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

Published: 18-04-2019| Version 1 | DOI: 10.17632/tywbtsjrjv.1
Contributors:
ARUN PANDIAN J,
GEETHARAMANI GOPAL

Description

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

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