Application of Machine Learning in the Validation of the RIDS Software—A Tool for Assessing the Maturity Levels of Smallholder Farmer Organizations

Smallholder farmers in sub-Saharan Africa face a range of challenges, including limited access to global markets, difficulty in coping with swiftly revolutionizing value chains and distribution channels, and varying and distinct consumer preferences. Farmers have therefore increasingly opted to participate in collective action through Smallholder Farmer Organizations (SFOs). However, the methodology for evaluating the effectiveness of these SFOs remains challenging. This paper presents and validates the Rural Institutions Diagnostics Software (RIDS) - an automation of a participatory methodology for evaluating SFOs. Further, it develops an Artificial Neural Network (ANN) model and employs it not only in predicting the maturity levels of SFOs based on their internal governance, management, capacity, resilience and leadership structures but also determines whether the methodology in RIDS is reproducible by the model. Data collected through stratified random sampling from Kenya, Uganda and Tanzania, with 268 patterns of input-output vectors was used. The best performance based on the cross-entropy error was achieved with 94% patterns classified correctly from testing and 97% from validation. The findings validate the RIDS’ tool’s potential for use in evaluating SFOs’ maturity, hence it recommends it for application by governments, researchers, development partners and other relevant practitioners in the agricultural sector.