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Image-based plant phenotyping represents a transdisciplinary research domain that utilises modern non-invasive technology combined with time-series high-throughput measurements for the extensive analysis and evaluation of plant traits such as morphology, physiology, and biochemistry. Phenotyping can be applied to an individual or group of experimental plants in various environments such as a laboratory, open or closed growth chamber, glasshouse, or field. Recognition of plant accessions plays an important role while evaluating, selecting and producing cultivars. However, it is very challenging, time-consuming and near impossible to correctly identify multiple accessions purely based on visual inspection or through manual phenotypic measurements. This research focuses on recognising different Arabidopsis accessions using high-resolution RGB images collected in two independent experiments with 40 Arabidopsis accessions and a high number of replicates. Experiments are conducted in an automated indoor plant cultivation chamber composed of full temperature control and LED sources ranging from 400 to 750 nm. Following data acquisition, various deep learning models are assessed to achieve interpretable accuracy of accession recognition by using a single image or a group of images taken over several days. Performance of selected models is scrutinised by classifying accessions while receiving a single image at the input or in a second case series of images whose number depends on the pre-defined screening days (for example, 3, 7, or 12 days after screening). Owing to the high variability among replicates in the majority of accessions, data complexity has to be reduced by extracting general leaf phenotypic traits and re-defining data split using statistical methods such as similarity matrix. This leads to achieving the desired accuracy (>92%) and precision in terms of correct recognition of various Arabidopsis accessions. The best-performing deep learning models utilised in this computer vision task could be further applied in the glasshouse environment to identify unknown accessions and therefore predict plant growth. https://icar2023.org/program/ |
