Title: Paddy variety identification from field crop images using deep learning techniques

Authors: Naveen N. Malvade; Rajesh Yakkundimath; Girish B. Saunshi; Mahantesh C. Elemmi

Addresses: Department of Information Science and Engineering, Smt. Kamala and Sri. Venkappa M. Agadi College of Engineering and Technology, Lakshmeshwar, 582116, Karnataka, India; Affiliated to: Visvesvaraya Technological University, Belagavi 590018, Karnataka, India ' Department of Computer Science and Engineering, K.L.E. Institute of Technology, Hubballi, Karnataka, 580027, India; Affiliated to: Visvesvaraya Technological University, Belagavi 590018, Karnataka, India ' Department of Computer Science and Engineering, K.L.E. Institute of Technology, Hubballi, Karnataka, 580027, India; Affiliated to: Visvesvaraya Technological University, Belagavi 590018, Karnataka, India ' Department of Computer Science and Engineering, Navkis College of Engineering, Hassan, 573217, Karnataka, India; Affiliated to: Visvesvaraya Technological University, Belagavi 590018, Karnataka, India

Abstract: On-field identification of paddy varieties provides actionable information to farmers and policymakers in many aspects of crop handling and management practices. In this paper, three transfer learning pre-trained models namely ResNet-50, EfficientNet-B7, and CapsNet are presented to effectively classify the field crop images of 15 different paddy varieties captured during the booting plant growth stage. The experiments using the CapsNet model with an image dataset comprising 60,000 labelled images show the significant performance with the testing accuracy of 92.96%, and validation accuracy of 95%. The ResNet-50 and EfficientNet-B7 models have yielded the average validation accuracies of 85% and 90%, respectively. The CapsNet model has achieved both higher accuracy and better computational efficiency over the considered deep learning classification models on the held out paddy field crop image dataset.

Keywords: paddy variety identification; field crop image classification; deep convolutional neural networks; DCNN; transfer learning; CapsNet; ResNet-50; EfficientNet-B7.

DOI: 10.1504/IJCVR.2023.131986

International Journal of Computational Vision and Robotics, 2023 Vol.13 No.4, pp.405 - 419

Received: 22 Apr 2021
Accepted: 19 Mar 2022
Published online: 06 Jul 2023 *

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