Yield prediction of root crops in field using remote sensing: A comprehensive review

Abstract

Yield information of root crops guides precision agriculture efforts and optimizes resource allocation. Predicting root crops prior to harvest is crucial to crop management and planning and requires obtaining root crop yield without damaging them. Non-destructive access to yield of root crops is challenging because of the edible portion of the crops being located underground, which impacts precision agriculture technology application. Remote sensing provides a possible way to solve this problem. There are no review reports on yield prediction for root crops using remote sensing, though root crops share the same growth characteristic of producing edible parts underground, which makes their yield prediction techniques similar. In this work, a total of 49 sources on the use of remote sensing techniques for yield prediction of root crops in field were collected, analyzed and discussed from the aspects of remote sensing platforms, input features and modelling methods. In terms of usage counts of remote sensing platforms, ground penetrating radars that are directly exposed to edible parts of root crops have the potential to be applied to root crop yield predictions, while spaceborne platforms are the current trend, accounting for 51 %. Feature combination from environment and crop itself is beneficial to crop yield prediction models, particularly the processed-based crop models. It is recommended to collect data time after ensuring specific root data types. Additionally, full-cycle data is suggested to be used to increase robustness of root crop yield prediction models. The result showed that plant-by-plant detection was only applied to radar-based platforms while spectral-based platforms are still in plot level, which further investigated that improving accuracy of root crop yield prediction through individual above ground phenotypic traits. The review is intended to summarize the development of root crop yield prediction using remote sensing and put forward further for further improvement.