BananaLSD: A banana leaf images dataset for classification of banana leaf diseases using machine learning
Shifat E. Arman,
Md. Abdullahil Baki Bhuiyan,
Hasan Muhammad Abdullah,
Shariful Islam,
Tahsin Tanha Chowdhury,
Md. Arban Hossain
Affiliations
Shifat E. Arman
Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka-1000, Bangladesh
Md. Abdullahil Baki Bhuiyan
Department of Plant Pathology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
Hasan Muhammad Abdullah
GIS and Remote Sensing Lab, Department of Agroforestry and Environment, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh; Corresponding author.
Shariful Islam
GIS and Remote Sensing Lab, Department of Agroforestry and Environment, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
Tahsin Tanha Chowdhury
GIS and Remote Sensing Lab, Department of Agroforestry and Environment, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
Md. Arban Hossain
Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka-1000, Bangladesh
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 smartphone 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.
