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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
The Science and Information Organization (SAI)
2024
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| 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 |
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| Summary: | 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. |
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