juliotorrest/msi_dl_optimization

MSI DL optimization

This repository implements the work described in the paper "Optimizing a Multispectral-Images-Based DL Model, Through Feature Selection, Pruning and Quantization", that will be presented at the 2022 IEEE International Symposium on Circuits & Systems, to be held in Austin, Texas USA from May 28 through June 1, 2022. The reference will be added after the paper is published.

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The inclusion of technology in agriculture is highly relevant given the increasing global demand for food and our growing population. This paper focuses on an application for the analysis of multispectral images of wheat fields, and how they could be used to predict yield in a way that is resource-efficient and time convenient. Thus, our main goal is to optimize a deep learning model already proposed in the literature, through feature selection, pruning, and quantization, to be efficient enough that it could be deployed on a computer with limited resources. The main results of this work show that the size of the model was reduced by almost 94%, its inference time was almost 73% faster compared to the original model, while reducing its performance by 19% (still better than what was found in literature). This could be an important step towards the deployment of edge intelligence for plant phenotyping.