User talk:Tumo Kgosiyame

Project Anton by Anton Tech

Over the next 15 years, sub-Saharan African farmers must produce 60% more food to meet its growing population’s demands.

Although Africa has most of the world’s arguable land, plant diseases pose a major threat to food security as it significantly reduces crop yield and compromises its quality. And while disease prevention is ideal, outbreaks are inevitable. The best path to increasing smallholder farmer productivity is reducing their impact.

For decades, farmers have relied on experts and experience. But recent technological advancements have digitized the once time-consuming process. By coupling deep learning and convolutional neural networks (CNNs), farmers can now identify plant diseases with the snap of a picture.

Aiming to address the looming threat of food security in Botswana, Tumo Kgosiyame and Kesego Mokgosi created Project Anton — a deep learning disease detection smart agent for crop and animal diseases.

What is Project Anton? The existing process for detecting and addressing a new disease that comes into the country is slow. Farmers may have to wait for weeks for extension services to analyze the data to figure out the disease. Project Anton is a disease detection smart agent that uses convolutional neural networks (CNN) to identify plant diseases and provide treatment recommendations.

How it works, users take a picture of the plant and send it to Project Anton via WhatsApp, Facebook Messenger, Twitter, or MMS. The neural networks analyze the image using data from verified sources about diseases and respond with the disease name and treatment recommendation.

We decided to integrate Project Anton with some existing platforms like WhatsApp, Facebook Messenger, and Twitter before rolling out our standalone mobile application to make access as frictionless as possible. We didn’t want to require users to install our application to use the service.