An automatic identification method of crop diseases and insect pests adapted to the field

Abstract

The invention relates to the field of computer vision, in particular to a field-adapted automatic identification method for crop diseases and insect pests. The method includes the following steps: S1, obtaining the original crop image data, and preprocessing it; S2, inputting the preprocessed crop image original data into the improved automatic identification model of crop diseases and insect pests, and predicting the corresponding Pest category; the network architecture of the improved automatic identification model of crop pests and diseases is: on the backbone network of the convolutional neural network, two branches of channel orthogonal constraints and species classification constraints are added, and the channel orthogonal constraints are added to the backbone network. On the last layer of features output by the network, the species classification constraint is added to any feature output by the backbone network. The method can be used to accurately identify the types of pests and diseases, without the need for managers to have the professional knowledge of domain experts, and the performance of the model in field environment identification is improved.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a field-adapted automatic identification method for crop diseases and insect pests. Background technique [0002] From 2006 to 2015, my country's crop diseases, insect pests, weeds and rodents were generally in a serious state, and the average annual loss of grain accounted for 20.88% of the country's total grain output. The sources of crop diseases and insect pests mainly include bacteria, fungi, oomycetes, viruses, nematodes and insects, etc. After the crops are infected with diseases, the leaves generally have symptoms such as spots, discoloration, deformity, wilting and necrosis. The health of crops is a condition for the survival of agricultural workers, and diagnosing these symptoms requires a high level of expertise, so it is of great significance to develop a method that can automatically identify crop diseases. [0003] Compared with traditional expert diagnosis methods t...

AI Technical Summary

Problems solved by technology

As the closest prior art of the present invention, the paper "Using Deep Learning for Image-Based Plant Disease Detection" and the paper "Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection" have provided detailed introductions, and the method has trained It is used to realize the classification neural network of plant diseases and insect pests, and realize the automatic identification of crop diseases and insect pests. They have high recognition accuracy under controlled laboratory conditions (with high requirements for lighting posture and background), but when it is applied The performance will drop sharply in the real field

[0004] The existing methods for automatic identification of plant diseases and insect pests still have great limitations when solving the problem of automatic identification of field crop diseases and insect pests. The main problems are as follows: 1. The images of crops are usually collected under controlled conditions in the laboratory. In terms of crop growth conditions, crop images for identifying pests and diseases are too ideal, so the trained network does not perform well in actual measurements, and can only recognize single, frontal, uniformly illuminated, and single-background crop leaves

The above situation will reduce the accuracy of identifying crop pests and diseases

[0005] Considering the above limitations of the existing methods, if a more robust automatic identification algorithm for crop diseases and insect pests in the real field is proposed, the following three main problems will be encountered: 1. The background of the crop images in the data set is single, but The background of the leaf image in the wild environment is changeable, such as figure 1 as shown in (a)

Images