AI and economic empowerment of women in agriculture

AI and economic empowerment of women in agriculture

The role of women in agriculture in Africa is widely recognised and documented. In Senegal, they are involved in agricultural work to the tune of 62.6% in rural areas (Profil genre dans l'agriculture Sénégal/ FAO CEDEAO 2018). Family farms, where women work, make little use of technology. Yet innovation through intelligent solutions is seen as a strategy for transforming agriculture and making it more efficient. How can women, major players in the agricultural sector, benefit from appropriate AI solutions? The aim is to propose AI solutions to improve and strengthen the economic empowerment of women in agriculture, particularly in the following areas:

  • Access to and control of land resources and other factors of production by men and women
  • Profitability and productivity
  • The development of other areas where women have a strong presence, such as the processing and marketing of agricultural products
Technologies are used in agriculture. Around the world, AI solutions are being implemented to help make agriculture more profitable and efficient (Plantix, etc.). AI is seen as an area that can help revolutionise traditional agriculture. What is the situation in Africa? We've seen the creation of integrated software that captures data via drones or sensors: on irrigation and humidity, with a view to boosting yields and productivity. What's more, a number of start-ups have been set up and are contributing to the development of e-agriculture at various levels, including MLouma and Grenier Intelligent in Senegal. As far as land management is concerned, tools such as the Plan d'Occupation et d'Affectation des Sols (POAS) are integrating gender to a greater extent. How can these different tools, which use data on agriculture and land tenure, promote the deployment of inclusive AI solutions through machine learning, making agriculture more profitable and sustainable while analysing and correcting inequalities between men and women in agriculture and access to productive resources?

Similar articles

Not that long ago, people lived and functioned in tight communities. Every vendor knew their customers personally and could make...


This Machine Learning Glossary aims to briefly introduce the most important Machine Learning terms - both for the commercially and...