation:

1. College of Engineering and Technology, Cor Jesu College, Inc.,Digos City,Philippines

2. School of Electrical, Electronics and Computer Engineering, Mapua University,Manila,Philippines

Publisher

IEEE

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Comparison Between L*A*B and HSV Image Segmentation with CNN in detecting fish freshness;Proceedings of the 2024 10th International Conference on Computing and Artificial Intelligence;2024-04-26

2. Identification of Salt Applying Image Processing and Machine Learning Techniques;2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM);2023-11-19

3. Detection of Chronic Myelogenous Leukemia Applying Support Vector Machine and Feature Extraction;2023 Seventh International Conference on Advances in Biomedical Engineering (ICABME);2023-10-12

4. A Cocoa Ripeness Detection and Classification Model Based on Improved YOLOv5s;2023 10th International Conference on Advanced Informatics: Concept, Theory and Application (ICAICTA);2023-10-07

5. RipSetCocoaCNCH12: Labeled Dataset for Ripeness Stage Detection, Semantic and Instance Segmentation of Cocoa Pods;Data;2023-06-18

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