“…As fruit and flower counts are also vital for yield forecasting applications, the precise detection and counting of these components have been a research focus for high‐throughput methods, with around half of the literature pertaining to automation of phenotypic traits centring around this. In image‐based phenotyping, CNNs have been used to address fruit (Chen et al., 2019; Fan et al., 2022; Ilyas et al., 2021; Kerfs et al., 2017; Kim et al., 2020; Kirk et al., 2020; Lamb & Chuah, 2018; Yu et al., 2019; Zhang et al., 2022; Zhou et al., 2020) and flower (Heylen et al., 2021; Lin & Chen, 2018) detection in real‐world agricultural conditions, as they offer greater robustness to the varying environmental conditions experienced than traditional machine learning methods that use manually defined features. However, a challenge for detection in real‐world environments is occlusion, which can be minimised by selecting an appropriate viewpoint from which to collect data so as to maximise the prominence of the organ of interest in the image.…”