Supervised Learning Based Plant Species Classification for Precise E- Agriculture

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.