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

arXiv: https://arxiv.org/abs/2209.02377 · HTML: https://mlsanity.com/paper/2209.02377/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 46 references that are in the map.
- First posted on arXiv 27 Aug 2022.

## Builds on (most cited first, 1 of 1)

- [Using Deep Learning for Image-Based Plant Disease Detection](https://mlsanity.com/paper/1604.03169/) (2016)

## Built on by (most cited first, 13 of 13)

- [EMR-Merging: Tuning-Free High-Performance Model Merging](https://mlsanity.com/paper/2405.17461/) (2024)
- [Leveraging Vision Language Models for Specialized Agricultural Tasks](https://mlsanity.com/paper/2407.19617/) (2024)
- [Merging Vision Transformers from Different Tasks and Domains](https://mlsanity.com/paper/2312.16240/) (2023)
- [A Vision-Language Foundation Model for Leaf Disease Identification](https://mlsanity.com/paper/2505.07019/) (2025)
- [MASS: MoErging through Adaptive Subspace Selection](https://mlsanity.com/paper/2504.05342/) (2025)
- [MangoLeafViT: Leveraging Lightweight Vision Transformer with Runtime Augmentation for Efficient Mango Leaf Disease Classification](https://mlsanity.com/paper/2505.23961/) (2025)
- [Navigating the Accuracy-Size Trade-Off with Flexible Model Merging](https://mlsanity.com/paper/2505.23209/) (2025)
- [Speeding Up EfficientNet: Selecting Update Blocks of Convolutional Neural Networks using Genetic Algorithm in Transfer Learning](https://mlsanity.com/paper/2303.00261/) (2023)
- [A Novel Neural Network-Based Federated Learning System for Imbalanced and Non-IID Data](https://mlsanity.com/paper/2311.10025/) (2023)
- [Training-free Task Classification for Multi-Task Model Merging](https://mlsanity.com/paper/2606.22589/) (2026)
- [A Comparative Study of Custom CNNs, Pre-trained Models, and Transfer Learning Across Multiple Visual Datasets](https://mlsanity.com/paper/2601.02246/) (2026)
- [GourNet: A CNN-Based Model for Mango Leaf Disease Detection](https://mlsanity.com/paper/2604.27764/) (2026)
- [Learning to Recover Task Experts from a Multi-Task Merged Model](https://mlsanity.com/paper/2606.26902/) (2026)

## Abstract (arXiv)

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.