Ready, Steady, Go AI: A Practical Tutorial on Fundamentals of Artificial Intelligence and Its Applications in Phenomics Image Analysis
Original PlantVillage tomato leaf images were obtained from J, Arun Pandian; Gopal, Geetharamani (2019), “Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network”, Mendeley Data, V1, doi: 10.17632/tywbtsjrjv.1. We provide: 1- A randomly split version of the dataset (80% training, 10% validation, and 10% testing). 2- 1000 annotated images with bounding boxes to train YOLOv3 on cropping leaf images. 3- 150 annotated images with segmentation masks to train SegNet on segmenting leaf images. 4- Cropped version of the split dataset. 5- Segmented version of the cropped and split dataset. 6- Balanced version of the segmented, cropped, and split dataset, balanced to1500 images per class, approximately. 7- Trained YOLO, SegNet, DCGAN, RF, DCNN, and Pretrained Densenet-161 DCNN models. For details, check our paper: "Ready, Steady, Go AI: A Practical Tutorial on Fundamentals of Artificial Intelligence and Its Applications in Phenomics Image Analysis"
Steps to reproduce
Follow our interactive tutorial available on our GitHub repository at https://github.com/HarfoucheLab/Ready-Steady-Go-AI