shubham10divakar / plant-disease-experiments

Plant Disease Detection using Deep Learning

Using Deep Learning for Image-Based Plant Disease Detection

Resources:

• Data set

• Additional dataset will be relesed

• Models

Objective

• Train and Evaluate different DNN Models for plant deasise 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 missclassification 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 : experement and if possible generate Apprprate data using the data 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 pipline using VGGSegNet Architecture

leaf-image-segmentation

• histogram based segmentation Pipline

Usage

Python main.py [--image  IMAGE FILE] [--segment BOOLIAN PARAMETER] [--species SPECIES TYPE] [--Model PREDICTION_MODEL]

arguments

	- --image       loaction of the image
	- --segment     True to  Segment before prediction , False not to 
	- --species     one of the Following Specious :  Apple,Cherry,Corn, Grape,Peach, Pepper,Potato,Strawberry, Sugercane, Tomato
	- --model       what models do you want to use VGG or Inception_V3

Examples

	 	# you can remove a part of arguments except image path

	 >>  python main.py --image "test/a.jpg" --segment True --species "Apple" --model 'Inception_v3'

• 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 "_masked" prefix.

• the images are traine with segmented network and lower performance on unsegmented dataset is expected

• You can cheack the segmentation accuracy from saved image