Dataset of cannabis seeds for machine learning applications

被引:5
|
作者
Chumchu, Prawit [1 ]
Patil, Kailas [2 ]
机构
[1] Kasetsart Univ, Sriracha, Thailand
[2] Vishwakarma Univ, Pune, India
来源
DATA IN BRIEF | 2023年 / 47卷
关键词
Cannabis detection; Cannabis seed image dataset; Computer vision; Deep learning; Machine learning; Seeds classification;
D O I
10.1016/j.dib.2023.108954
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The recent changes in policies in several countries regard-ing cannabis use has increased cannabis usage and research [1 , 2] . Cannabis is the second most used psychoactive sub-stance word-wide [3] . Cannabis remains the subject of many research works. The cannabis can be classified into different classes according to their external features like colour, shape, and size using some computer vision and machine learn-ing techniques. Precise classification or recognition is the un-met need of the agriculture business. This attracts many re-searchers to produce solutions with machine learning and deep learning techniques. Neat and clean dataset is the pri-mary requirements to build accurate and robust machine learning model and minimize misclassification for the real-time environment. To achieve this objective, we have cre-ated an image dataset of cannabis seed. Accordingly, we have considered seventeen cannabis seeds to create dataset. The dataset contains 17 subfolders of cannabis seeds and folder is named with the category of seed. We strongly believe the cannabis seeds dataset will be very helpful for training, test-ing, and validation of cannabis classification or recognition with machine learning models.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
引用
收藏
页数:9
相关论文
共 50 条
  • [21] WeedCube: Proximal hyperspectral image dataset of crops and weeds for machine learning applications
    Ram, Billy G.
    Mettler, Joseph
    Howatt, Kirk
    Ostlie, Michael
    Sun, Xin
    DATA IN BRIEF, 2024, 56
  • [22] Performance evaluation of machine learning models on large dataset of android applications reviews
    Qureshi, Ali Adil
    Ahmad, Maqsood
    Ullah, Saleem
    Yasir, Muhammad Naveed
    Rustam, Furqan
    Ashraf, Imran
    MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (24) : 37197 - 37219
  • [23] Novel Dataset Creation of Varieties of Banana and Ripening Stages for Machine Learning Applications
    Manasa, T. N.
    Pushpalatha, M. P.
    COMPUTER VISION AND IMAGE PROCESSING, CVIP 2023, PT II, 2024, 2010 : 374 - 381
  • [24] Performance evaluation of machine learning models on large dataset of android applications reviews
    Ali Adil Qureshi
    Maqsood Ahmad
    Saleem Ullah
    Muhammad Naveed Yasir
    Furqan Rustam
    Imran Ashraf
    Multimedia Tools and Applications, 2023, 82 : 37197 - 37219
  • [25] Efficient Large Scale Medical Image Dataset Preparation for Machine Learning Applications
    Denner, Stefan
    Scherer, Jonas
    Kades, Klaus
    Bounias, Dimitrios
    Schader, Philipp
    Kausch, Lisa
    Bujotzek, Markus
    Bucher, Andreas Michael
    Penzkofer, Tobias
    Maier-Hein, Klaus
    DATA ENGINEERING IN MEDICAL IMAGING, DEMI 2023, 2023, 14314 : 46 - 55
  • [26] Open access dataset of holographic videos for codec analysis and machine learning applications
    Gilles, Antonin
    Gioia, Patrick
    Madali, Nabil
    El Rhammad, Anas
    Morin, Luce
    2023 15TH INTERNATIONAL CONFERENCE ON QUALITY OF MULTIMEDIA EXPERIENCE, QOMEX, 2023, : 258 - 263
  • [27] A survey on dataset quality in machine learning
    Gong, Youdi
    Liu, Guangzhen
    Xue, Yunzhi
    Li, Rui
    Meng, Lingzhong
    INFORMATION AND SOFTWARE TECHNOLOGY, 2023, 162
  • [28] A benchmark dataset for machine learning in ecotoxicology
    Christoph Schür
    Lilian Gasser
    Fernando Perez-Cruz
    Kristin Schirmer
    Marco Baity-Jesi
    Scientific Data, 10
  • [29] A benchmark dataset for machine learning in ecotoxicology
    Schuer, Christoph
    Gasser, Lilian
    Perez-Cruz, Fernando
    Schirmer, Kristin
    Baity-Jesi, Marco
    SCIENTIFIC DATA, 2023, 10 (01)
  • [30] CuneiML: A Cuneiform Dataset for Machine Learning
    Chen, Danlu
    Agarwal, Aditi
    Berg-Kirkpatrick, Taylor
    Myerston, Jacobo
    JOURNAL OF OPEN HUMANITIES DATA, 2023, 9