/plant-disease-experiments

Primary LanguagePythonMIT LicenseMIT

Plant Disease Detection using Deep Learning

A Project to Train and Evaluate different DNN Models for plant disease detection problem, tackle the problem of scarce real-life representative data, experiment with different generative networks and generate more plant leaf image data and Implement segmentation pipeline to avoid misclassification due to unwanted input

Using Deep Learning for Image-Based Plant Disease Detection

Resources:

Objective

  • Train and Evaluate different DNN Models for plant disease detection problem

  • To tackle the problem of scarce real-life representative data, experiment with different generative networks and generate more plant leaf image data

  • Implement segmentation pipeline to avoid misclassification due to unwanted input

Approches for Solving the papers realtime Detection Problem

phase 1 : implement the paper

phase 2 : do analysis on the paper and identify the type of data problem

phase 3 : experiment and if possible generate appropriate data using the data to train the model again

Project Structure

Plant_Disease_Detection_Benchmark_models

  • Train and test different prediction models to get a baseline accuracy to compare to and see progress

Plant_Disease_Detection_gan_experiments

  • experiment with different generative networks to see their generative capability and if the output can be used to train more robust models

leaf-image-segmentation-segnet

  • segmentation pipeline using VGGSegNet Architecture

leaf-image-segmentation

  • histogram based segmentation Pipline

Usage

python main.py IMAGE_FILE [--segment] [--species SPECIES_TYPE] [--model PREDICTION_MODEL]

Arguments:
	IMAGE_FILE    Path of the image file
	--segment     If specified perform segmentation on the image before prediction
	--species     If the plant species on the image is priorly known. One of the following species:  Apple, Cherry, Corn, Grape, Peach, Pepper, Potato, Strawberry, Sugercane, Tomato
	--model       What models do you want to use, vgg or inceptionv3

Examples

# you can remove a part of arguments except image path

>>  python main.py 'test/a.jpg' --segment --species 'apple' --model 'inceptionv3'

  • Before using that make sure you download the weights from here for Inception_V3 and here for VGG Models and extract all and put it in Plant_Disease_Detection_Benchmark_models/Models/ folder.

  • This will segment the image and predict the output class based on that. Segmented image will be saved as the file name with "_marked" suffix before the file extension.

  • The images are trained with segmented network and lower performance on unsegmented dataset is expected.

  • You can check the segmentation accuracy from saved image.

  • Fill this form for bulk model access grants and future update notification.