@misc{10481/103749,
year = {2024},
month = {12},
url = {https://hdl.handle.net/10481/103749},
abstract = {Accurate identification of plant species is essential for various applications, including ecological
studies, agriculture, and conservation efforts. Statistics indicate that misidentification can lead to
significant issues in biodiversity management and agricultural productivity. Traditional identification
methods rely heavily on expert knowledge and manual comparison, which can be time-consuming
and prone to inaccuracies. Manual identification of plant species often requires extensive botanical
knowledge and experience. This process can be slow and subject to human error, leading to
misclassification and inconsistent results. The manual approach is not scalable, especially when
dealing with large datasets or conducting widespread biodiversity assessments. Additionally, the
reliance on visual inspection and comparison limits the ability to process and classify large volumes
of data efficiently. Our proposed solution utilizes machine learning algorithms to classify plant species
based on leaf images. By training Machine Learning (ML) models on a dataset of leaf images from
four plant species (Arjuna, Guvva, Chinar, Jatropha), we aim to develop a robust classification
system. The ML approach involves feature extraction, enabling accurate and automated species
identification. This method promises to enhance the efficiency and reliability of plant species
classification, supporting various applications in botany, agriculture, and environmental management.},
publisher = {Universidad de Granada},
keywords = {Agriculture},
keywords = {Biodiversity},
keywords = {Classification},
keywords = {Machine learning},
keywords = {Plant species},
title = {Supervised Learning Based Plant Species Classification for Precise E- Agriculture},
doi = {1989-9572},
author = {Rani, A.Radha and Nikhila, M. and Bhargavi, M.Manju and Sushma, P.},
}