CNN-Driven Plant Species Recognition System

• Author(s): Suchetha N V ; Arjun K ; Panchami B S
• Paper ID: 1706518
• Page: 358-366
• Published Date: 18-11-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 5 November-2024

Abstract

The need for innovative solutions to monitor and sustain plant species biodiversity is rising because global biodiversity declines rapidly. The traditional methods of identifying plants are frequently time-consuming and require botanists with expertise in these areas. The objective is to create a dependable, efficient, and scalable system for recognizing plant species using machine learning technology. The intent here is to construct a user-friendly tool leveraging complex machine learning techniques such as Convolutional Neural Networks (CNN), which allow scientists and the public to identify plant species correctly. The suggested approach is based on an extensive dataset of photos that depict many plant species at various phases of growth in addition to varying environmental circumstances. This assists in classification and feature extraction by CNNs that enables the model to learn specific features from these pictures and increase its extent of generalization across various plant species. This method could significantly increase plant identification's availability, speed, and accuracy, supporting conservation efforts and monitoring the world's biodiversity.

Keywords

Classification, Convolutional Neural Networks, Deep Learning, Feature Extraction, Generalization, Machine Learning.

Citations

IRE Journals:
Suchetha N V , Arjun K , Panchami B S "CNN-Driven Plant Species Recognition System" Iconic Research And Engineering Journals Volume 8 Issue 5 2024 Page 358-366

IEEE:
Suchetha N V , Arjun K , Panchami B S "CNN-Driven Plant Species Recognition System" Iconic Research And Engineering Journals, 8(5)