Deep learning Method overview and review of use for fruit detection and yield estimation
Abstract A review of developments in the rapidly developing field of deep learning is presented. Recommendations are made for original contributions to the literature, as opposed to formulaic applications of established methods to new application areas (e.g. to new crops) including the use of standard metrics (e.g. F1 score, the harmonic mean between Precision and Recall) for model comparison involving binary classification. A recommendation for the provision and use of publically available fruit in orchard image sets is made, to allow method comparisons and for implementation of transfer learning for deep learning models trained on the large public generic datasets. Emphasis is placed on practical aspects for application of deep learning models for the task of fruit detection and localisation, in support of tree crop load estimation. Approaches to the extrapolation of tree image counts to orchard yield estimation are also reviewed, dealing with the issue of occluded fruit in imaging. The review is intended to assist new users of deep learning image processing techniques, and to influence the direction of the coming body of application work on fruit detection.
Comput. Electron. Agric.