Apple detection during different growth stages in orchards using the improved YOLO-V3 model
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Summary
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.
- Type
- article
- Published
- 2019-02-01
- Cited by
- 974
- References
- 26
- OpenAlex
- https://openalex.org/W2909494862
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:68241008
Keywords
Artificial intelligence, Transformation (genetics), Feature (linguistics), Computer science, Computer vision
References
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- You Only Look Once: Unified, Real-Time Object Detection
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Detecting citrus fruits and occlusion recovery under natural illumination conditions
- Leaf classification in sunflower crops by computer vision and neural networks
- On Plant Detection of Intact Tomato Fruits Using Image Analysis and Machine Learning Methods
- Combining gray world and retinex theory for automatic white balance in digital photography
- Determination of the number of green apples in RGB images recorded in orchards
- Fruit classification by biogeography‐based optimization and feedforward neural network
- A review of key techniques of vision-based control for harvesting robot
- DeepFruits: A Fruit Detection System Using Deep Neural Networks
- Towards a Second Green Revolution
- Deep fruit detection in orchards
- YOLO9000: Better, Faster, Stronger
- Counting Apples and Oranges With Deep Learning: A Data-Driven Approach
- Weed identification based on K-means feature learning combined with convolutional neural network
- Deep Count: Fruit Counting Based on Deep Simulated Learning
- How deep learning extracts and learns leaf features for plant classification
- RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network
- Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation
Cited by
- A Robust Real-Time Detecting and Tracking Framework for Multiple Kinds of Unmarked Object
- Can We Automate Diagrammatic Reasoning?
- Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOV3-Dense
- Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN
- Cucumber Fruits Detection in Greenhouses Based on Instance Segmentation
- A Vision-Based Method Utilizing Deep Convolutional Neural Networks for Fruit Variety Classification in Uncertainty Conditions of Retail Sales
- Quality and Defect Inspection of Green Coffee Beans Using a Computer Vision System
- Salient Object Detection: Integrate Salient Features in the Deep Learning Framework
- Study on Real-time Prediction Method of Seizures based on YOLOV3 for EEG Spike Wave Detection
- Fruit Detection and Segmentation for Apple Harvesting Using Visual Sensor in Orchards
- A deep learning-based framework for an automated defect detection system for sewer pipes
- Recognition and Classification of Broiler Droppings Based on Deep Convolutional Neural Network
- Fast implementation of real-time fruit detection in apple orchards using deep learning
- Design and Analysis of Refined Inspection of Field Conditions of Oilfield Pumping Wells Based on Rotorcraft UAV Technology
- Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector
- Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety
- Improved Kiwifruit Detection Using Pre-Trained VGG16 With RGB and NIR Information Fusion
- Intelligent Detection for Tunnel Shotcrete Spray Using Deep Learning and LiDAR
- Detection of Collapsed Buildings in Post-Earthquake Remote Sensing Images Based on the Improved YOLOv3
- Tender Tea Shoots Recognition and Positioning for Picking Robot Using Improved YOLO-V3 Model
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