SVM and KNN Based CNN Architectures for Plant Classification

Automatic plant classification through plant leaf is a classical problem in Computer Vision.Plants classification is challenging due to the introduction of new species with a similar pattern and look-a-like.Many efforts are made to automate plant classification using plant leaf,plant flower,bark,or stem.After much effort,it has been proven that leaf is the most reliable source for plant classification.But it is challenging to identify a plant with the help of leaf structure because plant leaf shows similarity in morphological variations,like sizes,textures,shapes,and venation.Therefore,it is required to normalize all plant leaves into the same size to get better performance.Convolutional Neural Networks(CNN)provides a fair amount of accuracy when leaves are classified using this approach.But the performance can be improved by classifying using the traditional approach after applying CNN.In this paper,two approaches,namely CNN+Support Vector Machine(SVM)and CNN+K-Nearest Neighbors(kNN)used on 3 datasets,namely LeafSnap dataset,Flavia Dataset,and MalayaKew Dataset.The datasets are augmented to take care all the possibilities.The assessments and correlations of the predetermined feature extractor models are given.CNN+kNN managed to reach maximum accuracy of 99.5%,97.4%,and 80.04%,respectively,in the three datasets.

Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm

Since the beginning of time,humans have relied on plants for food,energy,and medicine.Plants are recognized by leaf,flower,or fruit and linked to their suitable cluster.Classification methods are used to extract and select traits that are helpful in identifying a plant.In plant leaf image categorization,each plant is assigned a label according to its classification.The purpose of classifying plant leaf images is to enable farmers to recognize plants,leading to the management of plants in several aspects.This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes.This modified algorithm works on different sets of plant leaves.The proposed algorithm examines several benchmark functions with adequate performance.On ten plant leaf images,this classification method was validated.The proposed model calculates precision,recall,F-measurement,and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms.Based on experimental data,it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%.