Agriculture is a core sector of Uganda’s economy and the largest employer. According to the Uganda National Household Survey (UNHS) 2016/17, a bigger proportion of the working population is engaged in agriculture, forestry, and fishing (65%). Among the females in the working population, 70% are engaged in agriculture compared to 58% of the males.
Plantains, cassava, sweet potato, and maize are major subsistence crops. The major export crop is coffee, but tea, tobacco, and cotton are also important. Although many farmers sold food crops to meet short-term expenses, the government attempted to encourage diversification in commercial agriculture that would lead to a variety of nontraditional exports.
The agriculture sector had a total contribution to GDP at current prices of 24.9 percent in FY 2016/17 compared to 23.7 percent in FY 2015/16. The food crop subsector registered the highest contribution within the agricultural sector at 13.6 percent in FY 2016/17, an increase from 12.1 percent in FY 2015/16. The government has therefore concluded that investing in agriculture to achieve higher growth rates is the most effective way of reducing poverty.
Uganda’s agricultural sector presents multiple highly profitable investment opportunities both for profit-oriented investments and partnerships. While some steps are being taken to provide insurance against crop failures, access to finance for small-scale farmers is limited.
The high cost and limited availability of improved farm inputs, including hybrid seeds and post-harvest technology, over-stretched extension services, poor transport networks, a lack of market information, inadequate production and post-harvest facilities, and weak value chain linkages all hinder and frustrate subsistence farmers.
Despite the enormous progress in poverty reduction, about 40 percent of all rural people still live below the poverty line; the poorest regions being in the north and north-east, where civil conflict has severely disrupted the lives and agricultural production of small farmers.
1. Data collection, preprocessing, and dataset building.
2. Develop, train, and test deep learning models for image classification.