Editorial: Bench to Bedside – the translation of intracortical inhibition marker to clinical practice

Pimenta, Danielle Carolina; Lima, Daniel; Slawka, Eric; Pacheco‐Barrios, Kevin; Fregni, Felipe

doi:10.21801/ppcrj.2022.83.11

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…Only the remaining categories are offered to the model for learning. The average accuracy of the model identification across all categories is verified during the test stage (Pimenta et al, 2022). Table Ⅲ shows the effect of the model on the recognition of new categories of two crops.…”

Section: Resultsmentioning

confidence: 95%

Research on Identification of Crop Leaf Pests and Diseases Based on Few-Shot Learning

Wang,

Zhang,

Han

et al. 2023

Eng. Agríc.

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The yield of crops has a significant impact on economic and social development. It is significant to ensure the healthy growth of crops. Leaves can represent the growth of crops. Crop health can be monitored by analyzing a sufficient number of leaf images. But advanced farming techniques make leaves less susceptible to pests and diseases. Therefore, it is difficult to collect enough leaves with pests and diseases for image analysis. To solve this problem, this research proposed a method based on few-shot learning to identify crop leaf images and judge crop health status. The main structure of the method is a siamese network. The structure of its two subnetworks is the convolution neural network with an attention module. Each subnetwork outputs a feature vector. Measuring the distance of two feature vectors in the feature space, the similarity is calculated. Then the categories of leaf pests and diseases are judged. The experiments in this research were carried out on apple and potato leaves. The accuracy of identifying their pests and diseases reached 98.03% and 97.34% respectively. The experiment proved that when the sample size is small. The method proposed can effectively identify crop leaf pests and diseases.

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Section: Resultsmentioning

confidence: 95%