Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network

In this data-set, 39 different classes of plant leaf and background images are available. The data-set containing 61,486 images. We used six different augmentation techniques for increasing the data-set size. The techniques are image flipping, Gamma correction, noise injection, PCA color augmentation, rotation, and Scaling.

The classes are,
1.Apple_scab
2.Apple_black_rot
3.Apple_cedar_apple_rust
4.Apple_healthy
5.Background_without_leaves
6.Blueberry_healthy
7.Cherry_powdery_mildew
8.Cherry_healthy
9.Corn_gray_leaf_spot
10.Corn_common_rust
11.Corn_northern_leaf_blight
12.Corn_healthy
13.Grape_black_rot
14.Grape_black_measles
15.Grape_leaf_blight
16.Grape_healthy
17.Orange_haunglongbing
18.Peach_bacterial_spot
19.Peach_healthy
20.Pepper_bacterial_spot
21.Pepper_healthy
22.Potato_early_blight
23.Potato_healthy
24.Potato_late_blight
25.Raspberry_healthy
26.Soybean_healthy
27.Squash_powdery_mildew
28.Strawberry_healthy
29.Strawberry_leaf_scorch
30.Tomato_bacterial_spot
31.Tomato_early_blight
32.Tomato_healthy
33.Tomato_late_blight
34.Tomato_leaf_mold
35.Tomato_septoria_leaf_spot
36.Tomato_spider_mites_two-spotted_spider_mite
37.Tomato_target_spot
38.Tomato_mosaic_virus
39.Tomato_yellow_leaf_curl_virus