This repository contains the source code for the paper Reliable Deep Learning Plant Leaf Disease Classification Based on Light-Chroma Separated Branches by Joao Paulo Schwarz Schuler, Santiago Romani, Mohamed Abdel-Nasser, Hatem Rashwan and Domenec Puig. The baseline folder contains the source code used to train our baseline model while the two-paths folder contains our two-paths LAB feed Inception V3 variants.
The Food and Agriculture Organization (FAO) estimated that plant diseases cost the world economy $220 billion in 2019. In this paper, we propose a lightweight Deep Convolutional Neural Network (DCNN) for automatic and reliable plant leaf diseases classification. The proposed method starts by converting input images of plant leaves from RGB to CIE LAB coordinates. Then, L and AB channels go into separate branches along with the first three layers of a modified Inception V3 architecture. This approach saves from 1/3 to 1/2 of the parameters in the separated branches. It also provides better classification reliability when perturbing the original RGB images with several types of noise (salt and pepper, blurring, motion blurring and occlusions). These types of noise simulate common image variability found in the natural environment. We hypothesize that the filters in the AB branch provide better resistance to these types of variability due to their relatively low frequency in the image-space domain.
You may be interested in our other paper on the same topic and the same dataset Color-aware two-branch DCNN for efficient plant disease classification. You may also be interested at optimizing deeper layers of a DCNN: V1 and V2.
This project is an open source project. If you like what you see, please give it a star on github.
Bibtex:
@inbook{Schuler2021plant,
author = {Schuler, Joao and Romaní, Santiago and Abdel-nasser, Mohamed and Rashwan, Hatem and Puig, Domenec},
year = {2021},
month = {10},
pages = {375-381},
title = {Reliable Deep Learning Plant Leaf Disease Classification Based on Light-Chroma Separated Branches},
booktitle = {Artificial Intelligence Research and Development},
publisher = {IOS Press},
isbn = {9781643682105},
doi = {10.3233/FAIA210157}
}
Due to library updates, the code used for the paper doesn't run on current tensorflow/keras. As of the writting of this readme file, the current version of tensorflow is 2.7. An updated version of the code was done after the paper publication. This version is now compatible with tensorflow 2.7:
