基于元学习的植物虫害识别原型网络VGG-ML.

[Objectives]To solve the problem of relying on a large number of training samples when using deep learning technology to identify plant pests, a VGG(visual geometry group)prototype network(VGG-meta learning, VGG-ML)based on the idea of meta-learning was proposed in this pater to identify plant pest types in small sample backgrounds. [Methods]VGG16 was used as the embedding unit to extract the characteristics and category characteristics of the plant pest sample. In order to improve the recognition ability of the network for new categories, and solve the problem of low recognition accuracy of plant pests and unrecognizable new categories of pests in the case of small samples, the dataset that the training set and the test set from different data categories was adopted in this pater. The test set was divided into a support set(obtaining class prototypes)and a query set(sample prototypes), and the similarity between sample prototypes and class prototypes was measured by Euclidean distance to determine the category to which the samples belong. [Results]Twenty four kinds of agricultural insect pests such as aphids, armyworms and flea beetles of 11 plants such as corn, sugar beet, and alfalfa in the public dataset IP102 were used as training data, and 8 kinds of common aquatic rice pests such as rice leaf roller, rice leaf caterpillar, Asian rice borer, rice gall midge, rice stem fly, rice water weevil, rice leaf hopper, and rice bract were used as test data. The recognition accuracy of VGG-ML was 67.98% and 81.5% respectively under 5-way, 1-shot and 5-way, 5-shot conditions, which was 3.53 and 4.4 percentage points higher than the original prototype network, respectively. Compared with the ResNet50 and VGG16 networks based on transfer learning, the accuracy of the 5-way and 5-shot tests increased by 28.65 and 25.94 percentage points, respectively. [Conclusions]VGG-ML was effective and reliable in the identification of plant pest types in small samples, and it could be applied to the identification of small samples of plants. [ABSTRACT FROM AUTHOR]