DLML-PC: an automated deep learning and metric learning approach for precise soybean pod classification and counting in intact plants.

ABSTRACT: Pod numbers are important for assessing soybean yield. How to simplify the traditional manual process and determine the pod number phenotype of soybean maturity more quickly and accurately is an urgent challenge for breeders. With the development of smart agriculture, numerous scientists have explored the phenotypic information related to soybean pod number and proposed corresponding methods. However, these methods mainly focus on the total number of pods, ignoring the differences between different pod types and do not consider the time-consuming and labor-intensive problem of picking pods from the whole plant. In this study, a deep learning approach was used to directly detect the number of different types of pods on non-disassembled plants at the maturity stage of soybean. Subsequently,

SUBMITTER: Guo Y 

PROVIDER: S-EPMC12319039 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature