IMAGE CLASSIFICATION OF AGRICULTURAL DATA USING SUPERVISED LEARNING TECHNIQUES:-A SURVEY

Nisha S Sukhwani

Abstract


Nowadays with the increasing progress in the field of machine learning and image processing, image classification plays a vital role and provides various advantages like to classify the different varieties of wheat/rice seeds or any other agricultural seeds/grains. The literature basically includes the algorithm for feature extraction and dimensionality reduction algorithms in order to reduce error. Accuracy totally depends on a number of the ratio of samples which are divided for training and testing phase. This survey includes an artificial neural network with back-propagation having multiple hidden layer and support vector machine, these supervised learning models are taken into consideration in order to classify the types, by which through the accuracy could be known. Various issues and challenges are highlighted in this survey. It is evident from the literature that back-propagation with multiple hidden layers can reduce more error in comparison to single layer hidden layer.

Keywords


image processing, segmentation, supervised learning, computer vision, classification, machine learning, neural networks.

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DOI: https://doi.org/10.26483/ijarcs.v9i2.5681

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