Farmland disease monitoring system based on machine vision

A machine vision and monitoring system technology, applied in the agricultural field, can solve the problems of real-time, poor accuracy, time-consuming and labor-intensive, etc., and achieve the effect of reducing recognition errors and facilitating viewing

Active Publication Date: 2020-02-28
YULIN UNIV
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  • Summary
  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

At present, the monitoring of farmland diseases is still in the way of manual fixed-point inspection, which is time-consuming and labor-intensive, and the real-time performance and accuracy are poor.

Method used

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  • Farmland disease monitoring system based on machine vision

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Embodiment Construction

[0026] In order to make the objects and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0027] Such as figure 1 As shown, the embodiment of the present invention provides a machine vision-based farmland disease monitoring system, including a target image acquisition module and a monitoring terminal. The target image acquisition module collects images of farmland crops to be monitored at fixed points through the UAV module. And the collected image data is sent to the monitoring terminal in real time through the wireless communication module; the target images collected by the drone all carry POS data, and the POS data includes at least latitude, longitude, elevation, heading angle (Phi), Pitch angle (Omega) and roll angle (Kappa); ...

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Abstract

The invention discloses a farmland disease monitoring system based on machine vision, and the system comprises a target image collection module and a monitoring terminal, and the interior of the monitoring terminal is provided with a disease and pest recognition module which achieves the recognition of holes, spots, pests and pest tracks in a picture based on a neural network model; the neural network model which adopts an ssd target detection algorithm, and an inception v2 deep neural network is trained by using a coco data set; and a disease and insect pest statistical analysis module whichis used for communicating the component external graphic template and the measuring scale to measure holes and spots on the picture, and realizing statistical analysis of disease and insect pests according to an recognition result of the disease and insect pest reognition module. According to the method, holes, spots and injurious insects on leaf surfaces and rhizomes of the crops are quickly recognized by adopting an inception v2 deep neural network, so that the current pest and disease damage conditions of the crops can be accurately obtained.

Description

technical field [0001] The invention relates to the agricultural field, in particular to a machine vision-based farmland disease monitoring system. Background technique [0002] The control of diseases and insect pests is an important factor to ensure the growth of crops. With the development of science and technology, the methods of prevention and control of diseases and insect pests have become various. At present, the monitoring of farmland diseases is still in the way of manual fixed-point inspection, which is time-consuming and labor-intensive, and the real-time performance and accuracy are poor. Contents of the invention [0003] The purpose of the present invention is to provide a machine vision-based farmland disease monitoring system, which realizes real-time monitoring and analysis of farmland disease conditions with high accuracy. [0004] In order to achieve the above object, the technical scheme that the present invention takes is: [0005] A farmland diseas...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G05D1/10
CPCG05D1/101G06V20/188G06F18/22Y02A40/10
Inventor 吴疆蒋平董婷马银霞
Owner YULIN UNIV
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