Framework for Apple Phenotype Feature Extraction Using Instance Segmentation and Edge Attention Mechanism

Zichong Wang, Weiyuan Cui, Chenjia Huang, Yuhao Zhou, Zihan Zhao, Yuchen Yue, Xinrui Dong and Chunli Lv ()
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Zichong Wang: China Agricultural University, Beijing 100083, China
Weiyuan Cui: China Agricultural University, Beijing 100083, China
Chenjia Huang: China Agricultural University, Beijing 100083, China
Yuhao Zhou: China Agricultural University, Beijing 100083, China
Zihan Zhao: China Agricultural University, Beijing 100083, China
Yuchen Yue: China Agricultural University, Beijing 100083, China
Xinrui Dong: China Agricultural University, Beijing 100083, China
Chunli Lv: China Agricultural University, Beijing 100083, China

Agriculture, 2025, vol. 15, issue 3, 1-23

Abstract: 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.

Keywords: apple phenotype feature extraction; growth anomaly recognition; deep learning; agricultural economic analysis (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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