Requirement

We run code in Google Colab

• Python 3.10.12
• PyTorch 2.0.1
• NVIDIA Tesla T4

Dataset

Here we have use Durian Leaf Disease dataset. The dataset original consists of 420 healthy and disease leaf images divided into 4 class by disease.

Dataset can be found here

The classes uses in dataset are:

• Leaf Spot
• Algal Leaf Spot
• Leaf Blight
• No Disease

Augmentation

durian plant disease with augmentation by rotation and cutout methods in roboflow 90° Rotate: Clockwise, Counter-Clockwise, Upside Down Cutout: 7 boxes with 15% size each

Dataset Split: Train 70 %, Valid 15 %, Test 15 %

Dataset No Augmentation can be download here.

Dataset Augmentation can be download here.

Train Model

Train VGG16, EfficientNet_b2, ResNet18 Pre-trained models with IMAGENET1K_V1

75 Epoch with Data_No_Augment and 25 Epoch with Data_Augment

Output of training is given below

Epoch 73/74 train Loss: 0.0003 Acc: 1.0000 valid Loss: 0.0464 Acc: 1.0000

Epoch 74/74 train Loss: 0.0004 Acc: 1.0000 valid Loss: 0.0424 Acc: 1.0000

Training complete in 2m 43s Best val Acc: 1.000000

Evaluation

Evaluation With Accuracy, weighted F1-Score

Model	Augmentation	Accuracy	F1 Score
ResNet18	No Augmentation	94.12%	0.9399
	Augmentation	97.06%	0.9706
VGG16	No Augmentation	89.71%	0.8976
	Augmentation	94.12%	0.9421
EfficientNet B2	No Augmentation	90.20%	0.9025
	Augmentation	92.16%	0.9217