Wheat seed classification based on improved lightweight EfficientNet-V2 model
Wheat seeds are often mixed with other seeds such as oats and barley,and how to classify seeds of sufficient purity is an important problem.In order to address this issue,a seed classification method based on an improved EfficientNet-V2 model is proposed,and the enhanced network is named CA-EfficientNet-V2_xs.Firstly,a dataset is constructed by purchasing commonly used wheat seeds(including common impurities such as oats and barley).Secondly,in order to expedite training and overcome the issue of insufficient data in the self-made dataset,transfer learning is adopted.Thirdly,in order to assist the model in more accurately locating and identifying the target of interest,the Coordinate Attention(CA)mechanism is adopted to replace the SE attention mechanism.Finally,the network structure is streamlined to reduce the model size and enhance training speed.The experimental results show that the classification accuracy of the improved model reaches 99.7%,which is 1.3%higher than that of the network before the improvement.Compared with 78 MB in the EfficientNet-V2_s model,the improved model size is reduced to 3.8 MB and the model size is reduced.The improved model is faster than mainstream networks.
wheat seeddeep learningattention mechanismtransfer learningEfficientNet-V2 model
NETL
NSTL
万方数据
