Abstract
Plants leaves recognition is an important scientific field that is concerned of recognizing leaves using image processing techniques. Several methods are presented using different algorithms to achieve the highest possible accuracy. This paper provides an analytical survey of various methods used in image processing for the recognition of plants through their leaves. These methods help in extracting useful information for botanists to utilize the medicinal properties of these leaves, or for any other agricultural and environmental purposes. We also provide insights and a complete review of different techniques used by researchers that consider different features and classifiers. These features and classifiers are studied in term of their capabilities in enhancing the accuracy ratios of the classification methods. Our analysis shows that both of the Support Victor Machines (SVM) and the Convolutional Neural Network (CNN) are positively dominant among other methods in term of accuracy.
Highlights
Pattern recognition and image processing techniques are exploited by using plant images to build plant lists for the conservation and preservation of existing classes of the plant [1]
In the recognition and classification methods, Probabilistic Neural Network (PNN) is utilized as a classifier due to many advantages, including high resistance of distortion, flexibility to modify data, and the specimen can be classified into multiple outputs
We found that Convolutional Neural Network (CNN) occupies ≈ 31% of existing classification methods, while each of PNN and Support Victor Machines (SVM) found in ≈ 16% of methods
Summary
College of Computer Sciences and Information Technology University of Anbar Anbar, Iraq. This paper provides an analytical survey of various methods used in image processing for the recognition of plants through their leaves. These methods help in extracting useful information for botanists to utilize the medicinal properties of these leaves, or for any other agricultural and environmental purposes. We provide insights and a complete review of different techniques used by researchers that consider different features and classifiers. These features and classifiers are studied in term of their capabilities in enhancing the accuracy ratios of the classification methods.
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