Assessing whether durian fruit is mature enough for harvesting is an important task. Currently, this task is often done manually by experienced experts. This study proposed a classification system of durian maturity before harvesting based on acoustic characteristics and machine learning for objective and experience-free assessment. A conventional microphone was placed into a sound-insulated tube to minimize the impact of surrounding noise at the durian orchard. A single-board computer was used to perform sound recording, analysis, and classification of durian maturity based on several popular machine learning models. Among the tested models, the K-Nearest Neighbors model revealed the best performance with accuracy and precision of 95.4% and 92.7%, respectively. This result shows that the proposed system has great potential for objectively classifying the maturity of durian fruit before harvesting.
Tạp chí khoa học Trường Đại học Cần Thơ
Lầu 4, Nhà Điều Hành, Khu II, đường 3/2, P. Xuân Khánh, Q. Ninh Kiều, TP. Cần Thơ
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