CSU-Carig
Detection of molds in BPI-Pn9 peanut seeds using image analysis and machine learning [manuscript] / Reanne Joy G. Rafael, Melanie S. Capalungan, B-Jay L. Daguio, Isaac A. Balbuena. (Record no. 17583)
| 000 -LEADER | |
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| fixed length control field | 02000nam a22001697a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | Agri 0156 |
| Item number | 2022 c.1 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Rafael, Reanne Joy G., author. |
| 245 ## - TITLE STATEMENT | |
| Title | Detection of molds in BPI-Pn9 peanut seeds using image analysis and machine learning [manuscript] / Reanne Joy G. Rafael, Melanie S. Capalungan, B-Jay L. Daguio, Isaac A. Balbuena. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Year of publication | ©2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xvii, 73 pages ; |
| Other physical details | 28 cm. |
| 500 ## - GENERAL NOTE | |
| General note | Thesis (B.S.) -- Cagayan State University, 2022. Includes bibliographical references. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Peanuts with molds may contain aflatoxin, a highly carcinogenic substance that represents a health hazard. Most routine tests for molds in food samples like peanuts use conventional colony count methods rather than specific detection techniques, which require 5-7 days to achieve a result. In contrast, rapid detection methods can reduce this by 50% or more. This study aimed to identify mold-contaminated peanuts using image analysis and machine learning. Samples of peanut seeds were sterilized and subjected to inoculation, and a laboratory test was performed in accordance with FDA and DOST to confirm the presence of molds at 1.3×102 CFU/g. Detailed images of with and without molds peanuts were captured. The detection of molds on peanut surfaces was based on the "you only look once (YOLO) v5" principle. In this context, a dataset of 828 preprocessed and augmented images was used to detect mold-contaminated peanuts. The dataset was trained using the YOLOV5 algorithm. The precision and recall performance of the YOLOV5 model were determined. The YOLOV5 model performed well in 150 epochs, in a resolution of 640x640 pixels, with a confidence threshold of 0.75 and an IoU threshold of 0.50. It achieved a precision rate of approximately 99% with a recall rate of 100%. Keywords: Image Analysis, Machine Learning, Molds, Peanuts, yolov5. |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Capalungan, Melanie S. |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Daguio, B-jay Lucina |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Balbuena, Isaac A. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Academic Research |
| Source of acquisition | Permanent Location | Date acquired | Koha item type | Collection code | Accession Number | Lost status | Shelving location | Withdrawn status | Current Location | Full call number |
|---|---|---|---|---|---|---|---|---|---|---|
| School | Cagayan State University - Carig Library | 2023-03-22 | Academic Research | Academic Research | Agri01562022C1 | Academic Research Section | Cagayan State University - Carig Library | Agri 0156 2022 c.1 |