A detection algorithm for cherry fruits based on the improved YOLO-v4 model

"Digital" agriculture is rapidly affecting the value of agricultural output. Robotic picking of the ripe agricultural product enables accurate and rapid picking, making agricultural harvesting intelligent. How to increase product output has also become a challenge for digital agriculture. During the cherry growth process, realizing the rapid and accurate detection of cherry fruits is the key to the development of cherry fruits in digital agriculture. Due to the inaccurate detection of cherry fruits, environmental problems such as shading have become the biggest challenge for cherry fruit detection. This paper proposes an improved YOLO-V4 deep learning algorithm to detect cherry fruits. This model is suitable for cherry fruits with a small volume. It is proposed to increase the network based on the YOLO-V4 backbone network CSPDarknet53 network, combined with DenseNet The density between layers, the a priori box in the YOLO-V4 model, is changed to a circular marker box that fits the shape of the cherry fruit. Based on the improved YOLO-V4 model, the feature extraction is enhanced, the network structure is deepened, and the detection speed is improved. To verify the effectiveness of this method, different deep learning algorithms of YOLO-V3, YOLO-V3-dense and YOLO-V4 are compared. The results show that the mAP (average accuracy) value obtained by using the improved YOLO-V4 model (YOLO-V4-dense) network in this paper is 0.15 higher than that of yolov4. In actual orchard applications, cherries with different ripeness of cherries in the same area can be detected, and the fruits with larger ripeness differences can be artificially intervened, and finally, the yield of cherry fruits can be increased.

[1]  Hong-Yuan Mark Liao,et al.  YOLOv4: Optimal Speed and Accuracy of Object Detection , 2020, ArXiv.

[2]  Guoxu Liu,et al.  YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3 , 2020, Sensors.

[3]  Yi Yang,et al.  Semantic Pooling for Complex Event Analysis in Untrimmed Videos , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  J. B. Li,et al.  Machine vision technology for detecting the external defects of fruits — a review , 2015 .

[7]  Yudong Zhang,et al.  Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach , 2020 .

[8]  Rui Li,et al.  Application of consumer RGB-D cameras for fruit detection and localization in field: A critical review , 2020, Comput. Electron. Agric..

[9]  En Li,et al.  Apple detection during different growth stages in orchards using the improved YOLO-V3 model , 2019, Comput. Electron. Agric..

[10]  Juan Carlos Corrales,et al.  A computer vision system for automatic cherry beans detection on coffee trees , 2020, Pattern Recognit. Lett..

[11]  Yang Yu,et al.  Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN , 2019, Comput. Electron. Agric..

[12]  Raphael Linker,et al.  Determination of the number of green apples in RGB images recorded in orchards , 2012 .

[13]  James Patrick Underwood,et al.  Deep fruit detection in orchards , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[14]  Shaohua Wan,et al.  Faster R-CNN for multi-class fruit detection using a robotic vision system , 2020, Comput. Networks.

[15]  Jing Zhang,et al.  Deep learning based segmentation for automated training of apple trees on trellis wires , 2020, Comput. Electron. Agric..

[16]  Zhihui Li,et al.  Visual saliency guided complex image retrieval , 2020, Pattern Recognit. Lett..

[17]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Mei Jiang,et al.  Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments , 2020, Comput. Electron. Agric..

[19]  Jing Zhang,et al.  Multi-class object detection using faster R-CNN and estimation of shaking locations for automated shake-and-catch apple harvesting , 2020, Comput. Electron. Agric..

[20]  Mahmoud Al-Ayyoub,et al.  Impact of digital fingerprint image quality on the fingerprint recognition accuracy , 2017, Multimedia Tools and Applications.

[21]  Yuanshen Zhao,et al.  A review of key techniques of vision-based control for harvesting robot , 2016, Comput. Electron. Agric..

[22]  Rui Shi,et al.  An attribution-based pruning method for real-time mango detection with YOLO network , 2020, Comput. Electron. Agric..

[23]  Xuan Li,et al.  Four-image encryption scheme based on quaternion Fresnel transform, chaos and computer generated hologram , 2017, Multimedia Tools and Applications.

[24]  Xin Zhang,et al.  Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN , 2020, Comput. Electron. Agric..

[25]  Qinghua Zheng,et al.  Semantics-Preserving Graph Propagation for Zero-Shot Object Detection , 2020, IEEE Transactions on Image Processing.

[26]  Rui-Sheng Jia,et al.  Fast Method of Detecting Tomatoes in a Complex Scene for Picking Robots , 2020, IEEE Access.

[27]  B. Gupta,et al.  Efficient deep learning approach for augmented detection of Coronavirus disease , 2021, Neural computing & applications.

[28]  R. Sparrow,et al.  Robots in agriculture: prospects, impacts, ethics, and policy , 2020, Precision Agriculture.

[29]  Jing Zhang,et al.  Branch detection for apple trees trained in fruiting wall architecture using depth features and Regions-Convolutional Neural Network (R-CNN) , 2018, Comput. Electron. Agric..

[30]  Rui Li,et al.  Improved Kiwifruit Detection Using Pre-Trained VGG16 With RGB and NIR Information Fusion , 2020, IEEE Access.

[31]  Vladimir Soloviev,et al.  Using YOLOv3 Algorithm with Pre- and Post-Processing for Apple Detection in Fruit-Harvesting Robot , 2020, Agronomy.