Fast implementation of real-time fruit detection in apple orchards using deep learning

To perform robust and efficient fruit detection in orchards is challenging since there are a number of variances in the working environments. Recently, deep-learning have shown a promising performance in many visual-guided agriculture applications. However, deep-learning based approaches requires labelling on training data, which is a labour-intensive and time-consuming task. In this study, a fast implementation framework of a deep-learning based fruit detector for apple harvesting is developed. The developed framework comprises an auto label generation module and a deep-learning-based fruit detector 'LedNet'. The Label Generation algorithm utilises the multi-scale pyramid and clustering classifier to assist fast labelling of training data. LedNet adopts feature pyramid network and atrous spatial pyramid pooling to improve the detection performance of the model. A light-weight backbone is also developed and utilised to improve computational efficiency. From the experimental results, LedNet achieves 0.821 and 0.853 on recall and accuracy on apple detection in orchards, and its weights size and inference time are 7.4 M and 28 ms, respectively. The experimental results show that LedNet can perform real-time apple detection in orchards robustly and efficiently.