A high-accuracy genotype classification approach using time series imagery

Genotype classification plays a vital role in cultivar evaluation, selection, and production. However, classifying plant genotypes by phenotypes remains an unresolved issue. In this paper, a high-accuracy approach is proposed for plant genotype classification. Based on the Densenet201 and bi-directional Long Short-Term Memory model (bi-directional LSTM), a Densenet201-BLSTM model is given in the approach for classifying various genotypes based on time series of plant images. The growth and development dynamic behaviours and important phenotypes of plants are bi-directionally encoded by the proposed Densenet201-BLSTM to model the complex relationship between phenotypes and genotypes. The accuracy of genotype classification obtained by the proposed DenseNet201-BLSTM model on the test dataset reaches 98.31%. The first attempt is made to classify genotypes of panicoid grain crops. What's more, the proposed genotype classification approach will be useful for the classification of progeny accessions based on their similarity to reference accessions.(c) 2022 IAgrE. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a high-accuracy approach for plant genotype classification using a combination of Densenet201 and bi-directional LSTM models. The proposed approach achieves an accuracy of 98.31% in classifying different genotypes of panicoid grain crops, and it can also be useful for the classification of progeny accessions based on their similarity to reference accessions.