Makerere University Maize Image Dataset
Babirye, Claire; Nakatumba-Nabende, Joyce; Namanya, Gloria; Mutebi, Chodrine; Ebellu, Moses; Murungi, Joab; Tobius, Saolo; Ssemwogerere, Jonah; Nakayima, Annet; Nabagereka, Deborah; Asasira, Judith; Kanyesigye, Ruth,
About the Dataset
The dataset was created to provide an open and accessible maize dataset with well-labeled, sufficiently curated, and prepared maize crop imagery that will be used by data scientists, researchers, the wider machine learning community, and social entrepreneurs within Sub-saharan Africa and worldwide for various machine learning experiments so as to build solutions towards infield maize crop disease diagnosis and spatial analysis.
Despite the fact that the agricultural sector is a national economic development priority in sub-Saharan Africa, crop pests and diseases have been the challenge affecting major food security crops like maize. Maize Leaf Blight, also known as Northern Corn Leaf Blight has become a menace in low land agro-ecologies, during the last decade. On the other hand, according to research, Maize Streak Disease which is caused by the Maize Streak Virus is regarded as the third most serious disease affecting maize in sub-Saharan Africa. The prominence of these diseases has greatly affected the yields of Africa’s most important food crop. The current state of data collection and crop pest and disease diagnosis is transitioning from disease identification using visible symptoms to the use of data-driven solutions applying machine learning and computer vision techniques. The image data that was collected previously is biased and not reproducible. It has also not been sufficiently curated, prepared, and shared with the wider community.