An Integrated Edge AI and IoT System for Quality Control, Waste Reduction and Predictive Maintenance in Small-Scale Food Processing

Small-scale food processing industries in
Rwanda and Malawi face persistent challenges in quality
control, energy efficiency, and supply chain management.
Traditional quality assessment methods are manual,
labour-intensive, and error-prone, leading to high
rejection rates, increased waste, and operational
inefficiencies, while the absence of real-time monitoring
results in excessive energy consumption and production
delays. This paper presents the design and development of
an integrated system that combines Artificial Intelligence
(AI) and the Internet of Things (IoT) to enhance
manufacturing efficiency, reduce waste, and improve
product quality. An AI-powered machine vision pipeline
built on Convolutional Neural Networks (CNNs) and
YOLOv8 performs real-time defect detection and
classification of maize grains, while IoT sensors monitor
machine health, environmental conditions, and energy
usage on the production line. Long Short-Term Memory
(LSTM) networks applied to sensor time-series data
forecast equipment failures, enabling predictive rather
than reactive maintenance. The system is deployed on edge
AI hardware, allowing on-site inference without
dependence on continuous cloud connectivity. Field
context motivates the work: post-harvest maize losses in
sub-Saharan Africa range between 15 and 30 percent,
roughly 25 percent of maize grains in Malawi are affected
by aflatoxin contamination, and 18 percent of processed
maize flour samples in Rwanda failed national quality
standards. Manual inspection processes approximately 100
kg of maize per hour against more than 1,000 kg per hour
for the automated system, a tenfold efficiency gain. The
solution is designed for affordability and scalability across
small and medium-sized enterprises, contributing to food
safety, waste reduction, and industrial competitiveness in
Rwanda and Malawi