Detecting diseased crops in Africa using Artificial Intelligence (AI) has been difficult due to lack of datasets.

As a result, African scientists are forced to rely on data from western countries which can produce inaccurate results.

Fortunately, scientists at the Responsible Artificial Intelligence Lab at the Kwame Nkrumah University of Science and Technology (RAIL-KNUST), have been able to produce datasets which makes it possible for African scientists to use them to create accurate systems to detect African plant diseases.

This dataset's collection of annotated photos of leaves from diverse crops, showing both healthy specimens and those affected by illnesses, is one of its main components.

These pictures show the subtle symptoms of crop diseases at various phases of crop development.

The dataset's emphasis on inclusion, which makes sure that it captures the agricultural diversity found in Africa, is essential to understanding its value.

The dataset offers a thorough understanding of disease patterns and manifestations by including annotated pictures of leaves from a range of crops.

Researchers, data scientists, and innovators who want to create specialized and efficient solutions for the agricultural problems the African continent faces will find this wealth of knowledge very useful.

 “What we have done at RAIL is to work with local farmers in creating datasets that can be used by developers within the African space.

“This is one of the first steps in our attempts at creating several of such datasets so that AI practitioners within the African space can rely on such data in creating solutions for the African environment,” said principal Investigator of RAIL-KNUST, Prof. Jerry John Kponyo.