How AI can help reduce food loss and waste in Nigeria’s tomato value chain
Fruits and vegetables are essential to good nutrition, but in low- and middle-income countries (LMICs), people often do not consume enough of them, with most falling short of World Health Organization dietary recommendations.
- Nigeria
- tomato
- value chains
- Artificial Intelligence
By Futoshi Yamauchi, Aoi Fukuhara, Dauda Bawa, Caleb Olanipekun, and Olufemi PopoolaMarch 25, 2026
Key Takeaways
- An AI image analysis system will evaluate tomato quality and quantify post‑harvest losses in Nigeria.
- A pilot assessment in Jos, Nigeria, collected extensive visual and other data across multiple tomato varieties to train and validate the AI model.
- The system will be scalable and applicable to other crops, with potential to reduce food waste, improve supply‑chain efficiency, and support smallholder farmers.
Key takeaways
An AI image analysis system will evaluate tomato quality and quantify post‑harvest losses in Nigeria.
A pilot assessment in Jos, Nigeria, collected extensive visual and other data across multiple tomato varieties to train and validate the AI model.
The system will be scalable and applicable to other crops, with potential to reduce food waste, improve supply‑chain efficiency, and support smallholder farmers.
Fruits and vegetables are essential to good nutrition, but in low- and middle-income countries (LMICs), people often do not consume enough of them, with most falling short of World Health Organization dietary recommendations. One key reason for this problem is supply: in many LMICs, a substantial amount of production is lost before it reaches consumers due to the widespread lack of appropriate post-harvest handling and cold chains, limiting produce availability and affordability.
One challenge in reducing food loss and waste is understanding how quality deteriorates between farms and retail outlets. If weaknesses in specific links in the value chain can be identified, then solutions can be better targeted. To innovate on current, survey-based measures of food losses, we are developing an artificial intelligence-assisted, photo-based quality assessment in the tomato value chain in Nigeria. If successful, it will help us understand both the quality distribution (including the rate of spoilage) of tomatoes and the volume of loss in the tomato value chain through photos taken at wholesale and retail markets.