Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments
By Dihua Wu, Shuaichao Lv, Mei Jiang, Huaibo Song. Computers and Electronics in Agriculture, 2020
461 citations recorded by the source corpus.
Abstract Achieving the rapid and accurate detection of apple flowers in natural environments is essential for yield estimation and the development of an automatic flower thinner. A real-time apple flower detection method using the channel pruned YOLO v4 deep learning algorithm was proposed. First, the YOLO v4 model under the CSPDarknet53 framework was built, and then, to simplify the apple flower detection model and ensure the efficiency of the model, the channel pruning algorithm was used to prune the model. Finally, a total of 2230 manually labeled apple flower images (including three varieties of Fuji, Red Love, and Gala) were used to fine-tune the model to achieve the fast and accurate detection of apple flowers. The test results showed that the number of parameters of the apple flower detection model after pruning was reduced by 96.74%, the model size was reduced by 231.51 MB, the inference time was decreased by 39.47%, and the mAP was 97.31%, which was only 0.24% lower than the model before pruning. To verify the effectiveness of the proposed method, five different deep learning algorithms including the Faster R-CNN, Tiny-YOLO v2, YOLO v3, SSD 300 and EfficientDet-D0 were compared. The comparative results showed that the mAP of the apple flower detection using the proposed method was 97.31%; the detection speed was 72.33f/s; the model size was 12.46 MB; the mAP was 12.21%, 15.56%, 14.19%, 5.67% and 7.79% higher than the other five algorithms, respectively; and the detection speed could meet the real-time requirements. Furthermore, the detection performance of apple flowers under different species of apple trees and illumination conditions was discussed. The results indicated that the proposed method had strong robustness to the changes of fruit tree varieties and illumination directions. The results showed that it was feasible to apply the proposed method for the real-time and accurate detection of apple flowers. The research could provide technical references for orchard yield estimation and the development of apple flower thinning robots.
Subfields: Analytical Chemistry, Plant Science
View Paper: https://www.semanticscholar.org/paper/48bbec049c3eb404eff8216ea9cd929dab2a9a7c
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Citations in the corpus
- A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS (2023, methodology): “YOLO models have been used in agriculture to detect and classify crops [21, 22], pests, and diseases [23], assisting in precision agriculture techniques and automating farming processes.”
- YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO (2024, background): “Vision Transformer (ViT) was introduced in 2020 [26].”
- Object detection using YOLO: challenges, architectural successors, datasets and applications (2022, methodology): “[76] YOLO v4 Apple flower detection • YOLO v4 model with CSP Darknet53 • Pruning of model using channel pruning • mAP - 97.”
- A Comprehensive Review of YOLO: From YOLOv1 to YOLOv8 and Beyond (2023, methodology): “YOLO models have been used in agriculture to detect and classify crops [11, 12], pests, and diseases [13], assisting in precision agriculture techniques and automating farming processes.”