BananaLSD: A banana leaf images dataset for classification of banana leaf diseases using machine learning

被引:2
|
作者
Arman, Shifat E. [1 ]
Bhuiyan, Md. Abdullahil Baki [2 ]
Abdullah, Hasan Muhammad [3 ]
Islam, Shariful [3 ]
Chowdhury, Tahsin Tanha [3 ]
Hossain, Md. Arban [1 ]
机构
[1] Univ Dhaka, Dept Robot & Mechatron Engn, Dhaka 1000, Bangladesh
[2] Bangabandhu Sheikh Mujibur Rahman Agr Univ, Dept Plant Pathol, Gazipur 1706, Bangladesh
[3] Bangabandhu Sheikh Mujibur Rahman Agr Univ, Dept Agroforestry & Environm, GIS & Remote Sensing Lab, Gazipur 1706, Bangladesh
来源
DATA IN BRIEF | 2023年 / 50卷
关键词
Banana leaf; Disease detection; Image classification; Machine learning; Deep learning; Computer vision; Plant pathology;
D O I
10.1016/j.dib.2023.109608
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
Bananas, one of the most widely consumed fruits globally, are highly susceptible to various leaf spot diseases, leading to significant economic losses in banana production. In this article, we present the Banana Leaf Spot Diseases (BananaLSD) dataset, an extensive collection of images showcasing three prevalent diseases affecting banana leaves: Sigatoka, Cordana, and Pestalotiopsis. The dataset was used to develop the BananaSqueezeNet model [1] . The BananaLSD dataset contains 937 images of banana leaves collected from banana fields, which were then further augmented to generate another 1600 images. The images were acquired using three smart phone cameras in diverse real-world conditions. The dataset has potential for reuse in the development of machine learning models that can help farmers identify symptoms early. It can be useful for researchers working on leaf spot diseases and serve as motivation for further researches. (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/ )
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页数:8
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