Cognitive framework and learning paradigms of plant leaf classification using artificial neural network and support vector machine

ABSTRACT

In this COVID-19 pandemic era, where people are losing their lives, there are several species and plants available on our Mother Earth that are beneficial to boosting the human immune system and sustaining their life. These plant leaves and trunks can also be used to effectively treat a variety of diseases in humans. The images of plants, as well as the identification of the leaf through artificial intelligence, are critical for obtaining such benefits. The proposed system automatically grades the various species and classify images of plant leaves into different families. The leaf image parameter is used to extract these properties such as colour, texture, shape, and so on. The proposed system makes use of colour and texture to form characteristics. The colour pattern for the texture uses GLCM and Shape extraction forms to extract colour information such as HSV (hue, saturation, value). The ANN (artificial neural network) algorithm is used to classify leaf images. Colour extraction, texture, and shape features, both alone and in combination, are used for classification. Using combined features yields better results than using single features. In the proposed system data set, 285 images are captured, 210 images with ANN are trained, and 75 images are used for the test set. With ANN, the system achieves an approximate 93.33% accuracy. The results were also validated with SVM (support vector machine), which provides an approximate 48% accuracy, indicating that ANN outperforms SVM.

KEYWORDS:

• Image processing
• leaf classification
• texture feature
• artificial neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).