Canopy recognition of cherry fruit tree based on SegNet network model

Lijun Qi; Jiarui Zhou; Junjie Wan; Zepeng Yang; Hao Zhang; Zhenzhen Cheng

doi:10.1117/12.2605881

15 September 2021 Canopy recognition of cherry fruit tree based on SegNet network model

Lijun Qi, Jiarui Zhou, Junjie Wan, Zepeng Yang, Hao Zhang, Zhenzhen Cheng

Proceedings Volume 11915, International Conference on Optics and Image Processing (ICOIP 2021); 119150H (2021) https://doi.org/10.1117/12.2605881
Event: International Conference on Optics and Image Processing (ICOIP 2021), 2021, Guilin, China

Abstract

Accurate acquisition of the canopy information of fruit trees is very important for the precise variable spray of modern orchards. Using drones to obtain fruit tree canopy images is a better nondestructive method, but the lighting conditions in the orchard are complex. It is difficult to quickly extract the canopy of fruit trees from the aerial images of orchard by UAV, which can be used to guide UAV to apply pesticide in real time and accurately. Therefore, we propose a method for canopy segmentation of fruit tree canopy images using SegNet network model. We use images obtained from modern orchards to verify the accuracy and real time of the network, and use four indicators to compare our network model with Unet and FCN-8s network models: accuracy, precision, recall and harmonic average. After that, we optimize the SegNet network model structure from three aspects: input method, network training parameters and neural network structure. The results show that SegNet has achieved satisfactory results in segmenting the canopy. The optimized SegNet model has an average recognition accuracy of 95.30%, and the recognition time of a single image is as low as 0.045 s, and it has good robustness in both strong and weak light environments. This shows that using SegNet network segmentation to extract fruit tree canopy information is a promising method, and it can provide a reference for real-time and accurate spray of UAVs.

Citation Download Citation

Lijun Qi, Jiarui Zhou, Junjie Wan, Zepeng Yang, Hao Zhang, and Zhenzhen Cheng "Canopy recognition of cherry fruit tree based on SegNet network model", Proc. SPIE 11915, International Conference on Optics and Image Processing (ICOIP 2021), 119150H (15 September 2021); https://doi.org/10.1117/12.2605881

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KEYWORDS

Data modeling

Optimization (mathematics)

Image segmentation

RGB color model

Convolution

Convolutional neural networks

Neural networks

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