A TinyML Approach for the Classification of Bean Crop Diseases

Modern agriculture has enabled food production for nearly eight billion people, yet plant diseases and climate change continue to threaten food security. Beans, a key nutritional crop worldwide, are vulnerable to diseases such as bean rust and angular leaf spot, which significantly reduce yield. Early detection is critical for effective treatment. While existing methods leverage cloud-based Deep Learning (DL) models for plant disease classification, they often require substantial computational resources. In this work, we propose an edge-based solution by deploying a quantized MobileNetV2 model on a resourceconstrained embedded device. We compare the performance of the full-precision (Float 32-bit) model and its quantized (Int 8-bit) counterpart deployed on a microcontroller unit (MCU), targeting Internet of Things (IoT) scenarios. Our study analyzes model accuracy, inference speed, and memory utilization, focusing on peak RAM and flash memory requirements. The results show that quantization significantly reduces memory footprint and inference time while maintaining competitive classification accuracy. These findings highlight the potential of quantization to enable efficient, sustainable, and deployable TinyML models for plant disease detection at the edge.