Image Segmentation of Plant Leaves in Natural Environments Based on LinkNet

Authors

  • Lili Zhang
  • Xiyin Liang

DOI:

https://doi.org/10.54097/jceim.v11i3.15

Keywords:

Deep learning, Leaf segmentation, LinkNet, Sub-pixel convolution

Abstract

The conventional approach to plant leaf image segmentation for subsequent measurement of leaf geometric parameters, while reasonably accurate, exhibits lower efficiency. To address this challenge, a plant leaf image segmentation algorithm based on deep learning semantic segmentation models and transfer learning is proposed to achieve rapid and precise leaf segmentation. The presented method adopts LinkNet as its foundational network structure and introduces four key enhancements: leveraging ResNet18 as the backbone encoder network to expedite model fitting through transfer learning; reducing the number of encoding and decoding blocks to decrease network complexity; refining channel reduction strategies to minimize parameters in upsampling; and implementing subpixel convolution for upsampling to lower computational load. Integrated with the focal loss function, this improved LinkNet network is applied to a standard leaf dataset. Experimental outcomes indicate that the proposed algorithm achieves a segmentation accuracy of 97.27%, comparable to the original LinkNet; inference time is merely 7.82 ms, a 39.89% reduction compared to the original network; a substantial decrease in model parameters and floating-point operations; and a significantly faster inference speed of the improved network compared to FCN, U-Net, DeepLabV3+, and similar networks. This algorithm not only swiftly segments the main body of leaves but also effectively preserves details such as leaf edge serrations. It truly enables efficient and precise segmentation of plant leaves, offering a novel approach for rapidly measuring leaf area and other geometric parameters.

References

YU D Y, FENG T X, LI Y X, et al. Research and implementation of living leaf area measurement based on plant image[J]. Intelligent Computer and Applications, 2019,9(4) : 173 - 176.

LIN K, CHEN J, SI H, et al. Measurement of leaf area index using reference object and computer vision[J]. Oxidation Communications, 2016, 39(2A) : 1832 - 1844.

SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4) : 640 - 651.

GAO M, VAN DER HEIJDEN G W A M, VOS J, et al. Estimation of leaf area for large scale phenotyping and modeling of rose genotypes[J]. Scientia Horticulturae, 2012, 138: 227 - 234.

PAN S J, YANG Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22 (10) : 1345 - 1359.

YU C Q, WANG J B, PENG C, et al. BiSeNet: bilateral segmentation network for real-time semantic segmentation[C]//Computer Vision -ECCV 2018, 2018.

CHAURASIA A, CULURCIELLO E. LinkNet: exploiting encoder representations for efficient semantic segmentation[C]//2017 IEEE Visual Communications and Image. Processing. St. Petersburg, FL, USA. IEEE, 2017.

HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA. IEEE, 2016 : 770 - 778.

EASLON H M, BLOOM A J. Easy Leaf Area: automated digital image analysis for rapid and accurate measurement of leaf area[J]. Applications in Plant Sciences, 2014, 2 (7) : 1400033.

Zhang S, Zhang C. Modified U-Net for plant diseased leaf image segmentation[J]. Computers and Electronics in Agriculture, 2023.

A A T, A B B, B J G, et al. Deep Leaf: Mask R-CNN based leaf Detection and Segmentation from digitized herbarium specimen images[J]. Pattern Recognition Letters, 2021.

Downloads

Published

20-12-2023

Issue

Section

Articles

How to Cite

Zhang, L., & Liang, X. (2023). Image Segmentation of Plant Leaves in Natural Environments Based on LinkNet. Journal of Computing and Electronic Information Management, 11(3), 67-72. https://doi.org/10.54097/jceim.v11i3.15

Similar Articles

1-10 of 53

You may also start an advanced similarity search for this article.