Article: Jasminum Grandiflorum flower images classification: deep learning and transfer learning models with the influence of preprocessing via contours and convex hull in Agritech 4.0 Journal: International Journal of Data Analysis Techniques and Strategies (IJDATS) 2025 Vol.17 No.2 pp.160 - 175 Abstract: This study specifically centres on classifying Jasminum Grandiflorum flowers through the utilisation of deep learning and transfer learning techniques. To achieve this, the research leverages advanced deep learning models such as CNNs, along with transfer learning using pre-trained architectures like VGG16, VGG19, ResNet18, and Vision Transformer. CNN stood out, excelling after extensive iterations. VGG 16 and 19 showed solid performance with fewer iterations, indicating competence in shorter training times. ResNet18 achieved the highest accuracy with fewer iterations but took longer (about 8 minutes per epoch), balancing efficiency and accuracy. ViT impressed with high accuracy despite needing more iterations, showcasing prowess in intricate learning and pattern recognition in the Jasminum Grandiflorum flower image dataset. The intended outcome of this research is to contribute significantly to the advancement of Agritech 4.0 by establishing a robust methodology for accurate Jasminum Grandiflorum flower classification without human participation. Inderscience Publishers - linking academia, business and industry through research

Title: Jasminum Grandiflorum flower images classification: deep learning and transfer learning models with the influence of preprocessing via contours and convex hull in Agritech 4.0

Authors: A. Anushya; Savita Shiwani; Ayush Shrivastava

Addresses: College of Computer Science and Engineering, University of Hail, Hail, 55471, Kingdom of Saudi Arabia ' Faculty of Computer Science and Engineering, Poornima University, Jaipur, 303905, India ' Faculty of Computer Science and Engineering, Poornima University, Jaipur, 303905, India

Abstract: This study specifically centres on classifying Jasminum Grandiflorum flowers through the utilisation of deep learning and transfer learning techniques. To achieve this, the research leverages advanced deep learning models such as CNNs, along with transfer learning using pre-trained architectures like VGG16, VGG19, ResNet18, and Vision Transformer. CNN stood out, excelling after extensive iterations. VGG 16 and 19 showed solid performance with fewer iterations, indicating competence in shorter training times. ResNet18 achieved the highest accuracy with fewer iterations but took longer (about 8 minutes per epoch), balancing efficiency and accuracy. ViT impressed with high accuracy despite needing more iterations, showcasing prowess in intricate learning and pattern recognition in the Jasminum Grandiflorum flower image dataset. The intended outcome of this research is to contribute significantly to the advancement of Agritech 4.0 by establishing a robust methodology for accurate Jasminum Grandiflorum flower classification without human participation.

Keywords: CNN; convolutional neural network; VGG16; VGG19; ResNet18; ViT; vision transformer; Jasminum Grandiflorum; AgriTech 4.0.

DOI: 10.1504/IJDATS.2025.147516

International Journal of Data Analysis Techniques and Strategies, 2025 Vol.17 No.2, pp.160 - 175

Received: 03 Sep 2023
Accepted: 03 Feb 2024
Published online: 20 Jul 2025 *

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Title: Jasminum Grandiflorum flower images classification: deep learning and transfer learning models with the influence of preprocessing via contours and convex hull in Agritech 4.0

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