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Robust crop disease diagnosis system

A crop disease and diagnosis system technology, applied in the field of robust crop disease diagnosis system, can solve the problems of mobile terminal automation scheme, lack of high accuracy, etc., and achieve the effect of reliable state estimation, fast speed and high precision

Active Publication Date: 2020-12-18
SICHUAN UNIV
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in terms of crop identification, there is still no high-accuracy mobile terminal automation solution proposed at home and abroad.

Method used

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Embodiment Construction

[0033] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0034] A robust crop disease diagnosis system comprising the following steps:

[0035] S1. The user takes pictures of the leaves of the crops to be detected with a smartphone, and obtains a picture of the leaves of the crops to be detected;

[0036] S2, for the picture obtained in step S1, use the Canny edge detection algorithm to process to obtain the binary image of the edge of the leaf surface;

[0037] S3. According to the picture obtained in step S1, send it to the non-local spatial attention convolution branch extraction of the pest detection module to obtain the first feature;

[0038] S4. According to the binary image obtained in step S2, send it to the fast down-sampling branch for extraction, and obtain the second feature;

[0039] S5....

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Abstract

The invention provides a robust crop disease diagnosis system. The system realizes disease diagnosis through the following steps that: S1, a user shoots the leaf surface of a crop to be detected through a smart phone to obtain the leaf surface picture of the crop to be detected; S2, the picture obtained in the step S1 is processed by using a Canny edge detection algorithm to obtain a binary imageof the edge of a leaf surface ; and S3, according to the picture obtained in the step S1, the binary picture is sent to a non-local space attention convolution integral branch of a pest detection module for extraction to obtain a first feature. According to the robust crop disease diagnosis system, the health state of a plant is judged according to stem and leaf pictures of the plant acquired by acamera through a computer algorithm; if a disease exists, the type of the disease is diagnosed; and the robust crop disease diagnosis system belongs to the technical field of computer vision. The invention aims to realize a deep neural plant disease and insect pest detection model which can be operated at a mobile terminal and has both real-time performance and accuracy, and replaces a current manual identification mode.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a robust crop disease diagnosis system. Background technique [0002] Agriculture is an important pillar industry. For a long time, due to the harm of diseases and insect pests, the production of food crops and economic crops has been reduced, which has seriously affected the sustainable development of regional agricultural economy. Crop diseases and insect pests are one of the main agricultural disasters in my country. Diagnosis and treatment of diseases will help to prevent and control diseases and insect pests in time, increase crop yields and reduce economic losses. However, there are so many types of crop diseases and insect pests, it is difficult for ordinary people to diagnose the accurate disease of the diseased plants only by their appearance. [0003] In the traditional practice, crop planters usually directly diagnose the pests and diseases of crops. This meth...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/20G06V10/44G06N3/045G06F18/253Y02A40/10
Inventor 雷印杰陈浩楠王浩
Owner SICHUAN UNIV
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