Object detection and tracking in Precision Farming: a systematic review

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 219, P. 108757 - 108757

Published: Feb. 23, 2024

Object Detection and Tracking have gained importance in recent years because of the great advances image video analysis techniques accurate results these technologies are producing. Moreover, they successfully been applied to multiple fields, including agricultural domain since offer real-time monitoring status crops animals while counting how many present within a field/barn. This review aims current literature on field Precision Farming. For that, over 300 research articles were explored, from which 150 last five systematically reviewed analysed regarding algorithms implemented, belong to, difficulties faced, limitations should be tackled future. Lastly, it examines potential issues that this approach might have, for instance, lack open-source datasets with labelled data. The findings study indicate critical enhance Farming pave way robotization sector provide insights crop animal management, optimize resource allocation. Future work focus optimal acquisition prior Tracking, along consideration biophysical environment farming scenarios.

Language: Английский

---

Exploring the potential of visual tracking and counting for trees infected with pine wilt disease based on improved YOLOv5 and StrongSORT algorithm

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 218, P. 108671 - 108671

Published: Feb. 7, 2024

Language: Английский

---

Performance evaluation of newly released cameras for fruit detection and localization in complex kiwifruit orchard environments

Journal of Field Robotics, Journal Year: 2024, Volume and Issue: 41(4), P. 881 - 894

Published: Jan. 30, 2024

Abstract Consumer RGB‐D and binocular stereo cameras were applied to fruit detection localization. However, few studies are documented on performance comparison of newly released under same scene in complex orchard. This study evaluates consumer based YOLOv5x for kiwifruit localization selection optimal one with better application orchard environment. Firstly, Azure Kinect, RealSense D435, ZED 2i employed capture images canopies. Subsequently, was train detect kiwifruits calyxes the images. Meanwhile, an overlap‐partitioning strategy calyx detection. Additionally, spatial coordinate transformation performed by integrating camera's extrinsic parameters depth map generated each camera. Finally, three‐dimensional coordinates calculated compared ground truth, followed accuracy analyzed. Results show that obtained mean average precision 93.2%, 91.3%, 95.8% three detection, respectively. Overlap‐partitioning improved significantly increased 13.00%, 16.30%, 7.70%, The absolute deviation Y‐axis is relatively high at 8.44 mm 6.67 while D435 achieved minimum 10.42 X‐axis 18.33 Z‐axis. Average speed image 0.164 s, 0.037 0.062 s 2i, These results indicate excellent than Kinect orchard, which could be a valuable reference other orchards select camera capacity.

Language: Английский

---

AppleYOLO: Apple yield estimation method using improved YOLOv8 based on Deep OC-SORT

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126764 - 126764

Published: Feb. 1, 2025

Language: Английский

---

Rotating Target Detection Method of Concrete Bridge Crack Based on YOLO v5

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(20), P. 11118 - 11118

Published: Oct. 10, 2023

Crack detection is a critical and essential aspect of concrete bridge maintenance management. Manual inspection often falls short in meeting the demands large-scale crack terms cost, efficiency, accuracy, data To address challenges faced by existing generic object algorithms achieving high accuracy or efficiency when detecting cracks with large ratios, overlapping structures, clear directional characteristics, this paper presents improvements to YOLO v5 model. These enhancements include introduction angle regression variables, definition new loss function, integration PSA-Neck ECA-Layer attention mechanism modules into network architecture, consideration contribution each node’s features network, addition skip connections within same feature scale. This results novel image rotation algorithm named “R-YOLO v5”. After training R-YOLO model for 300 iterations on dataset comprising 1628 images, achieved an [email protected] 94.03% test set, which significantly higher than other such as SASM, S2A Net, Re Det, well horizontal-box Furthermore, demonstrates advantages size (4.17 MB) speed (0.01 s per image). demonstrate that designed effectively detects bridges exhibits robustness, minimal memory usage, making it suitable real-time small devices like smartphones drones. Additionally, improvement strategy discussed study holds potential applicability enhancing algorithms.

Language: Английский

---

SY-Track: A tracking tool for measuring chicken flock activity level

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 217, P. 108603 - 108603

Published: Jan. 21, 2024

Language: Английский

---

DeepSORT with siamese convolution autoencoder embedded for honey peach young fruit multiple object tracking

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 217, P. 108583 - 108583

Published: Jan. 5, 2024

Language: Английский

---

Novel intelligent grazing strategy based on remote sensing, herd perception and UAVs monitoring

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 219, P. 108807 - 108807

Published: March 7, 2024

Language: Английский

---

Information-integration-based optimal coverage path planning of agricultural unmanned systems formations: From theory to practice

Journal of Industrial Information Integration, Journal Year: 2024, Volume and Issue: 40, P. 100617 - 100617

Published: April 21, 2024

Language: Английский

---

T‐YOLO: a lightweight and efficient detection model for nutrient buds in complex tea‐plantation environments

Journal of the Science of Food and Agriculture, Journal Year: 2024, Volume and Issue: 104(10), P. 5698 - 5711

Published: Feb. 19, 2024

Quick and accurate detection of nutrient buds is essential for yield prediction field management in tea plantations. However, the complexity plantation environments similarity color between older leaves make location challenging.

Language: Английский

---