  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.
