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Apnet: Lightweight network for apricot tree disease and pest detection in real-world complex backgrounds

文献类型： 外文期刊

作者： Li, Minglang 1 ; Tao, Zhiyong 1 ; Yan, Wentao 2 ; Lin, Sen 3 ; Feng, Kaihao 1 ; Zhang, Zeyi 1 ; Jing, Yurong 1 ;

作者机构： 1.Liaoning Tech Univ, Sch Elect & Informat Engn, Huludao 125105, Peoples R China

2.Chinese Acad Agr Sci, Res Inst Pomol, Xingcheng 125100, Peoples R China

3.Shenyang Ligong Univ, Sch Automat & Elect Engn, Shenyang 110159, Peoples R China

关键词： Disease detection; Pest detection; Forest ecosystems; Deep learning; Agriculture image analysis

期刊名称：PLANT METHODS （ 影响因子：4.4； 五年影响因子：5.7 ）

ISSN：

年卷期： 2025 年 21 卷 1 期

页码：

收录情况： SCI

摘要： Apricot trees, serving as critical agricultural resources, hold a significant role within the agricultural domain. Conventional methods for detecting pests and diseases in these trees are notably labor-intensive. Many conditions affecting apricot trees manifest distinct visual symptoms that are ideally suited for precise identification and classification via deep learning techniques. Despite this, the academic realm currently lacks extensive, realistic datasets and deep learning strategies specifically crafted for apricot trees. This study introduces ATZD01, a publicly accessible dataset encompassing 11 categories of apricot tree pests and diseases, meticulously compiled under genuine field conditions. Furthermore, we introduce an innovative detection algorithm founded on convolutional neural networks, specifically devised for the management of apricot tree pests and diseases. To enhance the accuracy of detection, we have developed a novel object detection framework, APNet, alongside a dedicated module, the Adaptive Thresholding Algorithm (ATA), tailored for the detection of apricot tree afflictions. Experimental evaluations reveal that our proposed algorithm attains an accuracy rate of 87.1% on ATZD01, surpassing the performance of all other leading algorithms tested, thereby affirming the effectiveness of our dataset and model. The code and dataset will be made available at https://github.com/meanlang/ATZD01.

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