Proposed Methodology on Enhancing Food Security in Nigeria Using Real Time Consumer Market Availability Application

As part of United Nations Sustainable Development goals, food security is one of the five targets of goal number 2 which is zero hunger by the year 2030. To achieve this market needs to function properly and citizens need timely information about food availability in the food market. This will make people to visit the market at appropriate time, buy what they need in its most nutritious (quality) state, consume fresh foods and thus prevent spoiling and wastages of perishable foods in the market. This paper proposed an artificial intelligent based mobile app which bridged the gap between sellers of tomato and consumers such that tomato does not overstay and be spoilt in the market. The research methodology has three stages: image classification, the recommender system using model-based deep reinforcement learning, and deep learning production. Image acquisition for trained tomato is done using ordinary digital camera, noise and background were removed. Image segmentation was done using artificial neural network (ANN) to separate tomato images into segment. Next, feature extraction was also done using feature fusion-based extraction while image classification into good, manageable or bad will be done using object-based ANN. A buyer can give feedback, and the feedback will be trained to provide future recommendation to another buyer. This was done using deep reinforcement learning using the Markov property. Both the model and the recommendation system were integrated into mobile app to make it accessible to the public.