A review of seed quality detection based on deep learning
Quality seeds lead to abundant grain production,which in turn ensures food security.As a major grain producer,China needs to prioritize seed quality to significantly improve crop yield and quality.There-fore,the development of fast and non-destructive techniques for seed quality assessment is of great signifi-cance in agricultural production.In recent years,machine vision has been widely used as a non-destructive method for grain seed quality inspection.By training models on grain seed image data,accurate and rapid identification of seed appearance quality can be achieved.This review focuses on the perspective of datasets and summarizes the research progress in the application of deep learning algorithms for appearance quality inspection of major grain seeds such as wheat,rice,and maize.The review also provides insights into future research directions based on the limitations identified in these studies.
NETL
NSTL
万方数据
