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TSSM Detection Instance Segmentation

Instance Segmentation
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TSSM Detection Instance Segmentation Computer Vision Project

Gent Lab

Updated 3 months ago

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Instance Segmentation Model yolov8x yolov8 snap yolonas yolonas-s roboflow-3-n-seg
Classes (15)
Adult_female
Adult_male
Aphid
Dead_mite
Hatched_egg
Hatching_egg
Juvenile
Mite
Nonviable_egg
Predatory_mite
Predatory_mite_egg
Psyllobora_egg
Stethorus_egg
Thrips
Viable_egg

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Description

Here are a few use cases for this project:

  1. Agricultural Pest Management: This CV model could be used in agricultural technology to classify the stages and types of Two-Spotted Spider Mites (TSSM) and other insects on crops, aiding farmers in informed pest control.

  2. Biological Research: TSSM Detection v2 could be utilized in biodiversity research projects to automatically classify mite samples and better understand these species' life cycles, behaviors, and impact on ecological systems.

  3. Smart Greenhouse Management: This model can be incorporated into intelligent greenhouse systems to monitor and detect mite infestations early, enabling precise intervention to ensure plant health and high-quality production.

  4. Pest Control Industries: Commercial pest control companies can utilize this model to identify the severity and type of mite infestation in clients' homes or businesses, guiding appropriate treatment strategies.

  5. Educational Usage: The model can be used in educational settings, such as biology classes, to teach about mites and their life stages, allowing students to gain hands-on experience with artificial intelligence and computer vision technologies.

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Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            tssm-detection-instance-segmentation_dataset,
                            title = { TSSM Detection Instance Segmentation Dataset },
                            type = { Open Source Dataset },
                            author = { Gent Lab },
                            howpublished = { \url{ https://universe.roboflow.com/gent-lab/tssm-detection-instance-segmentation } },
                            url = { https://universe.roboflow.com/gent-lab/tssm-detection-instance-segmentation },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { aug },
                            note = { visited on 2024-11-16 },
                            }

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