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Online real-time detection system and method for incidence of gibberella zeae infecting wheat based on embedding deep learning

A deep learning and detection system technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as the accumulation and adhesion of wheat grains, achieve automation, solve accumulation and blockage problems, and ensure stability sexual effect

Active Publication Date: 2020-07-03
NANJING AGRICULTURAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a real-time on-line detection system for wheat scab-infected grain rate based on embedded deep learning. Real-time on-line detection of rate of scab-infected kernels and identification of diseased kernels

Method used

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  • Online real-time detection system and method for incidence of gibberella zeae infecting wheat based on embedding deep learning
  • Online real-time detection system and method for incidence of gibberella zeae infecting wheat based on embedding deep learning
  • Online real-time detection system and method for incidence of gibberella zeae infecting wheat based on embedding deep learning

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Experimental program
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Embodiment 1

[0042] like Figures 1 to 6 As shown, a real-time online detection system for wheat scab infection rate based on embedded deep learning, which includes a color sorter, an integrated device, a conveyor belt, an acquisition module and a control module. in:

[0043] (1) Color sorter equipment

[0044] The color sorter is a kind of sorting equipment that uses photoelectric detection technology to automatically sort out the different-color particles in the granular material according to the difference in the optical characteristics of the material. The wheat kernels are preliminarily classified by the color sorter. The color sorter judges and distinguishes the scab-infected wheat kernels and the non-infected wheat kernels according to the color, and the distinguished uninfected wheat kernels directly enter the entrance of the integrated device. The color sorter in this embodiment can adopt existing equipment at present.

[0045] (2) Integrated device design

[0046] The integra...

Embodiment 2

[0056] Realization of disease particle rate detection algorithm:

[0057] 1. Establishment of deep learning detection model

[0058] ① Model sample preparation

[0059] Model sample selection: The wheat used to collect images in the experiment comes from the Food Testing Institute of the Jiangsu Academy of Agricultural Sciences. The experimental wheats are four types of wheat harvested in 2018. The varieties are Yannong 19, Jimai 22, Yangmai 23, Zhen Mai 168. Each variety of wheat comes from different origins, and each origin corresponds to a number, which is stored in ziplock bags with corresponding numbers. Wheat grains were manually classified by senior wheat experts into two categories: infected wheat grains and non-infected wheat grains.

[0060] ②Collect model pictures and make data sets

[0061] Turn on the Raspberry Pi development board to control the industrial camera inside the image acquisition device to capture wheat images. After the collection is complete, m...

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Abstract

The invention discloses an online real-time detection system and method for the incidence of gibberella zeae infecting wheat based on embedding deep learning. The system comprises a color sorter, an integration device, a conveyor belt, a collection module and a control module. The integration device comprises a vibration plate, a vibration motor and a banister brush. The vibration plate is in a slope shape, and the banister brush for leveling wheat grains is arranged above the vibration plate. The surface of the conveyor belt is provided with multiple rows of pits, and the side end, close to adischarging port of the vibration plate, of the conveyor belt is provided with a photoelectric sensor. The integration module comprises a sealed container, a lamp source and a camera. The camera andthe photoelectric sensor are connected with the control module, and the control module receives a picture shot by the camera and a signal detected by the photoelectric sensor. According to the onlinereal-time detection system, the problems of accumulation, adhesion and the like when the wheat grains are placed in the conveying process are solved, the incidence of the gibberella zeae infecting wheat is online detected in real time, and the diseased wheat grains are identified.

Description

technical field [0001] The invention relates to the fields of image recognition technology, instrument control and agricultural product detection, in particular, to a real-time on-line detection system for the rate of wheat scab-infected grains based on embedded deep learning. Background technique [0002] Wheat is the food crop with the largest planting area, the highest total output and the most abundant food processing types in the world. Wheat is one of the main food crops in my country, and its planting area is second only to rice in China. As one of the three major grains, wheat is almost all used for food, and only about one-sixth is used as feed, which has extremely high nutritional value. The high and stable yield of wheat is of great significance to the safety of food production in my country. During its growth and development, various diseases and insect pests will occur, which will affect the yield and quality of wheat. Among them, scab is one of the most impo...

Claims

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Application Information

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IPC IPC(8): B07C5/34B07C5/342B07C5/02B07C5/36G06K9/00
CPCB07C5/34B07C5/3425B07C5/361B07C5/02G06V20/10
Inventor 梁琨李赟莎王秋金李天晴王德州
Owner NANJING AGRICULTURAL UNIVERSITY
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