A Deep Learning-Based Decision Support Tool for Plant-Parasitic Nematode Management
Plant-parasitic nematodes (PPN), especially sedentary endoparasitic nematodes like root-knot nematodes (RKN), pose a significant threat to major crops and vegetables. They are responsible for causing substantial yield losses, leading to economic consequences, and impacting the global food supply. Th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/9/11/240 |
| _version_ | 1797458785865826304 |
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| author | Top Bahadur Pun Arjun Neupane Richard Koech |
| author_facet | Top Bahadur Pun Arjun Neupane Richard Koech |
| author_sort | Top Bahadur Pun |
| collection | DOAJ |
| description | Plant-parasitic nematodes (PPN), especially sedentary endoparasitic nematodes like root-knot nematodes (RKN), pose a significant threat to major crops and vegetables. They are responsible for causing substantial yield losses, leading to economic consequences, and impacting the global food supply. The identification of PPNs and the assessment of their population is a tedious and time-consuming task. This study developed a state-of-the-art deep learning model-based decision support tool to detect and estimate the nematode population. The decision support tool is integrated with the fast inferencing YOLOv5 model and used pretrained nematode weight to detect plant-parasitic nematodes (juveniles) and eggs. The performance of the YOLOv5-640 model at detecting RKN eggs was as follows: precision = 0.992; recall = 0.959; F1-score = 0.975; and mAP = 0.979. YOLOv5-640 was able to detect RKN eggs with an inference time of 3.9 milliseconds, which is faster compared to other detection methods. The deep learning framework was integrated into a user-friendly web application system to build a fast and reliable prototype nematode decision support tool (NemDST). The NemDST facilitates farmers/growers to input image data, assess the nematode population, track the population growths, and recommend immediate actions necessary to control nematode infestation. This tool has the potential for rapid assessment of the nematode population to minimise crop yield losses and enhance financial outcomes. |
| first_indexed | 2024-03-09T16:43:08Z |
| format | Article |
| id | doaj.art-891c510e251541ac90ca3a43df561249 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-09T16:43:08Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-891c510e251541ac90ca3a43df5612492023-11-24T14:50:04ZengMDPI AGJournal of Imaging2313-433X2023-11-0191124010.3390/jimaging9110240A Deep Learning-Based Decision Support Tool for Plant-Parasitic Nematode ManagementTop Bahadur Pun0Arjun Neupane1Richard Koech2School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, AustraliaSchool of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, AustraliaSchool of Health, Medical and Applied Sciences, Central Queensland University, Bundaberg, QLD 4760, AustraliaPlant-parasitic nematodes (PPN), especially sedentary endoparasitic nematodes like root-knot nematodes (RKN), pose a significant threat to major crops and vegetables. They are responsible for causing substantial yield losses, leading to economic consequences, and impacting the global food supply. The identification of PPNs and the assessment of their population is a tedious and time-consuming task. This study developed a state-of-the-art deep learning model-based decision support tool to detect and estimate the nematode population. The decision support tool is integrated with the fast inferencing YOLOv5 model and used pretrained nematode weight to detect plant-parasitic nematodes (juveniles) and eggs. The performance of the YOLOv5-640 model at detecting RKN eggs was as follows: precision = 0.992; recall = 0.959; F1-score = 0.975; and mAP = 0.979. YOLOv5-640 was able to detect RKN eggs with an inference time of 3.9 milliseconds, which is faster compared to other detection methods. The deep learning framework was integrated into a user-friendly web application system to build a fast and reliable prototype nematode decision support tool (NemDST). The NemDST facilitates farmers/growers to input image data, assess the nematode population, track the population growths, and recommend immediate actions necessary to control nematode infestation. This tool has the potential for rapid assessment of the nematode population to minimise crop yield losses and enhance financial outcomes.https://www.mdpi.com/2313-433X/9/11/240plant-parasitic nematodesroot-knot nematodesYOLO modelnematode detection/countingprototype tooldecision support tool |
| spellingShingle | Top Bahadur Pun Arjun Neupane Richard Koech A Deep Learning-Based Decision Support Tool for Plant-Parasitic Nematode Management Journal of Imaging plant-parasitic nematodes root-knot nematodes YOLO model nematode detection/counting prototype tool decision support tool |
| title | A Deep Learning-Based Decision Support Tool for Plant-Parasitic Nematode Management |
| title_full | A Deep Learning-Based Decision Support Tool for Plant-Parasitic Nematode Management |
| title_fullStr | A Deep Learning-Based Decision Support Tool for Plant-Parasitic Nematode Management |
| title_full_unstemmed | A Deep Learning-Based Decision Support Tool for Plant-Parasitic Nematode Management |
| title_short | A Deep Learning-Based Decision Support Tool for Plant-Parasitic Nematode Management |
| title_sort | deep learning based decision support tool for plant parasitic nematode management |
| topic | plant-parasitic nematodes root-knot nematodes YOLO model nematode detection/counting prototype tool decision support tool |
| url | https://www.mdpi.com/2313-433X/9/11/240 |
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