CN202084060U - Real-time classifying system of paddy rice pest - Google Patents
Real-time classifying system of paddy rice pest Download PDFInfo
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- CN202084060U CN202084060U CN2011201136956U CN201120113695U CN202084060U CN 202084060 U CN202084060 U CN 202084060U CN 2011201136956 U CN2011201136956 U CN 2011201136956U CN 201120113695 U CN201120113695 U CN 201120113695U CN 202084060 U CN202084060 U CN 202084060U
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Abstract
The utility model discloses a real-time classifying system of paddy rice pests. The system includes a camera used to shoot pictures for to-be-test pests, a video decoding unit used to input decoded image into a processor, a storage unit used to store images and sample trained results, a processor used to classifying the pests according to the sample trained results, and a display screen used to display classifying result. By using the real-time classifying system of the paddy rice pests, the real-time classification of the paddy rice pests can be realized. The pictures of pests can be transmitted to a far end in real time through wireless transmission for further statistic analyzing, filing and fine classifying by plant protection specialists.
Description
Technical field
The utility model relates to the plant protection technology field, relates in particular to a kind of rice grub real-time grading system.
Background technology
Diseases and pests of agronomic crop is one of main agricultural disaster of China, and it has, and kind is many, influence is big, also break out the characteristics of causing disaster often, and its occurrence scope and the order of severity often cause heavy losses to Chinese national economy, particularly agricultural production.According to data, the whole world has at least 6% crops be eaten by insect in growth period every year, if human and material resources and technology do not catch up with, may reach 10%-30%, and in China because insect pest, the whole nation annual grain and cotton loss about 15%, the main cause that causes this situation be exactly pest detection that the forecast is inaccurate is true, untimely, opportunity is missed in control.
Paddy rice is one of world's staple food crop.China's rice growing area accounts for 1/4 of national cereal crops, and output then accounts for over half.Zhejiang Province's paddy rice cultivated area and output also occupy domestic prostatitis.So, be very to be necessary to the research of rice insect pest.
Methods such as collection are clapped, lured to traditional manual detection employing dish usually, utilizes artificial sense to check insect at the scene, by instrument or the direct kinds of with the naked eye differentiating insect such as magnifier, microscopes, and statistical magnitude.This method is a kind of the most directly perceived, easy but very extensive method, and the investigation work amount is big, and single detection area coverage is little, and efficient is lower, investigation cost height.
Summary of the invention
The utility model provides a kind of rice grub real-time grading system, and this system has solved that traditional classification method workload is big, and efficient is low, the problem that cost is high.
A kind of rice grub real-time grading system comprises:
Camera is clapped and is got the image of tested insect;
The video decode module is with input processor behind the picture decoding;
Memory module, memory image and sample training result;
Processor is classified to tested insect according to the sample training result;
Display screen shows classification results.
Preferably, said system also comprises wireless transport module.
A kind of insect real-time grading method of said system comprises sample training and pattern detection:
Described sample training comprises:
(1) training sample of the various rice grubs of collection is some, obtains image;
(2) image is carried out gradation conversion, need the background removal of image is obtained the image of insect before the conversion, the formula of gradation conversion is y=0.3r+0.59g+0.11b, wherein: y represents the gray-scale value behind the gray processing, the value of r presentation video red component, the value of g presentation video green component, the value of b presentation video blue component;
(3) after the gradation conversion, adopt the eigenwert of compressed sensing algorithm abstract image;
With picture size i * h is example (i is a row, h for row), with the gray-scale value of each picture point be that element makes up gray matrix, fold with windrow, make it become the vectorial p of a row (p ∈ R
1 * n, n=i * h), p and compressed sensing observing matrix at random
Eigenwert (vector) x that multiplies each other and obtain image,
(x ∈ R
1 * m),
Be the gaussian random matrix, n represents line number, and m represents columns, and m＜＜n.
(4) described pattern detection comprises:
Obtain the image of test sample book, handle the image of test sample book (2), (3) set by step, obtains the eigenwert of the image of test sample book, utilizes the most contiguous algorithm, determines the type of test sample book.
The most contiguous algorithm comprises: the distance between the eigenwert of calculating test sample image and the eigenwert of training sample image, if a class pest comprises a plurality of training samples, then all distances are asked average, find the training sample of mean distance minimum, the insect type of this training sample is the insect type of test sample book.The formula that calculates distance between two eigenwerts is as follows:
The beneficial effect that the utlity model has is:
(1) realized the real-time grading of rice grub,, the insect picture has been real-time transmitted to far-end, further added up, file and segment for the plant protection expert by wireless transmission.
(2) the compressed sensing theory is applied to during the insect image feature value obtains, adopts stochastic matrix to extract eigenwert, not only effectively suppressed noise, and the angle and the intensity of illumination of taking pictures are not done requirement, be fit to field Real time identification insect more.
(3) the compressed sensing theory is applied to during the insect image feature value obtains, greatly reduces the dimension of data simultaneously, be adapted at conditional lower computer system such as storage, speed and use.
The present invention mainly combines compressed sensing theory, nearest neighbour classification algorithm with advanced person's data processor, 3G wireless transmission method, design portable rice grub system for rapidly identifying, can make things convenient for the farmland management personnel to prevent and treat fast to the rice grub classification.
Description of drawings
Fig. 1 is the structural representation of the utility model system;
Fig. 2 is the operational flowchart of the utility model system.
Embodiment
Rice grub real-time grading system as shown in Figure 1, the storer that comprises processor (CPU) 1 and the video decode module 2 that is connected with processor 1, form by program storage and data-carrier store 3, two control buttons 4, four pilot lamp 5, display screen 6 and wireless transport modules 8, the input end of video decode module 2 connects camera 7, and power module 9 is powered to said apparatus.
The said system workflow as shown in Figure 2, after the power-on module 9, pilot lamp A lights, the voltage of expression total system is normal, the CPU of native system adopts the dsp processor of ADI company, after system powers on, and the program after cpu reset is finished in the loading procedure storer, loading procedure rear indicator light B lights, and CPU is working properly in expression.
Camera 7 can be taken the insect image in real time, and the analog video signal that video decode module 2 is exported camera 7 is decoded into and meets the digital signal input processor 1 ITU656 standard agreement, the YUV4:2:2 form.The A that pushes button, the CPU images acquired is stored in the image of gathering in the data-carrier store (SDRAM) simultaneously, simultaneously image is discerned processing, insect is classified, and classification results is shown on display screen 6.
System adopts wireless transport module 8 to realize wireless transmission, wireless transport module 8 links to each other with CPU by USB interface, the indication CPU that pushes button behind the B sends and gathers good image, CPU at first connects with far-end server by wireless transport module, carry out the image transmission after connecting, adopt the TCP host-host protocol, light pilot lamp D after sub-picture transmission is finished.
A rice leaf roller photo (150 * 105 pixel) of getting with take is an example, and the method for said apparatus identification insect is:
1, at first the background of image is scratched and remove, then image being carried out gradation conversion handles, the formula that gradation conversion is handled is y=0.3r+0.59g+0.11b, wherein: y represents the gray-scale value after the gradation conversion, the value of r presentation video red component, the value of g presentation video green component, the value of b presentation video blue component;
2, the gray-scale value with each picture point of image after the gradation conversion is the gray matrix that element makes up the insect image, and is folded with windrow to the element of this gray matrix, obtains the vectorial p of row (p ∈ R
1 * n, n=15750), p and random Gaussian matrix
Multiplying each other obtains the eigenwert x of insect image,
(x ∈ R
1 * 3150),
Before detection, also need to carry out sample training, each 20 of the striped rice borer, yellow rice borer, rice leaf roller, pink rice borer, small brown rice planthopper, white backed planthopper, brown paddy plant hopper, Da Bai leafhopper, green leaf hopper adult sample image of gathering different attitudes are as training sample, handle according to step 1,2 then, obtain the eigenwert of all insect images.
3, according to the most contiguous algorithm insect is classified, detailed process is:
Calculate by following formula between the eigenwert of the eigenwert of test sample book and each training sample image apart from χ
2,
The mean value of distance is respectively 2499753.5,267186.9,3929645.3,2687734.8,2811369.3,4316368.1,3793615.3,4516837.5 and 3086334.5 between the eigenwert of the eigenwert of insect image shown in Figure 2 and pink rice borer, rice leaf roller, big clear leafhopper, striped rice borer, yellow rice borer, small brown rice planthopper, white backed planthopper, brown paddy plant hopper, each 20 sub-picture of Da Bai leafhopper, can judge that this insect is a rice leaf roller, be consistent with actual conditions, visible system of the present invention is practicable.
Claims (2)
1. a rice grub real-time grading system is characterized in that, comprising:
Camera is clapped and is got the image of tested insect;
The video decode module is with input processor behind the picture decoding;
Memory module, memory image and sample training result;
Processor is classified to tested insect according to the sample training result;
Display screen shows classification results.
2. rice grub real-time grading according to claim 1 system is characterized in that, comprises the wireless transport module that is connected with processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011201136956U CN202084060U (en) | 2011-04-18 | 2011-04-18 | Real-time classifying system of paddy rice pest |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011201136956U CN202084060U (en) | 2011-04-18 | 2011-04-18 | Real-time classifying system of paddy rice pest |
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| CN202084060U true CN202084060U (en) | 2011-12-21 |
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| CN2011201136956U Expired - Fee Related CN202084060U (en) | 2011-04-18 | 2011-04-18 | Real-time classifying system of paddy rice pest |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102239793A (en) * | 2011-04-18 | 2011-11-16 | 浙江大学 | Real-time classification method and system of rice pests |
| CN103957389A (en) * | 2014-05-13 | 2014-07-30 | 重庆大学 | 3G video transmission method and system based on compression sensing |
-
2011
- 2011-04-18 CN CN2011201136956U patent/CN202084060U/en not_active Expired - Fee Related
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102239793A (en) * | 2011-04-18 | 2011-11-16 | 浙江大学 | Real-time classification method and system of rice pests |
| CN103957389A (en) * | 2014-05-13 | 2014-07-30 | 重庆大学 | 3G video transmission method and system based on compression sensing |
| CN103957389B (en) * | 2014-05-13 | 2017-02-22 | 重庆大学 | 3G video transmission method and system based on compression sensing |
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| Date | Code | Title | Description |
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| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C17 | Cessation of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20111221 Termination date: 20140418 |