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Appearance quality identification and environmental factors tracing of Lyophyllum decastes for precise environment control using knowledge graph

文献类型： 外文期刊

作者： Zhou, Kai 1 ; Yu, Junyuan 1 ; Shi, Haotong 1 ; Hou, Rui 2 ; Wu, Huarui 3 ; Hou, Jialin 1 ;

作者机构： 1.Shandong Agr Univ, Coll Mech & Elect Engn, Tai An 271018, Peoples R China

2.Beijing Univ Posts & Telecommun, Dept Artificial Intelligence, Beijing 100876, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Lyophyllum decastes; Appearance identification; Residual neural network; Knowledge graph; Graph attention network

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 235 卷

页码：

收录情况： SCI

摘要： In the factory production of Lyophyllum decastes, inappropriate cultivation environments can lead to appearance quality issues, which in turn affect both yield and quality. However, the appearance characteristics of Lyophyllum decastes influenced by environmental factors share similarities, and the environmental factors that cause appearance quality problems exhibit coupling and complexity. Therefore, the identification of appearance characteristics and tracing of environmental factors present significant challenges. To address this issue, this paper proposes a multimodal learning network, DCRes-GAT, which integrates an improved Residual Neural Network (DCResNet) and a Graph Attention Network (GAT) to accurately identify the features of Lyophyllum decastes, while simultaneously tracing environmental factors and providing control recommendations. First, a knowledge graph based on the prior knowledge of quality and environmental factors is constructed, mapping this information to a point space and extracting key features. Next, DCResNet is employed to extract optical features from Lyophyllum decastes images. In addition, the receptive field is expanded through dilated convolutions, while pixel-level details are preserved, and a Convolutional Block Attention Module (CBAM) is incorporated to identify subtle visual differences. Finally, a dot product operation fuses point-space features with visual features, achieving accurate identification of characteristics and providing suggestions. Experimental results demonstrate that the DCRes-GAT model performs excellently, with a feature identification accuracy of 99.45%, and can precisely diagnose key environmental factors that cause appearance quality problems, achieving a diagnostic accuracy of 99.84%. This provides a basis for the precise control of the cultivation environment of Lyophyllum decastes.

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