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.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by-nc. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 6
Claim · model-written summary
The proposed leaf segmentation algorithm achieved 97.27% accuracy, comparable to original LinkNet.
Verbatim
t0“Plant Leaves”
e0“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.”
Claim · model-written summary
The algorithm took 7.82 ms per inference, 39.89% less than original LinkNet.
Verbatim
t0“Plant Leaves”
e0“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.”
Claim · model-written summary
The algorithm substantially reduced model parameters.
Verbatim
t0“Plant Leaves”
e0“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.”
Claim · model-written summary
The algorithm substantially reduced floating-point operations.
Verbatim
t0“Plant Leaves”
e0“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.”
Claim · model-written summary
The improved network inferred significantly faster than FCN, U-Net, and DeepLabV3+.
Verbatim
t0“Plant Leaves”
e0“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.”
Claim · model-written summary
The algorithm preserved leaf edge serrations during segmentation.
Not reportedComparatorDirectionValue
Verbatim
t0“Plant Leaves”
e0“This algorithm not only swiftly segments the main body of leaves but also effectively preserves details such as leaf edge serrations.”