Enhancing Security in Distributed Drone-Based Litchi Fruit Recognition and Localization Systems
Source abstract
Abstract
This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations. Our met... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (39)
Show source-provided references
1. A segmentation method based on motion from image sequence and depth10.1109/ICPR.1990.118100 ↗
2. Automatic fruit image recognition system based on shape and color features
3. Deep depth completion of a single RGB-D image
4. Guava detection and pose estimation using a low-cost RGB-D sensor in the field
5. AI-based object detection latest trends in remote sensing, multimedia and agriculture applications10.3389/fpls.2022.1041514 ↗
6. CropDeep: The crop vision dataset for deep-learning-based classification and detection in precision agriculture10.3390/s19051058 ↗
7. Fast object detection based on color histograms and local binary patterns
8. Texture representation in aam using gabor wavelet and local binary patterns
9. Benchmarking robustness of 3D object detection to common corruptions
10. A review of yolo algorithm developments10.1016/j.procs.2022.01.135 ↗
11. Apple detection during different growth stages in orchards using the improved YOLO-V3 model10.1016/j.compag.2019.01.012 ↗
12. Object detection based on YOLO network
13. Object detection using YOLO: Challenges, architectural successors, datasets and applications10.1007/s11042-022-13644-y ↗
14. Object detection in agricultural contexts: A multiple resolution benchmark and comparison to human10.1016/j.compag.2021.106404 ↗
15. Object detection for agricultural and construction environments using an ultrasonic sensor10.13031/jash.22.11260 ↗
16. Augmenting crop detection for precision agriculture with deep visual transfer learning a case study of bale detection10.3390/rs13010023 ↗
17. A smartphone-based application for scale pest detection using multiple-object detection methods
18. Object detection and terrain classification in agricultural fields using 3D lidar data
19. Selective kernel networks
20. Deep neural networks for object detection
21. Scale-transferrable object detection
22. Multi class fruit classification using efficient object detection and recognition techniques
23. T. Roddick, A. Kendall, and R. Cipolla, “Orthographic feature transform for monocular 3D object detection,” 2018, arXiv:1811.08188.
24. Automatic fruit recognition: A survey and new results using range/attenuation images10.1016/S0031-3203(98)00170-8 ↗
25. Automatic fruit recognition from natural images using color and texture features
26. Texture-based fruit detection10.1007/s11119-014-9361-x ↗
27. Fruit recognition based on convolution neural network
28. Multi-model CNN-RNN-LSTM based fruit recognition and classification
29. Fig fruit recognition method based on YOLO v4 deep learning
30. Apple stem/calyx real-time recognition using YOLO-v5 algorithm for fruit automatic loading system10.1016/j.postharvbio.2021.111808 ↗
31. YOLO-based deep learning framework for olive fruit fly detection and counting10.1109/ACCESS.2021.3088075 ↗
32. Real-time monitoring method of strawberry fruit growth state based on yolo improved model10.1109/ACCESS.2022.3220234 ↗
33. Fruit detection and recognition based on deep learning for automatic harvesting: An overview and review
34. An optimized YOLO-based object detection model for crop harvesting system10.1049/ipr2.12181 ↗
35. DSE-YOLO: Detail semantics enhancement YOLO for multi-stage strawberry detection10.1016/j.compag.2022.107057 ↗
36. An improved YOLO algorithm for detecting flowers and fruits on strawberry seedlings10.1016/j.biosystemseng.2023.11.008 ↗
37. Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments10.1016/j.compag.2020.105742 ↗
38. Fine-tuning convolutional neural networks for fine art classification10.1016/j.eswa.2018.07.026 ↗
39. Boosting broader receptive fields for salient object detection10.1109/TIP.2022.3232209 ↗