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

TL;DR

This paper presents a computer vision approach using a compressed YOLOv5 model via knowledge distillation to accurately count citrus fruits and estimate yield, aiding pre-harvest planning.

Contribution

It introduces a novel combination of fruit counting with a compressed deep learning model and linear regression for yield estimation in citrus trees.

Findings

High accuracy in fruit counting

Effective yield approximation

Model compression improves efficiency

Abstract

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