Deep Learning - BSc - Edge AI on Embedded Systems for Gray Mold Disease Detection

Created on June 27, 2025

2025

Objective

Edge AI is a new paradigm in Machine Learning (ML) that compresses Deep Learning (DL) models for inference and training on the billions of edge devices that constitute the Internet of Things (IoT). This is especially impactful in Digital Agriculture, to enhance the productivity of farmers as well as the efficiency and sustainability of crops. However, Edge AI is challenging due to resource constraints of the hardware devices: typical microprocessors (MCUs) in the IoT have less than 2MB of Flash memory and 320KB of SRAM, with a processor frequency of no more than 100MHz. For this reason, existing DL architectures must be compressed and optimized for inference at the edge on Digital Agriculture applications.

How

Understanding of the fundamental computer vision models for image classification (i.e. [1][2]) and object detection ([3][4]) is key for this thesis. You will gain familiarity with existing resource-constrained DL architectures ([5][6]) and get to know the deployment process for DL models on modern MCUs, such as the STM32U5 series, MCX N94x or GAP9. You will apply the computer vision models for the detection of Gray Mold Disease (Botrytis cinerea) in different plants, such as wine grapes, strawberries or cucumbers. You will propose compression techniques for existing models, including pruning and quantization, with an emphasis on the reduction of power consumption. The proposed techniques will be validated on existing datasets (i.e. [7]) and/or real-world data.

Outputs

An extensive survey of computer vision Edge AI models for image classification and object detection. A new open-source framework of Computer Vision models for the detection of gray mold disease in Smart Agriculture. Validation of the framework for different datasets, with an emphasis on accuracy, memory and processing requirements as well as power consumption. If the validation yields state of the art results and if time permits, the publication and presentation of the results in an international conference.

Prerequisites

  1. Solid understanding of Machine Learning and Deep Learning fundamentals
  2. High-level coding skills in Python
  3. Nice to have or willing to learn: Coding skills in C
  4. Nice to have or willing to learn: Understanding the architecture of modern MCUs
  5. Willingness to contribute to the state-of-the-art Deep Learning models
  • [1] Howard, Andrew G., Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
  • [2] Tan, Mingxing, and Quoc V. Le. "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
  • [3] R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation"
  • [4] Wang, Chien-Yao, I-Hau Yeh, and Hong-Yuan Mark Liao. "Yolov9: Learning what you want to learn using programmable gradient information"
  • [5] Lin, Ji, Wei-Ming Chen, Yujun Lin, John Cohn, Chuang Gan, and Song Han. "MCUNet: Tiny Deep Learning on IoT Devices."
  • [6] A. Papaioannou, C.S. Kouzinopoulos, D. Ioannidis, and D. Tzovaras. "An Ultra-Low-Power Embedded AI Fire Detection and Crowd Counting System for Indoor Areas"
  • [7] https://zenodo.org/records/7383601