Multiclass Classification Method Based on Deep Learning for Leaf Identification for Farmers


Authors : Sanjay Sunkad, Srujan Rumale, Aakash Bagale, Zabiulla Shariff.

Volume/Issue : Volume 3 - 2018, Issue 5 - May

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://goo.gl/WN56Nt

Thomson Reuters ResearcherID : https://goo.gl/3bkzwv

There are estimated to be nearly half a million species of plant in the world. Classification of species has been historically problematic and often results in duplicate identifications. Plant identification based on leaf is becoming one of the most interesting and a popular trend. Each leaf carries unique information that can be used in the identification of plants. In the identification of plants based on leaf, the leaf images need to be pre-processed accordingly to extract the various critical features. As the upcoming ground-breaking performance of neural networks in the field of classification and identification of objects is being carried out where success is being achieved, the new neural network known as Convolutional Neural Network(CNN) has made the identification and classification of objects more reliable with the performance and as well as the computations. Like any other classifier, the Convolutional Neural Network (Model) is trained with images and its specific labels. Having few layered Neural Network; we will get patterns (features) in given object (image). This trained model is used to classify new input images. As supervised deep learning is used it makes it produces accurate results and makes it easier for the user to classify leaf data with much higher accuracy. CNN’s are great at images, and have promise for text, simply because they are capable of identifying patterns in huge number of homogeneous features (pixels in 2D or characters in 1D). They are designed to handle large amount of image data. Hence by using Convolutional Neural Network, the accuracy is drastically improved.

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