Automating Mushroom Culture Classification: A Machine Learning Approach
Traditionally, the classification of mushroom cultures has conventionally relied on manual inspection by human experts. However, this methodology is susceptible to human bias and errors, primarily due to its dependency on individual judgments. To overcome these limitations, we introduce an innovativ...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
The Science and Information Organization (SAI)
2024
|
| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/44674/3/Automating%20Mushroom%20Culture%20-%20Copy.pdf http://ir.unimas.my/id/eprint/44674/ https://thesai.org/Publications/ViewPaper?Volume=15&Issue=4&Code=IJACSA&SerialNo=54 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.unimas.ir.44674 |
|---|---|
| record_format |
eprints |
| spelling |
my.unimas.ir.446742024-05-03T00:06:43Z http://ir.unimas.my/id/eprint/44674/ Automating Mushroom Culture Classification: A Machine Learning Approach Hamimah, Ujir Irwandi Hipni, Mohamad Hipiny Mohamad Hasnul, Bolhassan Ku Nurul Fazira, Ku Azir Syed Asif, Ali QA75 Electronic computers. Computer science T Technology (General) Traditionally, the classification of mushroom cultures has conventionally relied on manual inspection by human experts. However, this methodology is susceptible to human bias and errors, primarily due to its dependency on individual judgments. To overcome these limitations, we introduce an innovative approach that harnesses machine learning methodologies to automate the classification of mushroom cultures. Our methodology employs two distinct strategies: the first involves utilizing the histogram profile of the HSV color space, while the second employs a convolutional neural network (CNN)-based technique. We evaluated a dataset of 1400 images from two strains of Pleurotus ostreatus mycelium samples over a period of 14 days. During the cultivation phase, we base our operations on the histogram profiles of the masked areas. The application of the HSV histogram profile led to an average precision of 74.6% for phase 2, with phase 3 yielding a higher precision of 95.2%. For CNN-based method, the discriminative image features are extracted from captured images of rhizomorph mycelium growth. These features are then used to train a machine learning model that can accurately estimate the growth rate of a rhizomorph mycelium culture and predict contamination status. Using MNet and MConNet approach, our results achieved an average accuracy of 92.15% for growth prediction and 97.81% for contamination prediction. Our results suggest that computer-based approaches could revolutionize the mushroom cultivation industry by making it more efficient and productive. Our approach is less prone to human error than manual inspection, and it can be used to produce mushrooms more efficiently and with higher quality. The Science and Information Organization (SAI) 2024-04-30 Article PeerReviewed text en http://ir.unimas.my/id/eprint/44674/3/Automating%20Mushroom%20Culture%20-%20Copy.pdf Hamimah, Ujir and Irwandi Hipni, Mohamad Hipiny and Mohamad Hasnul, Bolhassan and Ku Nurul Fazira, Ku Azir and Syed Asif, Ali (2024) Automating Mushroom Culture Classification: A Machine Learning Approach. International Journal of Advanced Computer Science and Applications, 15 (4). pp. 519-525. ISSN 2158-107X https://thesai.org/Publications/ViewPaper?Volume=15&Issue=4&Code=IJACSA&SerialNo=54 DOI: 10.14569/IJACSA.2024.0150454 |
| institution |
Universiti Malaysia Sarawak |
| building |
Centre for Academic Information Services (CAIS) |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Malaysia Sarawak |
| content_source |
UNIMAS Institutional Repository |
| url_provider |
http://ir.unimas.my/ |
| language |
English |
| topic |
QA75 Electronic computers. Computer science T Technology (General) |
| spellingShingle |
QA75 Electronic computers. Computer science T Technology (General) Hamimah, Ujir Irwandi Hipni, Mohamad Hipiny Mohamad Hasnul, Bolhassan Ku Nurul Fazira, Ku Azir Syed Asif, Ali Automating Mushroom Culture Classification: A Machine Learning Approach |
| description |
Traditionally, the classification of mushroom cultures has conventionally relied on manual inspection by human experts. However, this methodology is susceptible to human bias and errors, primarily due to its dependency on individual judgments. To overcome these limitations, we introduce an innovative approach that harnesses machine learning methodologies to automate the classification of mushroom cultures. Our methodology employs two distinct strategies: the first involves utilizing the histogram profile of the
HSV color space, while the second employs a convolutional
neural network (CNN)-based technique. We evaluated a dataset
of 1400 images from two strains of Pleurotus ostreatus mycelium samples over a period of 14 days. During the cultivation phase, we base our operations on the histogram profiles of the masked areas. The application of the HSV histogram profile led to an average precision of 74.6% for phase 2, with phase 3 yielding a higher precision of 95.2%. For CNN-based method, the discriminative image features are extracted from captured images of rhizomorph mycelium growth. These features are then used to train a machine learning model that can accurately estimate the growth rate of a rhizomorph mycelium culture and predict contamination status. Using MNet and MConNet approach, our results achieved an average accuracy of 92.15% for growth prediction and 97.81% for contamination prediction. Our results suggest that computer-based approaches could revolutionize the mushroom cultivation industry by making it more efficient and productive. Our approach is less prone to human error than manual inspection, and it can be used to produce mushrooms more efficiently and with higher quality. |
| format |
Article |
| author |
Hamimah, Ujir Irwandi Hipni, Mohamad Hipiny Mohamad Hasnul, Bolhassan Ku Nurul Fazira, Ku Azir Syed Asif, Ali |
| author_facet |
Hamimah, Ujir Irwandi Hipni, Mohamad Hipiny Mohamad Hasnul, Bolhassan Ku Nurul Fazira, Ku Azir Syed Asif, Ali |
| author_sort |
Hamimah, Ujir |
| title |
Automating Mushroom Culture Classification: A Machine Learning Approach |
| title_short |
Automating Mushroom Culture Classification: A Machine Learning Approach |
| title_full |
Automating Mushroom Culture Classification: A Machine Learning Approach |
| title_fullStr |
Automating Mushroom Culture Classification: A Machine Learning Approach |
| title_full_unstemmed |
Automating Mushroom Culture Classification: A Machine Learning Approach |
| title_sort |
automating mushroom culture classification: a machine learning approach |
| publisher |
The Science and Information Organization (SAI) |
| publishDate |
2024 |
| url |
http://ir.unimas.my/id/eprint/44674/3/Automating%20Mushroom%20Culture%20-%20Copy.pdf http://ir.unimas.my/id/eprint/44674/ https://thesai.org/Publications/ViewPaper?Volume=15&Issue=4&Code=IJACSA&SerialNo=54 |
| _version_ |
1800728059705294848 |
| score |
13.211869 |