Maturity detection of Hemerocallis citrina Baroni based on LTCB YOLO and lightweight and efficient layer aggregation network
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| Udgivet i: | International Journal of Agricultural and Biological Engineering vol. 18, no. 2 (Apr 2025), p. 278-288 |
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International Journal of Agricultural and Biological Engineering (IJABE)
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| 001 | 3230952054 | ||
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| 022 | |a 1934-6344 | ||
| 022 | |a 1934-6352 | ||
| 024 | 7 | |a 10.25165/j.ijabe.20251802.9238 |2 doi | |
| 035 | |a 3230952054 | ||
| 045 | 2 | |b d20250401 |b d20250430 | |
| 084 | |a 204230 |2 nlm | ||
| 100 | 1 | |a Chen, Le |u College of Coal Engineering, Shanxi Datong University, Datong 037003, Shanxi, China | |
| 245 | 1 | |a Maturity detection of Hemerocallis citrina Baroni based on LTCB YOLO and lightweight and efficient layer aggregation network | |
| 260 | |b International Journal of Agricultural and Biological Engineering (IJABE) |c Apr 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Hemerocallis citrina Baroni is rich in nutritional value, with a clear trend of increasing market demand, and it is a pillar industry for rural economic development. Hemerocallis citrina Baroni exhibits rapid growth, a shortened harvest cycle, lacks a consistent maturity identification standard, and relies heavily on manual labor. To address these issues, a new method for detecting the maturity of Hemerocallis citrina Baroni, called LTCB YOLOv7, has been introduced. To begin with, the layer aggregation network and transition module are made more efficient through the incorporation of Ghost convolution, a lightweight technique that streamlines the model architecture. This results in a reduction of model parameters and computational workload. Second, a coordinate attention mechanism is enhanced between the feature extraction and feature fusion networks, which enhances the model precision and compensates for the performance decline caused by lightweight design. Ultimately, a bi-directional feature pyramid network with weighted connections replaces the Concatenate function in the feature fusion network. This modification enables the integration of information across different stages, resulting in a gradual improvement in the overall model performance. The experimental results show that the improved LTCB YOLOv7 algorithm for Hemerocallis citrina Baroni maturity detection reduces the number of model parameters and floating point operations by about 1.7 million and 7.3G, respectively, and the model volume is compressed by about 3.5M. This refinement leads to enhancements in precision and recall by approximately 0.58% and 0.18% respectively, while the average precision metrics mAP@0.5 and mAP@0.5:0.95 show improvements of about 1.61% and 0.82% respectively. Furthermore, the algorithm achieves a real-time detection performance of 96.15 FPS. The proposed LTCB YOLOv7 algorithm exhibits strong performance in detecting maturity in Hemerocallis citrina Baroni, effectively addressing the challenge of balancing model complexity and performance. It also establishes a standardized approach for maturity detection in Hemerocallis citrina Baroni for identification and harvesting purposes. | |
| 653 | |a Maturity | ||
| 653 | |a Feature extraction | ||
| 653 | |a Deep learning | ||
| 653 | |a Physical work | ||
| 653 | |a Nutritive value | ||
| 653 | |a Computer peripherals | ||
| 653 | |a Algorithms | ||
| 653 | |a Computer vision | ||
| 653 | |a Economic development | ||
| 653 | |a Neural networks | ||
| 653 | |a Rural areas | ||
| 653 | |a Real time | ||
| 653 | |a Parameters | ||
| 653 | |a Energy consumption | ||
| 653 | |a Harvest | ||
| 653 | |a Floating point arithmetic | ||
| 653 | |a Efficiency | ||
| 653 | |a Hemerocallis citrina | ||
| 653 | |a Economic | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Wu, Ligang |u College of Mechanical and Electrical Engineering, Shanxi Datong University, Datong 037003, Shanxi, China | |
| 700 | 1 | |a Wu, Yeqiu |u College of Architecture and Surveying Engineering, Shanxi Datong University, Datong 037003, Shanxi, China | |
| 773 | 0 | |t International Journal of Agricultural and Biological Engineering |g vol. 18, no. 2 (Apr 2025), p. 278-288 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3230952054/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3230952054/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3230952054/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |