Channel pruned YOLO V5s-based deep learning approach for rapid and accurate apple fruitlet detection before fruit thinning

The rapid and accurate detection of apple fruitlets before fruit thinning is important for the realization of early yield estimation and automatic fruit thinning. However, factors such as a complex growth environment, uncertain illumination, and the clustering and occlusion of apple fruitlets, especially the extreme similarities between fruitlets and backgrounds, make it difficult to effectively detect apple fruitlets before thinning. The overall goal of this study was to develop an accurate apple fruitlet detection method with small model size based on a channel pruned YOLO V5s deep learning algorithm. First, using transfer learning, a YOLO V5s detection model was built to detect apple fruitlets. To simplify the detection model and ensure the detection efficiency, a channel pruning algorithm was used to prune the YOLO V5s model. The pruned model was then fine-tuned to achieve rapid and accurate detection of apple fruitlets. The experimental results showed that the channel pruned YOLO V5s model provided an effective method to detect apple fruitlets under different conditions. A recall, precision, F1 score, and false detection rate of 87.6%, 95.8%, 91.5% and 4.2%, respectively, were achieved; the average detection time was 8 ms per image; and the model size was only 1.4 MB. The performance of our method outperformed seven methods in comparison, indicating that our method simplified the model effectively on the premise of ensuring the detection accuracy. Our method provides a reference for the development of portable mobile fruit thinning terminals, and it can be used to help growers optimise their orchard management. (c) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.

Automated summary

This study developed an accurate method for detecting apple fruitlets based on a channel pruned YOLO V5s deep learning algorithm, with a small model size. Experimental results showed that the method effectively detected apple fruitlets under different conditions, outperforming seven other methods.