Framework for Apple Phenotype Feature Extraction Using Instance Segmentation and Edge Attention Mechanism
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| Izdano u: | Agriculture vol. 15, no. 3 (2025), p. 305 |
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| Glavni autor: | |
| Daljnji autori: | , , , , , , |
| Izdano: |
MDPI AG
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| Teme: | |
| Online pristup: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3165753599 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2077-0472 | ||
| 024 | 7 | |a 10.3390/agriculture15030305 |2 doi | |
| 035 | |a 3165753599 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231331 |2 nlm | ||
| 100 | 1 | |a Wang, Zichong |u China Agricultural University, Beijing 100083, China | |
| 245 | 1 | |a Framework for Apple Phenotype Feature Extraction Using Instance Segmentation and Edge Attention Mechanism | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a A method for apple phenotypic feature extraction and growth anomaly identification based on deep learning and natural language processing technologies is proposed in this paper, aiming to enhance the accuracy of apple quality detection and anomaly prediction in agricultural production. This method integrates instance segmentation, edge perception mechanisms, attention mechanisms, and multimodal data fusion to accurately extract an apple’s phenotypic features, such as its shape, color, and surface condition, while identifying potential anomalies which may arise during the growth process. Specifically, the edge transformer segmentation network is employed to combine deep convolutional networks (CNNs) with the Transformer architecture, enhancing feature extraction and modeling long-range dependencies across different regions of an image. The edge perception mechanism improves segmentation accuracy by focusing on the boundary regions of the apple, particularly in the case of complex shapes or surface damage. Additionally, the natural language processing (NLP) module analyzes agricultural domain knowledge, such as planting records and meteorological data, providing insights into potential causes of growth anomalies and enabling more accurate predictions. The experimental results demonstrate that the proposed method significantly outperformed traditional models across multiple metrics. Specifically, in the apple phenotypic feature extraction task, the model achieved exceptional performance, with accuracy of 0.95, recall of 0.91, precision of 0.93, and mean intersection over union (mIoU) of 0.92. Furthermore, in the growth anomaly identification task, the model also performed excellently, with a precision of 0.93, recall of 0.90, accuracy of 0.91, and mIoU of 0.89, further validating its efficiency and robustness in handling complex growth anomaly scenarios. The method’s integration of image data with agricultural knowledge provides a comprehensive approach to both apple quality detection and growth anomaly prediction, offering reliable decision support for agricultural production. The proposed method, by integrating image data with agricultural domain knowledge, provides precise decision support for agricultural production, not only improving the efficiency and accuracy of apple quality detection but also offering reliable technical assurance for agricultural economic analysis. | |
| 653 | |a Economic analysis | ||
| 653 | |a Feature extraction | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Agricultural production | ||
| 653 | |a Phenotypes | ||
| 653 | |a Agriculture | ||
| 653 | |a Image processing | ||
| 653 | |a Data integration | ||
| 653 | |a Perception | ||
| 653 | |a Automation | ||
| 653 | |a Machine learning | ||
| 653 | |a Fruits | ||
| 653 | |a Localization | ||
| 653 | |a Genotype & phenotype | ||
| 653 | |a Dimensional analysis | ||
| 653 | |a Entropy | ||
| 653 | |a Recall | ||
| 653 | |a Data analysis | ||
| 653 | |a Decision support systems | ||
| 653 | |a Apples | ||
| 653 | |a Image segmentation | ||
| 653 | |a Computer vision | ||
| 653 | |a Predictions | ||
| 653 | |a Quality management | ||
| 653 | |a Neural networks | ||
| 653 | |a Classification | ||
| 653 | |a Instance segmentation | ||
| 653 | |a Methods | ||
| 653 | |a Natural language processing | ||
| 653 | |a Image quality | ||
| 653 | |a Anomalies | ||
| 653 | |a Meteorological data | ||
| 653 | |a Semantics | ||
| 653 | |a Economic | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Cui, Weiyuan |u China Agricultural University, Beijing 100083, China; National School of Development, Peking University, Beijing 100871, China | |
| 700 | 1 | |a Huang, Chenjia |u China Agricultural University, Beijing 100083, China; Faculty of Humanities, China University of Political Science and Law, Beijing 102249, China | |
| 700 | 1 | |a Zhou, Yuhao |u China Agricultural University, Beijing 100083, China | |
| 700 | 1 | |a Zhao, Zihan |u China Agricultural University, Beijing 100083, China; National School of Development, Peking University, Beijing 100871, China | |
| 700 | 1 | |a Yue, Yuchen |u China Agricultural University, Beijing 100083, China; National School of Development, Peking University, Beijing 100871, China | |
| 700 | 1 | |a Dong, Xinrui |u China Agricultural University, Beijing 100083, China; School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China | |
| 700 | 1 | |a Lv, Chunli |u China Agricultural University, Beijing 100083, China | |
| 773 | 0 | |t Agriculture |g vol. 15, no. 3 (2025), p. 305 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3165753599/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3165753599/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3165753599/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |