A Novel Multistage Approach for Medicinal Plant Classification with Deep Learning Techniques

Narayana Rao K; Srinivas Kalime; Sujatha P; Dinesh Babu Vunnava; Sushma S; Tulasi Krishna Sajja

doi:10.54392/irjmt2548

Authors

Narayana Rao K RISE Krishna Sai Prakasam Group of Institutions, Ongole, Andhra Pradesh, India. Author https://orcid.org/0000-0002-7342-6294
Srinivas Kalime Department of Computer Science and Engineering, Sumathi Reddy Institute of Technology for Women, Warangal, Telangana, India Author https://orcid.org/0000-0003-1329-8005
Sujatha P Department of Computer Science and Engineering, Miracle Educational Society Group of Institutions, Vijaynagaram, Andhra Pradesh, India Author
Dinesh Babu Vunnava Department of Computer Science and Engineering, RV Institute of Technology, Guntur, India Author https://orcid.org/0000-0001-7745-069X
Sushma S Department of Information Technology, Aditya University, Surampalem, India Author https://orcid.org/0009-0004-1776-3191
Tulasi Krishna Sajja Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, India Author

DOI:

https://doi.org/10.54392/irjmt2548

Keywords:

Intrusion Detection, Ranking Algorithms, Machine Learning, Ensemble Learning

Abstract

Accurate classification of medicinal plant images into high-level categories and specific sub-groups is essential for various applications, including agriculture, plant research, and conservation. This paper proposes a multi-stage deep learning approach to enhance the precision of medicinal plant image classification. In the first stage, known as Broad Classification, CNN and pre-trained models such as VGG16, ResNet50 and EfficientNetB0 are utilized to categorize images into high-level groups, including "Medicinal Plants," "Fruit-Related Plants," and "Flower-Related Plants." The model is fine-tuned using data augmentation techniques to ensure robust learning and generalization. In the second stage, referred to as Detailed Classification, separate models are trained for each high-level group to classify images into specific sub-groups within that category. The architecture of these models is adjusted to accommodate the unique number of classes in each sub-group. Each model undergoes training with optimized hyperparameters and is evaluated based on precision, recall, F1-score, and accuracy. The proposed multi-stage method demonstrates the ability to handle both broad and fine-grained medicinal plant classifications effectively, showcasing an improvement in classification performance over traditional single-stage models. This approach highlights the potential for deep learning to contribute to more precise and practical medicinal plant image classification solutions.

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