A Review Analysis Techniques of Flower Classification Based on Machine Learning Algorithms

• Published in: ECS transactions 2022-04, Vol.107 (1), p.9609-9614
• Main Authors: Kaur, Rupinder, Jain, Dr. Anubha, Saini, Pushpanjali, Kumar, Sarvesh
• Format: Article
• Language: English
• ISSN: 1938-5862; 1938-6737
• DOI: 10.1149/10701.9609ecst

A system design for the recognition of flower species will be very beneficial to only in agriculture but also in industries of pharmaceutical science, study, and practice botany, trade, and farming. For the flower classification system, it will require extra species testing as there are varieties of species in flowers and thus it become exceptionally difficult to characterize them when it comes for the basic identification of flower among same species. Therefore, this subject has already become crucial for research purposes. So, for better detection, various techniques have been implemented through machine learning, which the latest trends become for such problems solving. Machine learning is well known for the strongest for its large part of classification and recognition performance in the computer. Classification is the most vital approaches of AI. Major brief of machine learning is information evaluation. Numerous algorithms present for system classification like decision trees (DT), Neural Network, Navie Bayes, SVM, Back Propagation, multi-class classification, Artificial Neural, K-nearest neighbor, multi-layer perception, etc. The paper summarizes the foremost aspects of machine learning and its drawbacks.