DEEP LEARNING-BASED PLANT IDENTIFICATION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS

Dr. V. VAIDEHI; Dr. VIJI VINOD

doi:10.5281/zenodo.10683576

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JOURNAL ARTICLE

DEEP LEARNING-BASED PLANT IDENTIFICATION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS

Dr. V. VAIDEHI Dr. VIJI VINOD

Year: 2024 Journal: Zenodo (CERN European Organization for Nuclear Research) Publisher: European Organization for Nuclear Research

DOI: 10.5281/zenodo.10683576

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Abstract

Abstract The collection is composed of six common medicinal plants found throughout Tamil Nadu. Sophisticated and tuned CNN (Convolutional Neural Network) model is used for core classification. 70% of dataset can be used for training the model. 30% of dataset used for validation. Randomization and augmentation of dataset can be done before feeding to model. ECOC framework is used. One-versus-all coding design is used with SVM classifier. CNN models are used for feature extraction used as input for model.

Keywords:

Convolutional neural network Pattern recognition (psychology) Support vector machine Feature extraction Artificial neural network Coding (social sciences) Identification (biology)

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Topics

Smart Agriculture and AI

Life Sciences → Agricultural and Biological Sciences → Plant Science

Traditional Chinese Medicine Studies

Health Sciences → Medicine → Complementary and alternative medicine

Wood and Agarwood Research

Physical Sciences → Chemistry → Organic Chemistry

DEEP LEARNING-BASED PLANT IDENTIFICATION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS

Abstract

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