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Article type: Research Article
Authors: Vu, Hoai Nama | Nguyen, Quang Dungb | Nguyen, Thuy Linhb | Tran-Anh, Datb; *
Affiliations: [a] Faculty of Information Technology, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam | [b] Faculty of Information Technology>, Thuyloi University, Hanoi, Vietnam
Correspondence: [*] Corresponding author. Dat Tran-Anh. E-mail: [email protected].
Abstract: In the real world, the appearance of similar rice varieties depends on various factors such as resolution, angle, lighting conditions, and perspective. Additionally, complex environmental factors and characteristics of each rice type, such as enhanced light intensity, cross-polarization, and shading, rice background color, and image similarity, play a role. This indicates that the data augmentation process may enhance the accuracy of crop identification, particularly in the context of self-supervised machine learning. The aim of this research is to develop a precise rice segmentation method based on the improved Mask R-CNN (Region-based Convolutional Neural Network) with multitask data augmentation. The Mask R-CNN model is enhanced by incorporating multitask input to improve feature extraction for rice. Experimental results demonstrate that the improved Mask R-CNN model can accurately segment various rice types under different conditions, such as different background colors and varying sizes of rice grains. The achieved precision, recall, F1 score, and segmentation mean Average Precision (mAP) are 95.5%, 96.3%, 95.9%, and 0.924, respectively. The average runtime on the test set is 0.35 seconds per image. Our method outperforms two comparative approaches, showcasing its ability to accurately segment rice in the market deployment phase with near real-time performance. This study establishes the foundation for the accurate detection of valuable agricultural products.
Keywords: Multi-augmentation, deep learning, Mask RCNN, rice recognition, fusion metric
DOI: 10.3233/JIFS-241133
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
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