Optimizing Feature Selection Using Particle Swarm Optimization and Utilizing Ventral Sides of Leaves for Plant Leaf Classification

As the digital images produce a lot of information about the pixels, there is a need to find alternative methods to reduce the image feature dataset for faster and automatic classification of plants through digital leaf images. In the present work, the leaf image texture features have been extracted through Gabor based techniques and then subjecting them to PSO-CFS based search method for identifying the best set of features from the complete feature set and then classifying them using four classification algorithms like KNN, J48, CART and RF. Another objective of this work is to utilize the two faces available on the plant leaves (Dorsal and Ventral), instead of one (i.e. Dorsal) for classification of plants on the basis of digital leafimages and to analyse the effects on classification accuracy values for dorsal and ventral sides of leaf images.