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In the field of planting fruit trees, pre-harvest estimation of fruit yieldis important for fruit storage and price evaluation. However, considering thecost, the yield of each tree cannot be assessed by directly picking theimmature fruit. Therefore, the problem is a very difficult task. In this paper,a fruit counting and yield assessment method based on computer vision isproposed for citrus fruit trees as an example. Firstly, images of single fruittrees from different angles are acquired and the number of fruits is detectedusing a deep Convolutional Neural Network model YOLOv5, and the model iscompressed using a knowledge distillation method. Then, a linear regressionmethod is used to model yield-related features and evaluate yield. Experimentsshow that the proposed method can accurately count fruits and approximate theyield.

Yield Evaluation of Citrus Fruits based on the YoloV5 compressed by Knowledge Distillation