A comprehensive dataset of rice field weed detection from Bangladesh

被引:1
|
作者
Ali, Md Sawkat [1 ]
Rashid, Mohammad Rifat Ahmmad [1 ]
Hossain, Tasnim [2 ]
Kabir, Md Ahsan [2 ]
Kamrul, Md. [1 ]
Aumy, Sayam Hossain Bhuiyan [1 ]
Mridha, Mehedi Hasan [1 ]
Sajeeb, Imam Hossain [1 ]
Islam, Mohammad Manzurul [1 ]
Jabid, Taskeed [1 ]
机构
[1] East West Univ, Dept Comp Sci & Engn, Dhaka, Bangladesh
[2] Mil Inst Sci & Technol, Dept Elect Elect & Commun Engn, Dhaka, Bangladesh
来源
DATA IN BRIEF | 2024年 / 57卷
关键词
Automated weed detection; Rice field; Machine learning; Precision agriculture; Identification; Deep learning; Crop management;
D O I
10.1016/j.dib.2024.110981
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
In agricultural research, particularly concerning rice cultivation, the presence of weeds within rice fields is acknowledged as a significant contributor to both diminished crop quality and increased production costs. Rice fields, due to their inherently moist environment, offer ideal conditions for weed proliferation. Traditionally, the control of these weeds has been managed through labor-intensive manual methods. However, as the agricultural sector evolves, there is a notable pivot towards leveraging advanced technological solutions, including deep learning and machine learning. The efficacy of these technologies hinges on the availability of high-quality, relevant data. To address this, a comprehensive dataset comprising 3632 high-resolution RGB images has been developed. This dataset is designed to capture a diverse range of weed species, specifically 11 types that are frequently found in rice fields. The diversity of the dataset ensures that machine learning models trained using this data can effectively identify and differentiate between desired and undesired plant species. While the dataset predominantly includes images from Bangladesh, the weed species it documents are commonly found across various global rice-growing regions, enhancing the dataset's applicability in different agricultural settings. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC license ( http://creativecommons.org/licenses/by-nc/4.0/ )
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页数:13
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