heading

2025 | Benin | Water issue adressed: Too dirty

Controlling Water in Market Gardening: Monitoring Water Quality, Using Chatbot for Improving Skills and Smart Irrigation

Urban market gardening is an essential source of food, employment, and income in Benin. However, this production relies heavily on the use of uncontrolled water. This exposes the crops, often consumed raw, to high risks of microbiological and chemical contamination. Local studies have revealed the presence of fecal coliforms, excessive turbidity, and non-compliant residues in irrigation water.

Yet, market gardeners lack simple tools to monitor water quality and apply treatments adapted to their context. This raises a dual challenge: protecting consumer health and ensuring sustainable and credible market gardening production.

To address this issue, the project proposes a three-part technological and educational solution:

  • –  Real-time detection of water quality via economical sensors (pH, turbidity, TDS)

  • –  A conversational assistant (chatbot) accessible via WhatsApp, which trains and improves the

    market gardener’s skills, while helping him to understand the malfunctions and anomalies recorded;

  • –  Smart irrigation, taking into account soil moisture, the crop, its growth stage and weather forecasts

    in order to optimally manage the available water stock and guarantee healthy harvests.

    This project is part of a dynamic of strengthening local capacities and securing agricultural practices, while directly contributing to SDGs 3 (health: by reducing the risks of diseases linked to the consumption of products irrigated with contaminated water), 4 (education: by improving the skills of market gardeners), 6 (clean water: by encouraging the use of clean and monitored water) and 12 (responsible consumption: by promoting responsible management of water resources).

    The sensors accurately detected critical thresholds (pH, turbidity, TDS), and the device responded efficiently, averaging 34 seconds for pH changes, 17 seconds for turbidity changes, and 11 seconds for conductivity and TDS. It displayed a relative accuracy rate of 93% for coliform determination. The chatbot correctly interpreted the data. Smart irrigation management resulted in water savings of up to 50% without impacting yields.