MangoLeafBD: A Comprehensive Image Dataset to Classify Diseased and Healthy Mango Leaves

TL;DR

This paper introduces MangoLeafBD, a comprehensive, publicly available image dataset of 4000 mango leaf images from Bangladesh, aimed at advancing machine learning-based disease classification in agriculture.

Contribution

The paper presents the first standard, ready-to-use dataset of mango leaves with disease labels, facilitating research in automated disease detection for mango cultivation.

Findings

Dataset contains 4000 images of 1800 leaves with seven diseases.

Collected from four orchards in Bangladesh, applicable to other regions.

Aims to promote machine learning research in agricultural disease classification.

Abstract

Agriculture is of one of the few remaining sectors that is yet to receive proper attention from the machine learning community. The importance of datasets in the machine learning discipline cannot be overemphasized. The lack of standard and publicly available datasets related to agriculture impedes practitioners of this discipline to harness the full benefit of these powerful computational predictive tools and techniques. To improve this scenario, we develop, to the best of our knowledge, the first-ever standard, ready-to-use, and publicly available dataset of mango leaves. The images are collected from four mango orchards of Bangladesh, one of the top mango-growing countries of the world. The dataset contains 4000 images of about 1800 distinct leaves covering seven diseases. Although the dataset is developed using mango leaves of Bangladesh only, since we deal with diseases that are common across many countries, this dataset is likely to be applicable to identify mango diseases in other countries as well, thereby boosting mango yield. This dataset is expected to draw wide attention from machine learning researchers and practitioners in the field of automated agriculture.