Journal Article10.1016/J.COMPAG.2019.01.012
Apple detection during different growth stages in orchards using the improved YOLO-V3 model
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TL;DR: The test results show that the proposed YOLOV3-dense model is superior to the original YOLO-V3 model and the Faster R-CNN with VGG16 net model, which is the state-of-art fruit detection model.
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About: This article is published in Computers and Electronics in Agriculture. The article was published on 01 Feb 2019.
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Citations
A Review of Target Recognition Technology for Fruit Picking Robots: From Digital Image Processing to Deep Learning
TL;DR: In this article , the authors systematically summarize the research work on target recognition techniques for picking robots in recent years, analyze the technical characteristics of different approaches, and conclude their development history.
Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant Factory
TL;DR: In this article , an improved Small MobileNet YOLOv5 (SM-YOLO-v5) detection algorithm and model was proposed for target detection by tomato-picking robots in plant factories.
Using an improved lightweight YOLOv8 model for real-time detection of multi-stage apple fruit in complex orchard environments
Baoling Ma,Zhixin Hua,Yuchen Wen,Hongxing Deng,Yongjie Zhao,Liuru Pu,Huaibo Song +6 more
TL;DR: A lightweight YOLOv8 model, YOLOv8n-ShuffleNetv2-Ghost-SE, is proposed for real-time apple fruit detection in complex orchard environments, achieving 94.1% precision, 82.6% recall, and 91.4% mAP with a 2.6 MB model size and 39.37 fps detection speed.
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A Pineapple Target Detection Method in a Field Environment Based on Improved YOLOv7
TL;DR: In this paper , a target detection model based on the improved YOLOv7 network was proposed to achieve the accurate detection and maturity classification of pineapples in the field.
Fruit Detection in the Wild: The Impact of Varying Conditions and Cultivar
Michael Halstead,Simon Denman,Clinton Fookes,Chris McCool +3 more
- 29 Nov 2020
TL;DR: The introduction of these three novel and diverse datasets demonstrates the potential for multi-task learning to improve cross-dataset generalisability while also highlighting the importance of diverse data to adequately train and evaluate real-world systems.
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