70+ Image Classification Datasets from different Industry domains — Part 2
By: Abhishek Annamraju
Tags: Industry, Computer Vision, Dataset, Image Classification, Deep Learning

Agriculture Related Dataset

Demo

* Goal — To detect different rice plant diseases.

* Application — Early and accurate detection is essential to take necessary measures in saving the rest of the crop

* Details — 2K+ images spread over 3 types of diseases — Brown spots, Hispa, and Leaf Blast

* How to utilize the dataset and create a classifier using Pytorch’s VGG-Net Pipeline

Demo

* Goal — To detect presence of broad leaved docks in images.

* Application — Automated detection helps weed sprayer target right locations

* Details — 2K+ images with 2 different classes

* How to utilize the dataset and create a classifier using Pytorch’s Densenet Pipeline

Demo

* Goal — To recognize different weed species.

* Application — Automated detection helps weed sprayer target right locations

* Details — 17K+ images with 8 different classes of weed.

* How to utilize the dataset and create a classifier using Mxnet’s Resnet Pipeline

Demo

* Goal — To recognize different plant species.

* Application — Automated visual recognition helps with monitoring different species as per the need

* Details — 500+ images with 10+ different classes of plant species.

* How to utilize the dataset and create a classifier using Pytorch’s Resnet Pipeline

Demo

* Goal — To recognize plant diseases.

* Application — Early and accurate detection is essential to take necessary measures in saving the rest of the crop

* Details —3.5K+ images with 5+ different classes of plant diseases.

* How to utilize the dataset and create a classifier using Keras’ Resnet Pipeline

Reference Image. Credits

* Goal — To detect presence of cactus spread in satellite images.

* Application — Helps with understanding spread of cacti over arid and desert regions

* Details — 17K+ 32x32 patch images with 2 different classes.

* How to utilize the dataset and create a classifier using Pytorch’s Densenet Pipeline

Demo

* Goal — To detect presence of invasive weed species Hydrangea in forests.

* Application — Early detection helps with taking proper measure to keep the forest ecosystem in balance

* Details —2.5K+ images with 2 different classes.

* How to utilize the dataset and create a classifier using Keras’ Resnet Pipeline

Demo

* Goal — To classify different plant species with images of seedlings and find presence of weed.

* Application — Early detection helps with taking proper measures to remove the unwanted plants

* Details — 2.5K+ images with 12 different classes of plants.

* How to utilize the dataset and create a classifier using Keras’ Mobilenet V2 Pipeline

Demo

* Goal — To detect different leaf and stem diseases.

* Application — Early detection helps with taking proper measures to save rest of the vegetation

* Details — 500+images with 4 different classes of diseases.

* How to utilize the dataset and create a classifier using Mxnet’s Densenet Pipeline

Demo

* Goal — To detect different leaf and stem diseases.

* Application — Early detection helps with taking proper measures to save rest of the vegetation

* Details — 1K+images with 10 different classes of diseases.

* How to utilize the dataset and create a classifier using Pytorch’s Resnet Pipeline

Demo

* Goal — To monitor different stages of wheat growth.

* Application — Helps to keep track of yield

* Details — 15K+images with weekly labelled stages of crop growth.

* How to utilize the dataset and create a classifier using Pytorch’s Resnet Pipeline

Demo

* Goal — To classify different leaf species.

* Application — Monitor different species and take actions for their growth

* Details — 1K+images with 10+ leaf types.

* How to utilize the dataset and create a classifier using Pytorch’s Densenet Pipeline

Demo

* Goal — To classify different plant diseases.

* Application — Early Detection helps with taking proper measures to save the crop.

* Details — 2.5K+images spread over 17 disease types.

* How to utilize the dataset and create a classifier using Mxnet’s Densenet Pipeline