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Object Detection Model for Gender Screening of Cannabis Seeds

Published: 20 August 2023 Publication History

Abstract

Understanding and perception of cannabis are more expansive than they formerly were. Almost all growers are primarily interested in getting harvests of big flower buds from cannabis female plants since THC, CBD and other cannabinoids are found in female flowers and valuable for medical and industrial market segments. Selecting only female seeds to cultivate is an important step to produce THC, CBD profitably. Unfortunately, outdoor cultivation in Thailand traditionally grows regular cannabis seeds that grow up of mixed male and female plants. The male plants will be later spot and eliminated during the pre-flowering stage. This incurs the higher cost of investment and the economic loss consequence. A smart farming approach using AI technology is thus introduced for screening seed genders before cultivation. A dataset of cannabis seed images of Hang Kra Rog, a well-known Thai cannabis cultivar, was collected from several regions. Data augmentation techniques were carried out to increase the sample size and improve the quality of images. The two object detection models, YOLOv5, were constructed using the initial and augmented data. Compared to the original dataset, the performance of the model trained on the augmented image dataset achieved the better precision of 96.1%, recall of 97.1%, and mAP_0.5 of 98.3% with detection speed at 9.3 ms.

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Cited By

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  • (2024)Detection and Classification of Cannabis Seeds Using RetinaNet and Faster R-CNNSeeds10.3390/seeds30300313:3(456-478)Online publication date: 28-Aug-2024
  • (2024)Cannabis Seed Variant Detection Using Faster R-CNN2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS)10.1109/ICACCS60874.2024.10716962(1403-1408)Online publication date: 14-Mar-2024
  • (2023)Semi-Automated Image Annotation for Cannabis Seed Gender Detection Model2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)10.1109/SEAI59139.2023.10217788(189-193)Online publication date: 16-Jun-2023

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        cover image ACM Other conferences
        ICCTA '23: Proceedings of the 2023 9th International Conference on Computer Technology Applications
        May 2023
        270 pages
        ISBN:9781450399579
        DOI:10.1145/3605423
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Publication History

        Published: 20 August 2023

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        Author Tags

        1. cannabis seed gender
        2. object detection
        3. smart farming

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        Cited By

        View all
        • (2024)Detection and Classification of Cannabis Seeds Using RetinaNet and Faster R-CNNSeeds10.3390/seeds30300313:3(456-478)Online publication date: 28-Aug-2024
        • (2024)Cannabis Seed Variant Detection Using Faster R-CNN2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS)10.1109/ICACCS60874.2024.10716962(1403-1408)Online publication date: 14-Mar-2024
        • (2023)Semi-Automated Image Annotation for Cannabis Seed Gender Detection Model2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)10.1109/SEAI59139.2023.10217788(189-193)Online publication date: 16-Jun-2023

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