An Improved EfficientNetV2 Model Based on Visual Attention Mechanism: Application to Identification of Cassava Disease.

With the characteristic of high recognition rate and strong network robustness, convolutional neural network has now become the most mainstream method in the field of crop disease recognition. Aiming at the problems with insufficient numbers of labeled samples, complex backgrounds of sample images, and difficult extraction of useful feature information, a novel algorithm is proposed in this study based on attention mechanisms and convolutional neural networks for cassava leaf recognition. Specifically, a combined data augmentation strategy for datasets is used to prevent single distribution of image datasets, and then the PDRNet (plant disease recognition network) combining channel attention mechanism and spatial attention mechanism is proposed.

A Method for Medical Microscopic Images' Sharpness Evaluation Based on NSST and Variance by Combining Time and Frequency Domains.

An algorithm for a sharpness evaluation of microscopic images based on non-subsampled shearlet wave transform (NSST) and variance is proposed in the present study for the purpose of improving the noise immunity and accuracy of a microscope's image autofocus. First, images are decomposed with the NSST algorithm; then, the decomposed sub-band images are subjected to variance to obtain the energy of the sub-band coefficients; and finally, the evaluation value is obtained from the ratio of the energy of the high- and low-frequency sub-band coefficients. The experimental results show that the proposed algorithm delivers better noise immunity performance than other methods reviewed by this study while maintaining high sensitivity.