Develop a system that can classify and detect leaf diseases in potato plants based on deep learning. This system can help farmers and agricultural researchers to get accurate and fast diagnose results of disease in plants, especially in potato plant.
clone : https://github.com/rizqiamaliatuss/PotatoLeafDiseaseClassification.git
This experiment consist of 3 major step :
--- Datasets was collected from :
- PlantVillage datasets = https://www.kaggle.com/emmarex/plantdisease
- Potato Plantation, Malang, Indonesia.
- Google Images = i recommend you to follow this tutorial to collect dataset from google images https://www.pyimagesearch.com/2017/12/04/how-to-create-a-deep-learning-dataset-using-google-images/
--- Clustering dataset
Divide dataset into 5 class : Altenaria Solani, Healthy Leaf, Virus, Insect and Phytophthora Infestan.
--Dataset +--- Alternaria Solani +--- Healthy Leaf +--- Virus +--- Insect +--- Phytophthora Infestan
--- Cropping image cropping image aim to delete noise in images, so we will get more spesific dataset.
--- Resizing image Resize image (224x224)
--- configure models In this experiment, we use VGG16 and VGG19 architecture models. To configure models, please remember this folder :
-- finalproject
+--- VGG16.py #Class VGG16
+--- VGG19.py #Class VGG19
also remember name of class in models file. after that configure model to training program.
--- Training Program check inisialisasi model in program
--- create folder output --- run the program
python train_vgg.py --dataset dataset --model output/finalprojectb1.h5 --label-bin output/finalprojectb1.pickle --plot output/finalprojectb1.png
Change the parameter --dataset --model --label and --plot with your parameter
--- run the program
python classify.py --image images/alter.jpg --model output/finalprojectb1.model --label-bin output/finalprojectb1.pickle --width 64 --height 64
Change the parameter --image --model and --label with your parameter
Reference :