Aloe Vera Diseases Dataset for Classification of Aloe Vera Diseases Using Machine Learning

Published: 29 April 2024| Version 2 | DOI: 10.17632/cksmdjw8gy.2


This dataset serves as a valuable resource for researchers and practitioners in the fields of agriculture, machine learning, and computer vision, with a primary focus on the classification of diseases affecting Aloe vera plants. The dataset encompasses three main categories: Background Removed Aloe Vera Disease Dataset, Original Aloe Vera Disease Dataset, and Augmented Aloe Vera Disease Dataset. Each category contains images representing the diverse conditions of Aloe Vera plants, including Fresh, Rot, and Rust. By providing variations such as background removed images, original images, and augmented images, this dataset facilitates robust algorithm development and evaluation. Researchers can leverage these datasets to train and test machine learning models for accurate classification and early detection of Aloe Vera diseases. The dataset aims to accelerate advancements in agricultural technology, crop protection, and sustainable farming practices through innovative applications of computer vision and machine learning. A total of 2307 images of Fresh, Rust, and Rot Aloe Vera were collected from a rooftop garden in Jhenaidah and Munshigonj, Bangladesh, between November 2023 and January 2024. Subsequently, 9000 augmented images were generated from these originals using techniques such as flip, rotation, noise, shift, brighten, and zoom, aimed at increasing the dataset size while maintaining an image size of 800x800 pixels. Folder Structure: 1. Augmented Aloe Vera Disease Dataset: Number of datasets: 9000 Data format : .jpg 2. Background Removed Aloe Vera Disease Dataset: Number of datasets: 2450 Data format : .png 3. Original Aloe Vera Disease Dataset: Number of datasets: 2307 Data format : .jpg



Daffodil International University


Image Processing, Machine Learning, Image Classification, Plant Diseases, Deep Learning, Neural Network