ArticleSensors (Basel, Switzerland)2021
Design of Citrus Fruit Detection System Based on Mobile Platform and Edge Computer Device.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 32 citations in OpenAlex.
- A Deep Learning Model for Chili Pepper Fruit Shape Classification Using DenseNet-121 and CBAM.Plants (Basel, Switzerland) · 2026Article
- A Vision Method for Detecting Citrus Separation Lines Using Line-Structured Light.Journal of imaging · 2025Article
- ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images.Sensors (Basel, Switzerland) · 2025Article
- An improved deep learning network for image detection and its application in Dendrobii caulis decoction piece.Scientific reports · 2024Article
- Real-time and lightweight detection of grape diseases based on Fusion Transformer YOLO.Frontiers in plant science · 2024Article
- Fruits hidden by green: an improved YOLOV8n for detection of young citrus in lush citrus trees.Frontiers in plant science · 2024Article
- Fast Recognition and Counting Method of Dragon Fruit Flowers and Fruits Based on Video Stream.Sensors (Basel, Switzerland) · 2023Article
- Automatic Meter Reading from UAV Inspection Photos in the Substation by Combining YOLOv5s and DeeplabV3.Sensors (Basel, Switzerland) · 2022Article
- Dragon fruit detection in natural orchard environment by integrating lightweight network and attention mechanism.Frontiers in plant science · 2022Article
- Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive Review.Frontiers in plant science · 2022Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Citrus fruit detection can provide technical support for fine management and yield determination of citrus orchards. Accurate detection of citrus fruits in mountain orchards is challenging because of leaf occlusion and citrus fruit mutual occlusion of different fruits. This paper presents a citrus detection task that combines UAV data collection, AI embedded device, and target detection algorithm. The system used a small unmanned aerial vehicle equipped with a camera to take full-scale pictures of citrus trees; at the same time, we extended the state-of-the-art model target detection algorithm, added the attention mechanism and adaptive fusion feature method, improved the model's performance; to facilitate the deployment of the model, we used the pruning method to reduce the amount of model calculation and parameters. The improved target detection algorithm is ported to the edge computing end to detect the data collected by the unmanned aerial vehicle. The experiment was performed on the self-made citrus dataset, the detection accuracy was 93.32%, and the processing speed at the edge computing device was 180 ms/frame. This method is suitable for citrus detection tasks in the mountainous orchard environment, and it can help fruit growers to estimate their yield.
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