Tech entrepreneur and software engineer Kondwani Nantchito.

The Mnzeru Mkupangwa mobile app, which uses Artificial Intelligence (AI), was launched in response to the challenges faced by Malawian farmers due to crop diseases and a lack of agricultural knowledge.

The App enables farmers to swiftly detect plant diseases using only a smartphone camera, transforming access to agricultural expertise in Malawi.

Malawian farmers have historically had limited access to latest farming expertise, according to a World Bank assessment critiquing their delayed adoption of modern farming techniques.

The Mnzeru Mkupangwa App overcomes this gap by focusing on Malawi's agricultural sector, where only 7% of the population is commercial farmers, yet 24% of this group continues to live in poverty.

Crop diseases contribute to significant economic losses—up to 40% in some cases. By providing real-time disease identification, the app aims to mitigate these losses, helping farmers secure more reliable yields and improve their economic stability.

Developed by tech entrepreneur and software engineer Kondwani Nantchito, the App utilises machine learning classification models trained on supervised data and it covers several crops, including maize, tomatoes, blueberry, pepper, and apple, among others.

Once users upload or take a photo of a diseased plant, the App quickly identifies the issue, displaying the disease name.

Beyond disease identification, Mnzeru Mkupangwa includes an AI chatbot feature that assists farmers with general farming questions. The App interface is supports local languages, such as Chichewa and English, making it accessible to a broader range of users.

Nantchito says: "The app is already seeing positive feedback from farmers, who appreciate the practical manuals and knowledge offered through the AI chatbot. Looking forward, the team plans to add agricultural guides in local languages, aiming to expand accessibility and boost Malawi’s agricultural sector.

“This plays a crucial role in enhancing crop productivity by providing a reliable disease detection feature, allowing farmers to identify and address crop issues early. In Sub-Saharan Africa, where crop diseases and pests are estimated to reduce yields by up to 40% annually, this is particularly important.”