International Journal of Scientific Research and Engineering Development

Accurate plant species identification is a fundamental challenge in botany, agriculture, and ecological conservation, yet traditional manual approaches are time-consuming, error-prone, and require significant domain expertise. This paper presents a Next-Generation Automated System for Accurate and HighPrecision Plant Species Identification Using Leaf Images, leveraging state-of-the-art deep learning architectures — specifically Convolutional Neural Networks (CNN) combined with transfer learning techniques — to classify plant species from leaf morphology. The proposed system extracts discriminative features such as leaf shape, texture, venation patterns, color distribution, and margin characteristics to build a robust multi-class classification model. A comprehensive image preprocessing pipeline including augmentation, normalization, and background removal is employed to improve model generalization across diverse environmental conditions. Experimental results on benchmark datasets demonstrate that the system achieves a classification accuracy of 96.8%, surpassing conventional image processing and shallow machine learning approaches. The framework integrates a user-friendly web interface enabling real-time identification, making it accessible to farmers, botanists, and ecological researchers. This work contributes to the growing domain of AI-driven precision agriculture and biodiversity monitoring, offering a scalable, practical solution for automated plant identification.

J.Vijayaragavan,"Next-Generation Automated System for Accurate and High-Precision Plant Species Identification Using Leaf Images" International Journal of Scientific Research and Engineering Development, V9(3): Page(1960-1968) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.