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The KaraAgro AI Maize Dataset

Akogo, Darlington; Nakatumba-Nabende, Joyce; Samori, Issah; Acquaye, Christabel; Addo, Michael; Amoako ,Emmanuel; Cudjoe, Frank; Buaba, Jerry; Tusubira Francis, Jeremy; Namanya, Gloria

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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 in-field maize crop disease diagnosis and spatial analysis. This maize image dataset was collected across three different classes: Healthy, Fall armyworm, and Maize Streak Virus.

Context

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. Fall armyworm affects 44 countries in Africa. In new household surveys in Ghana and Zambia, 98% of farmers reported maize to be affected. The average maize loss reported by farmers in Ghana was 26.6% and in Zambia 35%. Extrapolating these losses nationally gives an estimate of US$177m lost value of the annual maize crop in Ghana and US$159m in Zambia. 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 previously collected is biased and not reproducible It has also not been sufficiently curated, prepared, and shared with the wider community.

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