ArticlePlant methods2025
Apnet: Lightweight network for apricot tree disease and pest detection in real-world complex backgrounds.
Article in Plant methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Robust rice-crab detection via receptive-field attention and feature reassembly in unstructured agricultural scenes.Scientific reports · 2026Article
- Explainable VGG16 transfer learning with SHAP and grad-CAM for wax moth pest and infestation detection in honeybee apiaries using imaging data.Scientific reports · 2026Article
- YOLO-Shrimp: A Lightweight Detection Model for Shrimp Feed Residues Fusing Multi-Attention Features.Sensors (Basel, Switzerland) · 2026Article
- LSL-YOLO11n: a YOLO11n-based model for maize leaf disease detection in complex field environments.Frontiers in plant science · 2026Article
- PFLO: a high-throughput pose estimation model for field maize based on YOLO architecture.Plant methods · 2025Article
- PMJDM: a multi-task joint detection model for plant disease identification.Frontiers in plant science · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.