数字农科院2.0

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;Tao, Zhiyong;Yan, Wentao;Lin, Sen;Feng, Kaihao;Zhang, Zeyi;Jing, Yurong

作者机构：

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

期刊名称： PLANT METHODS

ISSN：

年卷期： 2025 年 21 卷 1 期

页码：

收录情况： SCIE（2025版）

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.

分类号：

  • 相关文献

[1]Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles. Pan Pan,Wenlong Guo,Xiaoming Zheng,Lin Hu,Guomin Zhou,Jianhua Zhang. 2023

[2]Positive Microbial-Enzyme Feedbacks on Soil Organic Carbon Enhance Understory Cultivation of Paris polyphylla Across Forest Types. Zhang, Hongbin,Liu, Shouzan,Zhang, Shaobo,Bai, Yan. 2025

[3]ASFL-YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family. Xu, Lijia,Shi, Xiaoshi,Tang, Zuoliang,He, Yong,Yang, Ning,Ma, Wei,Zheng, Chengyu,Chen, Huabao,Zhou, Taigang,Huang, Peng,Wu, Zhijun,Wang, Yuchao,Zou, Zhiyong,Kang, Zhiliang,Dai, Jianwu,Zhao, Yongpeng. 2023

[4]YOLO-YSTs: An Improved YOLOv10n-Based Method for Real-Time Field Pest Detection. Yiqi Huang,Zhenhao Liu,Hehua Zhao,Chao Tang,Bo Liu,Zaiyuan Li,Fanghao Wan,Wanqiang Qian,Xi Qiao. 2025

[5]An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease. Jiangtao Qi,Xiangnan Liu,Kai Liu,Farong Xu,Hui Guo,Xinliang Tian,Mao Li,Zhiyuan Bao,Yang Li. 2022

[6]Pseudo high-frequency boosts the generalization of a convolutional neural network for cassava disease detection. Jiayu Zhang,Chao Qi,Peter Mecha,Yi Zuo,Zongyou Ben,Haolu Liu,Kunjie Chen. 2022

[7]Rectifying the extremely weakened signals for cassava leaf disease detection. Jiayu Zhang,Baohua Zhang,Innocent Nyalala,Peter Mecha,Junlong Chen,Kunjie Chen,Junfeng Gao. 2025

[8]Spectroscopic detection of cotton Verticillium wilt by spectral feature selection and machine learning methods. Li, Weinan,Liu, Lisen,Li, Jianing,Yang, Weiguang,Guo, Yang,Huang, Longyu,Yang, Zhaoen,Peng, Jun,Jin, Xiuliang,Lan, Yubin. 2025

[9]Deep learning based weed detection and target spraying robot system at seedling stage of cotton field. Xiangpeng Fan,Xiujuan Chai,Jianping Zhou,Tan Sun. 2023

[10]Inter-Continental Transfer of Pre-Trained Deep Learning Rice Mapping Model and Its Generalization Ability. Lingbo Yang,Ran Huang,Jingcheng Zhang,Jingfeng Huang,Limin Wang,Jiancong Dong,Jie Shao. 2023

[11]Semantic Segmentation Based on Temporal Features: Learning of Temporal-Spatial Information From Time-Series SAR Images for Paddy Rice Mapping. Yang, Lingbo,Huang, Ran,Huang, Jingfeng,Lin, Tao,Wang, Limin,Mijiti, Ruzemaimaiti,Wei, Pengliang,Tang, Chao,Shao, Jie,Li, Qiangzi,Du, Xin. 2021

[12]A Novel Physics-Statistical Coupled Paradigm for Retrieving Integrated Water Vapor Content Based on Artificial Intelligence. Ruyu Mei,Kebiao Mao,Jiancheng Shi,Jeffrey Nielson,Sayed M. Bateni,Fei Meng,Guoming Du. 2023

[13]Machine learning for image-based multi-omics analysis of leaf veins. Yubin Zhang,Ning Zhang,Xiujuan Chai,Tan Sun. 2023

[14]Auction-based deep learning-driven smart agricultural supply chain mechanism. Yu Feng,Dong Mei,Hua Zhao. 2023

[15]Research on machine vision and deep learning based recognition of cotton seedling aphid infestation level. Xin Xu,Jing Shi,Yongqin Chen,Qiang He,Liangliang Liu,Tong Sun,Ruifeng Ding,Yanhui Lu,Chaoqun Xue,Hongbo Qiao. 2023

[16]Integrating satellite-derived climatic and vegetation indices to predict smallholder maize yield using deep learning. Liangliang Zhang,Zhao Zhang,Yuchuan Luo,Juan Cao,Ruizhi Xie,Shaokun Li. 2021

[17]A rapid, low-cost deep learning system to classify strawberry disease based on cloud service. Guo feng YANG,Yong YANG,Zi kang HE,Xin yu ZHANG,Yong HE. 2022

[18]Deep learning for change detection in remote sensing: a review. Bai, Ting,Wang, Le,Yin, Dameng,Sun, Kaimin,Chen, Yepei,Li, Wenzhuo,Li, Deren. 2022

[19]Mapping of lakes in the Qinghai-Tibet Plateau from 2016 to 2021: trend and potential regularity. Zhichong Yang,Si Bo Duan,Xiaoai Dai,Yingwei Sun,Meng Liu. 2022

[20]Bionic technology and deep learning in agricultural engineering: Current status and future prospects. Chunlei Tu,Jinxia Li,Xingsong Wang,Shen Cheng,Jie Li. 2021

作者其他论文 更多>>