Development of a Portable IoT and Machine Learning-Based Soil Health Analyzer to Improve Fertilizer Use Efficiency Among Smallholder Farmers in Rwanda - Charite Uwatwembi (African Leadership University, 2025)
Agriculture is an important driver of Rwanda's economic system, employing over 70% of the population. Still, the sector is characterized by constantly low productivity attributed to inefficient fertilizer use and poor soil fertility control. Despite government initiatives, Rwanda’s fertilizer usage rates average about 72 kg per hectare, much below the national target of 94.6 kg per hectare. Moreover, as of the 2023 growing season, only approximately 51% of farmers actively used chemical fertilizers, resulting in considerable yield gaps. These practices, coupled with insufficient soil diagnostics, have caused extreme soil degradation, worsened by Rwanda’s hilly terrain, in which over 745,000 hectares of agricultural land are yearly suffering from erosion, resulting in a productivity loss that equates to approximately 5.5% of agricultural GDP. The absence of timely and accurate soil analysis (such that farmers are empowered to optimally exploit fertilizers, including minimizing the costs of over-application) has traditionally delayed the agricultural efficiency of farmers because traditional laboratory-based soil analyses are expensive and centralized and have generally led to delayed results that are by far too late for real-time decision-making in farming. To address these critical gaps, this study's project recommends the layout of a portable, cost-effective Internet of Things (IoT) and machine learning (ML)-centered soil fitness analyzer. The device I am proposing combines low-cost sensors for the measurement of important soil parameters such as the pH, the moisture content, and the degree of nutrients directly on the farm. Data collected from those sensors will be analyzed in real time by a machine learning model developed on local soil statistics to provide customized, particular fertilizer suggestions. The machine guarantees complete accessibility by way of presenting twin interfaces: a responsive web-based dashboard for farmers with internet connectivity and a one-way SMS notification gadget for smallholder farmers without internet access, facilitating full-scale adoption throughout diverse farmer demographics. The resolution receives support through its ability to boost fertilizer use performance while reducing costs and time and minimizing environmental damage from fertilizer runoff and erosion. The project supports Rwanda's national goals by providing smallholder farmers with personalized soil information at the right time, which matches their specific farm needs. The implementation of this system will result in higher agricultural yields, decreased food waste, and increased farmer income, which will drive broader socioeconomic development and strengthen Rwanda's agricultural sector.
This project develops a portable IoT + machine learning soil health analyzer tailored for smallholder farmers in Rwanda. The device measures soil pH, moisture, and nutrient levels and issues fertilizer suggestions via a web dashboard and SMS interface. This preprint includes embedded diagrams and images in the PDF and represents the full final draft before journal submission.