Segmentation of mycotoxin's contamination in maize: A deep learning approach
Maize is the main staple food and feed inSub-Saharan African countries and is highly susceptible to mycotoxin contamination under opportune environmental conditions. The presence of mycotoxins in maize affects the health of consumers and impacts global trade. According to the literature, the lack of...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Informatics in Medicine Unlocked |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914823000904 |
| _version_ | 1797800972868648960 |
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| author | Judith Leo |
| author_facet | Judith Leo |
| author_sort | Judith Leo |
| collection | DOAJ |
| description | Maize is the main staple food and feed inSub-Saharan African countries and is highly susceptible to mycotoxin contamination under opportune environmental conditions. The presence of mycotoxins in maize affects the health of consumers and impacts global trade. According to the literature, the lack of mycotoxin awareness and the existence of strategies that are labor- and cost-prohibitive have led to the ongoing mycotoxin contamination in maize. Therefore, this study developed a cost-effective deep learning-based mobile application for segmentation of mycotoxin contamination in maize; using the RESNET152 model with performance rates of accuracy, test accuracy, epochs, time used, loss and image size results at 99.5%, 99.9%, 40, 07:30 min, and 0.051; and 460 respectively and performance evaluation metrics of F1-Score and sensitivity 0.62 and 0.997 respectively. During, the development processes, a total of 4800 images were collected and augmented. Then, the resulting 9600 data points were randomly shuffled and then split into the ratio of 70%:20:10% for training, validation, and testing datasets in order to avoid overfitting and biases in the resulting model. Lastly, the average result of model validation was 89% which was conducted among the farmers in the Maize area, Maize entrepreneurs, ICT experts, decision-makers from the Government, and policymakers. Therefore, the study recommends the collection of quality data which can be in the form of images, satellite, and biochemical properties of mycotoxin in order to enable researchers to analyze the contamination of mycotoxin and its linkages with environmental factors such as weather, soil characteristics, geographical position, and other unexpected events. |
| first_indexed | 2024-03-13T04:44:22Z |
| format | Article |
| id | doaj.art-58b2796e249d46798c1a8d47a1421144 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-03-13T04:44:22Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-58b2796e249d46798c1a8d47a14211442023-06-19T04:29:00ZengElsevierInformatics in Medicine Unlocked2352-91482023-01-0139101248Segmentation of mycotoxin's contamination in maize: A deep learning approachJudith Leo0School of Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, TanzaniaMaize is the main staple food and feed inSub-Saharan African countries and is highly susceptible to mycotoxin contamination under opportune environmental conditions. The presence of mycotoxins in maize affects the health of consumers and impacts global trade. According to the literature, the lack of mycotoxin awareness and the existence of strategies that are labor- and cost-prohibitive have led to the ongoing mycotoxin contamination in maize. Therefore, this study developed a cost-effective deep learning-based mobile application for segmentation of mycotoxin contamination in maize; using the RESNET152 model with performance rates of accuracy, test accuracy, epochs, time used, loss and image size results at 99.5%, 99.9%, 40, 07:30 min, and 0.051; and 460 respectively and performance evaluation metrics of F1-Score and sensitivity 0.62 and 0.997 respectively. During, the development processes, a total of 4800 images were collected and augmented. Then, the resulting 9600 data points were randomly shuffled and then split into the ratio of 70%:20:10% for training, validation, and testing datasets in order to avoid overfitting and biases in the resulting model. Lastly, the average result of model validation was 89% which was conducted among the farmers in the Maize area, Maize entrepreneurs, ICT experts, decision-makers from the Government, and policymakers. Therefore, the study recommends the collection of quality data which can be in the form of images, satellite, and biochemical properties of mycotoxin in order to enable researchers to analyze the contamination of mycotoxin and its linkages with environmental factors such as weather, soil characteristics, geographical position, and other unexpected events.http://www.sciencedirect.com/science/article/pii/S2352914823000904Deep learning algorithmsMaizeMycotoxinSub-Saharan African countryLabor- and cost-prohibitive tool |
| spellingShingle | Judith Leo Segmentation of mycotoxin's contamination in maize: A deep learning approach Informatics in Medicine Unlocked Deep learning algorithms Maize Mycotoxin Sub-Saharan African country Labor- and cost-prohibitive tool |
| title | Segmentation of mycotoxin's contamination in maize: A deep learning approach |
| title_full | Segmentation of mycotoxin's contamination in maize: A deep learning approach |
| title_fullStr | Segmentation of mycotoxin's contamination in maize: A deep learning approach |
| title_full_unstemmed | Segmentation of mycotoxin's contamination in maize: A deep learning approach |
| title_short | Segmentation of mycotoxin's contamination in maize: A deep learning approach |
| title_sort | segmentation of mycotoxin s contamination in maize a deep learning approach |
| topic | Deep learning algorithms Maize Mycotoxin Sub-Saharan African country Labor- and cost-prohibitive tool |
| url | http://www.sciencedirect.com/science/article/pii/S2352914823000904 |
| work_keys_str_mv | AT judithleo segmentationofmycotoxinscontaminationinmaizeadeeplearningapproach |