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Smart Agriculture ›› 2021, Vol. 3 ›› Issue (2): 100-114.doi: 10.12133/j.smartag.2021.3.2.202105-SA005

• Information Processing and Decision Making • Previous Articles     Next Articles

Yield Estimation Method of Apple Tree Based on Improved Lightweight YOLOv5

LI Zhijun1,2(), YANG Shenghui1,2, SHI Deshuai1,2, LIU Xingxing1,2, ZHENG Yongjun1,2()   

  1. 1.College of Engineering, China Agricultural University, Beijing 100083, China
    2.Yantai Institute of China Agricultural University, Yantai 264670, China
  • Received:2021-05-13 Revised:2021-06-10 Online:2021-06-30 Published:2021-08-25
  • corresponding author: Yongjun ZHENG E-mail:335022969@qq.com;zyj@cau.edu.cn


Yield estimation of fruit tree is one of the important works in orchard management. In order to improve the accuracy of in-situ yield estimation of apple trees in orchard, a method for the yield estimation of single apple tree, which includes an improved YOLOv5 fruit detection network and a yield fitting network was proposed. The in-situ images of the apples without bags at different periods were acquired by using an unmanned aerial vehicle and Raspberry Pi camera, formed an image sample data set. For dealing with no attention preference and the parameter redundancy in feature extraction, the YOLOv5 network was improved by two approaches: 1) replacing the depth separable convolution, and 2) adding the attention mechanism module, so that the computation cost was decreased. Based on the improvement, the quantity of fruit was estimated and the total area of the bounding box of apples were respectively obtained as output. Then, these results were used as the input of the yield fitting network and actual yields were applied as the output to train the yield fitting network. The final model of fruit tree production estimation was obtained by combining the improved YOLOv5 network and the yield fitting network. Yield estimation experimental results showed that the improved YOLOv5 fruit detection algorithm could improve the recognition accuracy and the degree of lightweight. Compared with the previous algorithm, the detection speed of the algorithm proposed in this research was increased by up to 15.37%, while the mean of average accuracy (mAP) was raised up to 96.79%. The test results based on different data sets showed that the lighting conditions, coloring time and with white cloth in background had a certain impact on the accuracy of the algorithm. In addition, the yield fitting network performed better on predicting the yield of apple trees. The coefficients of determination in the training set and test set were respectively 0.7967 and 0.7982. The prediction accuracy of different yield samples was generally stable. Meanwhile, in terms of the with/without of white cloth in background, the range of relative error of the fruit tree yield measurement model was respectively within 7% and 13%. The yield estimation method of apple tree based on improved lightweight YOLOv5 had good accuracy and effectiveness, which could achieve yield estimation of apples in the natural environment, and would provide a technical reference for intelligent agricultural equipment in modern orchard environment.

Key words: apple in-situ yield estimation, deep learning, fruit detection, BP neural network, YOLOv5

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