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

? 信息處理與決策 ? 上一篇    下一篇

基于輕量化改進YOLOv5的蘋果樹產量測定方法

李志軍1,2(), 楊圣慧1,2, 史德帥1,2, 劉星星1,2, 鄭永軍1,2()   

  1. 1.中國農業大學 工學院,北京 100083
    2.中國農業大學 煙臺研究院,山東 煙臺 264670
  • 收稿日期:2021-05-13 修回日期:2021-06-10 出版日期:2021-06-30 發布日期:2021-08-25
  • 基金資助:
    山東煙臺校地融合發展項目(2019XDRHXMPT30);國家重點研發項目(2018YFD0700603)
  • 作者簡介:李志軍(1996-),男,碩士研究生,研究方向為智能農業裝備。E-mail:335022969@qq.com。
  • 通訊作者: 鄭永軍 E-mail:335022969@qq.com;zyj@cau.edu.cn

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

摘要:

果樹測產是果園管理的重要環節之一,為提升蘋果果園原位測產的準確性,本研究提出一種包含改進型YOLOv5果實檢測算法與產量擬合網絡的產量測定方法。利用無人機及樹莓派攝像頭采集摘袋后不同著色時間的蘋果果園原位圖像,形成樣本數據集;通過更換深度可分離卷積和添加注意力機制模塊對YOLOv5算法進行改進,解決網絡中存在的特征提取時無注意力偏好問題和參數冗余問題,從而提升檢測準確度,降低網絡參數帶來的計算負擔;將圖片作為輸入得到估測果實數量以及邊界框面總積。以上述檢測結果作為輸入、實際產量作為輸出,訓練產量擬合網絡,得到最終測產模型。測產試驗結果表明,改進型YOLOv5果實檢測算法可以在提高輕量化程度的同時提升識別準確率,與改進前相比,檢測速度最大可提升15.37%,平均mAP最高達到96.79%;在不同數據集下的測試結果表明,光照條件、著色時間以及背景有無白布均對算法準確率有一定影響;產量擬合網絡可以較好地預測出果樹產量,在訓練集和測試集的決定系數R2分別為0.7967和0.7982,均方根誤差RMSE分別為1.5317和1.4021 ㎏,不同產量樣本的預測精度基本穩定;果樹測產模型在背景有白布和無白布的條件下,相對誤差范圍分別在7%以內和13%以內。本研究提出的基于輕量化改進YOLOv5的果樹產量測定方法具有良好的精度和有效性,基本可以滿足自然環境下樹上蘋果的測產要求,為現代果園環境下的智能農業裝備提供技術參考。

關鍵詞: 蘋果原位測產, 深度學習, 果實檢測, BP神經網絡, YOLOv5

Abstract:

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

中圖分類號: 

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