CN104794712A - Single-pear-tree yield detecting system based on electronic identification - Google Patents
Single-pear-tree yield detecting system based on electronic identification Download PDFInfo
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/73—Deblurring; Sharpening
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
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- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T2207/30—Subject of image; Context of image processing
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Abstract
The invention relates to a single-pear-tree yield detecting system based on electronic identification. The single-pear-tree yield detecting system comprises a CCD (charge coupled device) vision sensor, a clearness processor, a single-side yield identifier and an embedded processor, wherein the CCD vision sensor is used for acquiring images of one side of a single pear tree so as to obtain single-side pear tree images; the clearness processor is used for removing smog of the single-side pear tree images so as to obtain smog-removal single-side pear-tree images, the single-side yield identifier is used for identifying images of the smog-removal single-side pear-tree images so as to obtain the single-side fruit number of the pear tree, and the embedded processor is connected with the single-side yield identifier and used for determining all yield of the single pear tree based on the single-side fruit number of the pear tree. By the single-pear-tree yield detecting system, even if in the smog day, the overall yield of the single pear tree can be accurately predicted.
Description
Technical field
The present invention relates to detection of electrons field, particularly relate to a kind of individual plant pear tree output detection system based on electronic recognition.
Background technology
Pears (Pear), fruit title, rose family Pyrus L, perennial deciduous tree fruit tree, leaf is avette, and how whitely spend, the color of general pears is that crust presents golden yellow or warm yellow, and the inside pulp is then well-illuminated white, fresh and tender succulence, taste is sweet, the micro-acid of core taste, cool sexuality.Pear tree is gathered during fruit maturation between 7 ~ September, can using fresh herb or section dry.To stew together with rock sugar water, can cough be treated.Its kind and kind are extremely many, and cultivated area is also comparatively broad.
But if pear tree is excessively planted, will cause the unbalanced supply-demand in region, the downward price adjustment amplitude of pears is excessive, have impact on the income of peasant on the contrary.Therefore needing to carry out the estimation of pear tree output, carry out the modulation of pear tree planting scheme with the pear tree output estimated, ensureing the price of the Simultaneous Stabilization pears that pear tree is supplied, safeguarding the interests of peasant.
Pear tree output detection system of the prior art or the mode by manual measurement or the mode by image recognition are carried out, but the former too relies on manually, at substantial human cost and time cost, the latter uses single image, single estimation parameter detects, the output detection technique indifference of all kinds pear tree, accuracy is not high, and cannot realize effectively detecting to pear tree under various haze weather.
Therefore, need a kind of new pear tree output detection system, original manual measurement mode can be substituted, improve detection efficiency, simultaneously, overcome haze weather to the impact detected, thus accurately can obtain the true output of every strain pear tree in all weather, the production schedule for orchard worker provides important reference data.
Summary of the invention
In order to solve the problem, the invention provides a kind of individual plant pear tree output detection system based on electronic recognition, by high-definition camera, direct picture collection is carried out to individual plant pear tree, whole fruit number is estimated based on front fruit number, more crucially, also according to atmospheric attenuation model determination haze to the influence factor of image, and the process of mist elimination haze is carried out to the image gathered under various haze weather, has widened the range of application of detection system.
According to an aspect of the present invention, provide a kind of individual plant pear tree output detection system based on electronic recognition, before described detection system is arranged at individual plant pear tree, comprise CCD vision sensor, sharpening processor, one-sided output recognizer and flush bonding processor, described CCD vision sensor is used for carrying out image acquisition to obtain one-sided pear tree image to the side of individual plant pear tree, described sharpening processor is used for carrying out the process of mist elimination haze to described single pear tree image of surveying, to obtain the one-sided pear tree image of mist elimination haze, described one-sided output recognizer is used for carrying out image recognition to the one-sided pear tree image of described mist elimination haze, to obtain the one-sided fruit number of pear tree, described flush bonding processor is connected with described one-sided output recognizer, for determining whole output of described individual plant pear tree based on the one-sided fruit number of described pear tree.
More specifically, described based in the individual plant pear tree output detection system of electronic recognition, also comprise: power supply, comprise solar powered device, accumulator, change-over switch and electric pressure converter, described change-over switch is connected respectively with described solar powered device and described accumulator, determine whether be switched to described solar powered device to be powered by described solar powered device according to accumulator dump energy, described electric pressure converter is connected with described change-over switch, with the 5V voltage transitions will inputted by change-over switch for 3.3V voltage, portable hard drive, for prestoring Pear Fruit upper limit gray threshold, Pear Fruit lower limit gray threshold and estimation multiplying power, described Pear Fruit upper limit gray threshold and described Pear Fruit lower limit gray threshold are used for the Pear Fruit in image to be separated with image background, and described estimation multiplying power is used for based on the whole output of pear tree one-sided output estimation pear tree, display device, is connected with described flush bonding processor, for showing the Word message corresponding with whole output of described individual plant pear tree, described CCD vision sensor is used for taking the front of described individual plant pear tree, to obtain described one-sided pear tree image, described sharpening processor comprises: store sub-device, for prestoring sky upper limit gray threshold and sky lower limit gray threshold, described sky upper limit gray threshold and described sky lower limit gray threshold are for separating of the sky areas of publishing picture in picture, also for prestoring presetted pixel value threshold value, described presetted pixel value threshold value value is between 0 to 255, the sub-device of haze Concentration Testing, is arranged in air, for detecting the haze concentration of individual plant pear tree position in real time, and removes intensity according to haze concentration determination haze, and described haze removes intensity value between 0 to 1, the sub-device of Region dividing, connect described CCD vision sensor to receive described one-sided pear tree image, gray processing process is carried out to obtain gray processing area image to described one-sided pear tree image, also be connected with storage subset, the pixel identification of gray-scale value in described gray processing area image between described sky upper limit gray threshold and described sky lower limit gray threshold is formed gray processing sky sub pattern, described gray processing sky sub pattern is partitioned into obtain the non-sky subimage of gray processing from described gray processing area image, the colour non-sky subimage corresponding with described gray processing non-sky subimage is obtained based on the correspondence position of described gray processing non-sky subimage in described beat image, black channel obtains subset, be connected with described Region dividing subset to obtain the non-sky subimage of described colour, for each pixel in the non-sky subimage of described colour, calculate its R, G, B tri-Color Channel pixel value, the R of all pixels in described colour non-sky subimage, G, B tri-extracts the Color Channel at the minimum Color Channel pixel value place of numerical value in Color Channel pixel value as black channel, overall air light value obtains subset, be connected to obtain presetted pixel value threshold value with described storage subset, obtain subset with described Region dividing subset and described black channel to be connected respectively to obtain described one-sided pear tree image and described black channel, multiple pixels that black channel pixel value in described one-sided pear tree image is more than or equal to presetted pixel value threshold value are formed set of pixels to be tested, the gray-scale value air light value as a whole of the pixel of maximum gradation value will be had in described set of pixels to be tested, atmospheric scattering light value obtains subset, be connected respectively with described Region dividing subset and described haze Concentration Testing subset, to each pixel of described one-sided pear tree image, extract its R, G, in B tri-Color Channel pixel value, minimum value is as target pixel value, use and keep the Gaussian filter EPGF (edge-preserving gaussian filter) at edge to carry out filtering process to obtain filtered target pixel value to described target pixel value, target pixel value is deducted filtered target pixel value to obtain object pixel difference, EPGF is used to carry out filtering process to obtain filtered target pixel value difference to object pixel difference, filtered target pixel value is deducted filtered target pixel value difference and remove reference value to obtain haze, haze is removed intensity and be multiplied by haze removal reference value to obtain haze removal threshold value, get haze and remove minimum value in threshold value and target pixel value as comparison reference, get the atmospheric scattering light value of the maximal value in comparison reference and 0 as each pixel, medium transmission rate obtains subset, obtain subset and described atmospheric scattering light value with described overall air light value to obtain subset and be connected respectively, the atmospheric scattering light value of each pixel is removed value divided by overall air light value to obtain, deducts 1 described except value is to obtain the medium transmission rate of each pixel, sharpening Image Acquisition subset, with described Region dividing subset, described overall air light value obtains subset and is connected respectively with described medium transmission rate acquisition subset, the medium transmission rate of each pixel is deducted to obtain the first difference by 1, described first difference is multiplied by overall air light value to obtain product value, the pixel value of each pixel in described one-sided pear tree image is deducted described product value to obtain the second difference, by described second difference divided by the medium transmission rate of each pixel to obtain the sharpening pixel value of each pixel, in described one-sided pear tree image, the pixel value of each pixel comprises the R of each pixel in described one-sided pear tree image, G, B tri-Color Channel pixel value, correspondingly, the sharpening pixel value of each pixel obtained comprises the R of each pixel, G, B tri-Color Channel sharpening pixel value, the one-sided pear tree image of sharpening pixel value composition mist elimination haze of all pixels, described one-sided output recognizer is connected with described sharpening processor and described portable hard drive respectively, described one-sided output recognizer comprises: the sub-device of contrast strengthen, be connected to obtain the one-sided pear tree image of mist elimination haze with described sharpening processor, contrast enhancement processing is performed, to obtain enhancing image to the one-sided pear tree image of described mist elimination haze, the sub-device of wavelet filtering, is connected with the sub-device of described contrast strengthen, performs wavelet filtering process, to obtain filtering image to described enhancing image, the sub-device of gray processing process, is connected with the sub-device of described wavelet filtering, performs gray processing process, to obtain gray level image to described filtering image, the sub-device of image recognition, be connected respectively with the sub-device of described gray processing process and described portable hard drive, the pixel identification of gray-scale value in described gray level image between described Pear Fruit upper limit gray threshold and described Pear Fruit lower limit gray threshold is formed multiple Pear Fruit subimage, using the sum of multiple Pear Fruit subimage as the one-sided fruit number of pear tree, described flush bonding processor is connected respectively with described portable hard drive, described one-sided output recognizer, calculates the one-sided fruit number of pear tree and the product estimating multiplying power, using the whole output of described product as described individual plant pear tree.
More specifically, described based in the individual plant pear tree output detection system of electronic recognition: the resources occupation rate of described flush bonding processor calculating self, when the resources occupation rate of self is less than the first preset percentage, substitutes the operation of described one-sided output recognizer.
More specifically, described based in the individual plant pear tree output detection system of electronic recognition: when described flush bonding processor is greater than the second preset percentage in the resources occupation rate of self, terminate substituting the operation of described one-sided output recognizer.
More specifically, described based in the individual plant pear tree output detection system of electronic recognition: described first preset percentage and described second preset percentage are pre-stored in described portable hard drive, and described first preset percentage is less than described second preset percentage.
More specifically, described based in the individual plant pear tree output detection system of electronic recognition: described flush bonding processor and described one-sided output recognizer are integrated on one piece of surface-mounted integrated circuit.
Accompanying drawing explanation
Below with reference to accompanying drawing, embodiment of the present invention are described, wherein:
Fig. 1 is the block diagram of the individual plant pear tree output detection system based on electronic recognition illustrated according to an embodiment of the present invention.
Embodiment
Below with reference to accompanying drawings the embodiment of the individual plant pear tree output detection system based on electronic recognition of the present invention is described in detail.
The adaptability of pear tree environment is to external world stronger than apple.Cold-resistant, drought-enduring, waterlogging, Salt And Alkali Tolerance.The area of winter minimum temperature more than-25 degree, most kind can safe overwintering.Well developed root system, vertical root can reach more than 2-3m deeply, and horizontal branch is distributed more widely, is about hat about 2 times.Happiness light happiness temperature, soil layer deep, well-drained gentle slope mountain planting should be selected, especially with sand loam mountain region for ideal.Dryness is strong, and layer is more obvious.Early, fruiting period is long, and namely some kind 2-3 starts result, and the best fruiting period can maintain more than 50 years for result.
The minority kind floral leaf of pear tree is simultaneously open or bloom after first opening up leaf, and pollen after fertilization, fruit germinates, and holder is grown for pulp, and ovary development is core, and Ovule Development is seed.In fruit development process, mainly cell division in early stage, tissue differentiation, stage is that cell expands and pulp maturation.Volume of fruits growth curve becomes S type.Pears root growth has two summit of growths every year: first time summit of growth appear at young sprout when stopping growing; Second time peak appears at the 9-10 month.Under optimum conditions, pears root system can grow in the anniversary, without rest period.
Identification to pear tree output of the prior art is except the manual measurement mode of original backwardness, most employing image recognition technology, but owing to lacking haze eliminating equipment, cause under various haze weather, detected image is smudgy, pear tree output error is too bigger than normal, even likely causes carrying out output identification.
The present invention has built a kind of individual plant pear tree output detection system based on electronic recognition, instead of the mode of manual measurement, introduces haze removal mechanisms at work, effectively ensured the precision that pear tree output is estimated and reliability.
Fig. 1 is the block diagram of the individual plant pear tree output detection system based on electronic recognition illustrated according to an embodiment of the present invention, before described detection system is arranged at individual plant pear tree, comprise CCD vision sensor 1, sharpening processor 2, one-sided output recognizer 3 and flush bonding processor 4, flush bonding processor 4 is connected respectively with CCD vision sensor 1, sharpening processor 2, one-sided output recognizer 3, and sharpening processor 2 is connected respectively with CCD vision sensor 1, one-sided output recognizer 3.
Wherein, described CCD vision sensor 1 is for carrying out image acquisition to obtain one-sided pear tree image to the side of individual plant pear tree, described sharpening processor 2 is for carrying out the process of mist elimination haze to described single pear tree image of surveying, to obtain the one-sided pear tree image of mist elimination haze, described one-sided output recognizer 3 is for carrying out image recognition to the one-sided pear tree image of described mist elimination haze, to obtain the one-sided fruit number of pear tree, described flush bonding processor 4 is connected with described one-sided output recognizer 3, for determining whole output of described individual plant pear tree based on the one-sided fruit number of described pear tree.
Then, continue to be further detailed the concrete structure of the individual plant pear tree output detection system based on electronic recognition of the present invention.
Described detection system also comprises: power supply, comprise solar powered device, accumulator, change-over switch and electric pressure converter, described change-over switch is connected respectively with described solar powered device and described accumulator, determine whether be switched to described solar powered device to be powered by described solar powered device according to accumulator dump energy, described electric pressure converter is connected with described change-over switch, with the 5V voltage transitions will inputted by change-over switch for 3.3V voltage.
Described detection system also comprises: portable hard drive, for prestoring Pear Fruit upper limit gray threshold, Pear Fruit lower limit gray threshold and estimation multiplying power, described Pear Fruit upper limit gray threshold and described Pear Fruit lower limit gray threshold are used for the Pear Fruit in image to be separated with image background, and described estimation multiplying power is used for based on the whole output of pear tree one-sided output estimation pear tree.
Described detection system also comprises: display device, is connected with described flush bonding processor 4, for showing the Word message corresponding with whole output of described individual plant pear tree.
Described CCD vision sensor 1 for taking the front of described individual plant pear tree, to obtain described one-sided pear tree image.
Described sharpening processor 2 comprises:
Store sub-device, for prestoring sky upper limit gray threshold and sky lower limit gray threshold, described sky upper limit gray threshold and described sky lower limit gray threshold are for separating of the sky areas of publishing picture in picture, also for prestoring presetted pixel value threshold value, described presetted pixel value threshold value value is between 0 to 255;
The sub-device of haze Concentration Testing, is arranged in air, for detecting the haze concentration of individual plant pear tree position in real time, and removes intensity according to haze concentration determination haze, and described haze removes intensity value between 0 to 1;
The sub-device of Region dividing, connect described CCD vision sensor 1 to receive described one-sided pear tree image, gray processing process is carried out to obtain gray processing area image to described one-sided pear tree image, also be connected with storage subset, the pixel identification of gray-scale value in described gray processing area image between described sky upper limit gray threshold and described sky lower limit gray threshold is formed gray processing sky sub pattern, described gray processing sky sub pattern is partitioned into obtain the non-sky subimage of gray processing from described gray processing area image, the colour non-sky subimage corresponding with described gray processing non-sky subimage is obtained based on the correspondence position of described gray processing non-sky subimage in described beat image,
Black channel obtains subset, be connected with described Region dividing subset to obtain the non-sky subimage of described colour, for each pixel in the non-sky subimage of described colour, calculate its R, G, B tri-Color Channel pixel value, the R of all pixels in described colour non-sky subimage, G, B tri-extracts the Color Channel at the minimum Color Channel pixel value place of numerical value in Color Channel pixel value as black channel;
Overall air light value obtains subset, be connected to obtain presetted pixel value threshold value with described storage subset, obtain subset with described Region dividing subset and described black channel to be connected respectively to obtain described one-sided pear tree image and described black channel, multiple pixels that black channel pixel value in described one-sided pear tree image is more than or equal to presetted pixel value threshold value are formed set of pixels to be tested, the gray-scale value air light value as a whole of the pixel of maximum gradation value will be had in described set of pixels to be tested;
Atmospheric scattering light value obtains subset, be connected respectively with described Region dividing subset and described haze Concentration Testing subset, to each pixel of described one-sided pear tree image, extract its R, G, in B tri-Color Channel pixel value, minimum value is as target pixel value, use and keep the Gaussian filter EPGF (edge-preserving gaussian filter) at edge to carry out filtering process to obtain filtered target pixel value to described target pixel value, target pixel value is deducted filtered target pixel value to obtain object pixel difference, EPGF is used to carry out filtering process to obtain filtered target pixel value difference to object pixel difference, filtered target pixel value is deducted filtered target pixel value difference and remove reference value to obtain haze, haze is removed intensity and be multiplied by haze removal reference value to obtain haze removal threshold value, get haze and remove minimum value in threshold value and target pixel value as comparison reference, get the atmospheric scattering light value of the maximal value in comparison reference and 0 as each pixel,
Medium transmission rate obtains subset, obtain subset and described atmospheric scattering light value with described overall air light value to obtain subset and be connected respectively, the atmospheric scattering light value of each pixel is removed value divided by overall air light value to obtain, deducts 1 described except value is to obtain the medium transmission rate of each pixel;
Sharpening Image Acquisition subset, with described Region dividing subset, described overall air light value obtains subset and is connected respectively with described medium transmission rate acquisition subset, the medium transmission rate of each pixel is deducted to obtain the first difference by 1, described first difference is multiplied by overall air light value to obtain product value, the pixel value of each pixel in described one-sided pear tree image is deducted described product value to obtain the second difference, by described second difference divided by the medium transmission rate of each pixel to obtain the sharpening pixel value of each pixel, in described one-sided pear tree image, the pixel value of each pixel comprises the R of each pixel in described one-sided pear tree image, G, B tri-Color Channel pixel value, correspondingly, the sharpening pixel value of each pixel obtained comprises the R of each pixel, G, B tri-Color Channel sharpening pixel value, the one-sided pear tree image of sharpening pixel value composition mist elimination haze of all pixels.
Described one-sided output recognizer 3 is connected with described sharpening processor 2 and described portable hard drive respectively, and described one-sided output recognizer 3 comprises:
The sub-device of contrast strengthen, is connected with described sharpening processor 2 to obtain the one-sided pear tree image of mist elimination haze, performs contrast enhancement processing, to obtain enhancing image to the one-sided pear tree image of described mist elimination haze;
The sub-device of wavelet filtering, is connected with the sub-device of described contrast strengthen, performs wavelet filtering process, to obtain filtering image to described enhancing image;
The sub-device of gray processing process, is connected with the sub-device of described wavelet filtering, performs gray processing process, to obtain gray level image to described filtering image;
The sub-device of image recognition, be connected respectively with the sub-device of described gray processing process and described portable hard drive, the pixel identification of gray-scale value in described gray level image between described Pear Fruit upper limit gray threshold and described Pear Fruit lower limit gray threshold is formed multiple Pear Fruit subimage, using the sum of multiple Pear Fruit subimage as the one-sided fruit number of pear tree.
Described flush bonding processor 4 is connected respectively with described portable hard drive, described one-sided output recognizer 3, calculates the one-sided fruit number of pear tree and the product estimating multiplying power, using the whole output of described product as described individual plant pear tree.
Alternatively, described based in the individual plant pear tree output detection system of electronic recognition: described flush bonding processor 4 calculates self resources occupation rate, when the resources occupation rate of self is less than the first preset percentage, substitute the operation of described one-sided output recognizer 3; Described flush bonding processor 4, when the resources occupation rate of self is greater than the second preset percentage, terminates substituting the operation of described one-sided output recognizer 3; Described first preset percentage and described second preset percentage are pre-stored in described portable hard drive, and described first preset percentage is less than described second preset percentage; And, described flush bonding processor 4 and described one-sided output recognizer 3 can be integrated on one piece of surface-mounted integrated circuit.
In addition, haze image can realize the mist elimination haze of image by a series of images treatment facility, to obtain the image of sharpening, improves the visibility of image.These image processing equipments perform different image processing functions respectively, based on the principle that haze is formed, reach the effect removing haze.The sharpening process of haze image all has great using value for dual-use field, and military domain comprises military and national defense, remote sensing navigation etc., and civil area comprises road monitoring, target following and automatic Pilot etc.
The process that haze image is formed can be described by atmospheric attenuation process, relation between haze image and real image and sharpening image can be stated by the medium transmission rate of overall air light value and each pixel, namely when known haze image, according to the medium transmission rate of overall air light value with each pixel, sharpening image can be solved.
There are some effective and through verifying means in the solving of medium transmission rate for overall air light value and each pixel, such as, for the medium transmission rate of each pixel, need the atmospheric scattering light value obtaining overall air light value and each pixel, and the atmospheric scattering light value of each pixel can obtain carrying out the Gaussian smoothing filter at twice maintenance edge to the pixel value of each pixel in haze image, therebetween, the intensity of haze removal is adjustable; And the acquisition pattern of overall air light value has two kinds, a kind of mode is, black channel by obtaining haze image (namely makes the black channel value of some pixels very low in haze image, black channel is R, G, one in B tri-Color Channel), in haze image, obtain by finding the maximum pixel of gray-scale value in multiple pixels that searching black channel pixel value is bigger than normal, be about to the gray-scale value air light value as a whole of that search out, that gray-scale value is maximum pixel, participate in the sharpening process of each pixel in haze image; In addition, overall air light value is also by obtaining with under type: the gray-scale value calculating each pixel in haze image, by the gray-scale value of pixel maximum for gray-scale value air light value as a whole.
Relation between concrete haze image and real image and sharpening image, and the relation between parameters can see above content.
By the discussion to haze image formation basic theory, build the relation between haze image and sharpening image, by this relation of multiple Parametric Representation, subsequently by the multiple parameter values that obtain and haze image and the higher image of reducible acquisition sharpness, some statistical means and empirical means have been used in acquisition due to parameter, therefore the image that described sharpness is higher can not be equal to real image completely, but there is the mist elimination haze effect of certain degree, for the every field operation under haze weather provides effective guarantee.
Adopt the individual plant pear tree output detection system based on electronic recognition of the present invention, unreasonable and owing to not considering that haze weather brings impact to cause the technical matters of system reliability difference on output accuracy of detection for the existing pear tree output detection system testing mechanism based on image recognition technology, by introducing estimation multiplying power, the whole output relying on the one-sided fruit number of pear tree to calculate described individual plant pear tree are made to become possibility, in addition, by introducing sharpening processor, the process of mist elimination haze is carried out to image, the normal work of detection system of the present invention is avoided to be subject to the unfavorable interference of various haze weather.
Be understandable that, although the present invention with preferred embodiment disclose as above, but above-described embodiment and be not used to limit the present invention.For any those of ordinary skill in the art, do not departing under technical solution of the present invention ambit, the technology contents of above-mentioned announcement all can be utilized to make many possible variations and modification to technical solution of the present invention, or be revised as the Equivalent embodiments of equivalent variations.Therefore, every content not departing from technical solution of the present invention, according to technical spirit of the present invention to any simple modification made for any of the above embodiments, equivalent variations and modification, all still belongs in the scope of technical solution of the present invention protection.
Claims (6)
1. the individual plant pear tree output detection system based on electronic recognition, before being arranged at individual plant pear tree, it is characterized in that, described detection system comprises CCD vision sensor, sharpening processor, one-sided output recognizer and flush bonding processor, described CCD vision sensor is used for carrying out image acquisition to obtain one-sided pear tree image to the side of individual plant pear tree, described sharpening processor is used for carrying out the process of mist elimination haze to described single pear tree image of surveying, to obtain the one-sided pear tree image of mist elimination haze, described one-sided output recognizer is used for carrying out image recognition to the one-sided pear tree image of described mist elimination haze, to obtain the one-sided fruit number of pear tree, described flush bonding processor is connected with described one-sided output recognizer, for determining whole output of described individual plant pear tree based on the one-sided fruit number of described pear tree.
2., as claimed in claim 1 based on the individual plant pear tree output detection system of electronic recognition, it is characterized in that, described detection system also comprises:
Power supply, comprise solar powered device, accumulator, change-over switch and electric pressure converter, described change-over switch is connected respectively with described solar powered device and described accumulator, determine whether be switched to described solar powered device to be powered by described solar powered device according to accumulator dump energy, described electric pressure converter is connected with described change-over switch, with the 5V voltage transitions will inputted by change-over switch for 3.3V voltage;
Portable hard drive, for prestoring Pear Fruit upper limit gray threshold, Pear Fruit lower limit gray threshold and estimation multiplying power, described Pear Fruit upper limit gray threshold and described Pear Fruit lower limit gray threshold are used for the Pear Fruit in image to be separated with image background, and described estimation multiplying power is used for based on the whole output of pear tree one-sided output estimation pear tree;
Display device, is connected with described flush bonding processor, for showing the Word message corresponding with whole output of described individual plant pear tree;
Described CCD vision sensor is used for taking the front of described individual plant pear tree, to obtain described one-sided pear tree image;
Described sharpening processor comprises:
Store sub-device, for prestoring sky upper limit gray threshold and sky lower limit gray threshold, described sky upper limit gray threshold and described sky lower limit gray threshold are for separating of the sky areas of publishing picture in picture, also for prestoring presetted pixel value threshold value, described presetted pixel value threshold value value is between 0 to 255;
The sub-device of haze Concentration Testing, is arranged in air, for detecting the haze concentration of individual plant pear tree position in real time, and removes intensity according to haze concentration determination haze, and described haze removes intensity value between 0 to 1;
The sub-device of Region dividing, connect described CCD vision sensor to receive described one-sided pear tree image, gray processing process is carried out to obtain gray processing area image to described one-sided pear tree image, also be connected with storage subset, the pixel identification of gray-scale value in described gray processing area image between described sky upper limit gray threshold and described sky lower limit gray threshold is formed gray processing sky sub pattern, described gray processing sky sub pattern is partitioned into obtain the non-sky subimage of gray processing from described gray processing area image, the colour non-sky subimage corresponding with described gray processing non-sky subimage is obtained based on the correspondence position of described gray processing non-sky subimage in described beat image,
Black channel obtains subset, be connected with described Region dividing subset to obtain the non-sky subimage of described colour, for each pixel in the non-sky subimage of described colour, calculate its R, G, B tri-Color Channel pixel value, the R of all pixels in described colour non-sky subimage, G, B tri-extracts the Color Channel at the minimum Color Channel pixel value place of numerical value in Color Channel pixel value as black channel;
Overall air light value obtains subset, be connected to obtain presetted pixel value threshold value with described storage subset, obtain subset with described Region dividing subset and described black channel to be connected respectively to obtain described one-sided pear tree image and described black channel, multiple pixels that black channel pixel value in described one-sided pear tree image is more than or equal to presetted pixel value threshold value are formed set of pixels to be tested, the gray-scale value air light value as a whole of the pixel of maximum gradation value will be had in described set of pixels to be tested;
Atmospheric scattering light value obtains subset, be connected respectively with described Region dividing subset and described haze Concentration Testing subset, to each pixel of described one-sided pear tree image, extract its R, G, in B tri-Color Channel pixel value, minimum value is as target pixel value, use and keep the Gaussian filter EPGF at edge to carry out filtering process to obtain filtered target pixel value to described target pixel value, target pixel value is deducted filtered target pixel value to obtain object pixel difference, EPGF is used to carry out filtering process to obtain filtered target pixel value difference to object pixel difference, filtered target pixel value is deducted filtered target pixel value difference and remove reference value to obtain haze, haze is removed intensity and be multiplied by haze removal reference value to obtain haze removal threshold value, get haze and remove minimum value in threshold value and target pixel value as comparison reference, get the atmospheric scattering light value of the maximal value in comparison reference and 0 as each pixel,
Medium transmission rate obtains subset, obtain subset and described atmospheric scattering light value with described overall air light value to obtain subset and be connected respectively, the atmospheric scattering light value of each pixel is removed value divided by overall air light value to obtain, deducts 1 described except value is to obtain the medium transmission rate of each pixel;
Sharpening Image Acquisition subset, with described Region dividing subset, described overall air light value obtains subset and is connected respectively with described medium transmission rate acquisition subset, the medium transmission rate of each pixel is deducted to obtain the first difference by 1, described first difference is multiplied by overall air light value to obtain product value, the pixel value of each pixel in described one-sided pear tree image is deducted described product value to obtain the second difference, by described second difference divided by the medium transmission rate of each pixel to obtain the sharpening pixel value of each pixel, in described one-sided pear tree image, the pixel value of each pixel comprises the R of each pixel in described one-sided pear tree image, G, B tri-Color Channel pixel value, correspondingly, the sharpening pixel value of each pixel obtained comprises the R of each pixel, G, B tri-Color Channel sharpening pixel value, the one-sided pear tree image of sharpening pixel value composition mist elimination haze of all pixels,
Described one-sided output recognizer is connected with described sharpening processor and described portable hard drive respectively, and described one-sided output recognizer comprises:
The sub-device of contrast strengthen, is connected to obtain the one-sided pear tree image of mist elimination haze with described sharpening processor, perform contrast enhancement processing, to obtain enhancing image to the one-sided pear tree image of described mist elimination haze;
The sub-device of wavelet filtering, is connected with the sub-device of described contrast strengthen, performs wavelet filtering process, to obtain filtering image to described enhancing image;
The sub-device of gray processing process, is connected with the sub-device of described wavelet filtering, performs gray processing process, to obtain gray level image to described filtering image;
The sub-device of image recognition, be connected respectively with the sub-device of described gray processing process and described portable hard drive, the pixel identification of gray-scale value in described gray level image between described Pear Fruit upper limit gray threshold and described Pear Fruit lower limit gray threshold is formed multiple Pear Fruit subimage, using the sum of multiple Pear Fruit subimage as the one-sided fruit number of pear tree;
Described flush bonding processor is connected respectively with described portable hard drive, described one-sided output recognizer, calculates the one-sided fruit number of pear tree and the product estimating multiplying power, using the whole output of described product as described individual plant pear tree.
3., as claimed in claim 2 based on the individual plant pear tree output detection system of electronic recognition, it is characterized in that:
The resources occupation rate of described flush bonding processor calculating self, when the resources occupation rate of self is less than the first preset percentage, substitutes the operation of described one-sided output recognizer.
4., as claimed in claim 3 based on the individual plant pear tree output detection system of electronic recognition, it is characterized in that:
Described flush bonding processor, when the resources occupation rate of self is greater than the second preset percentage, terminates substituting the operation of described one-sided output recognizer.
5., as claimed in claim 4 based on the individual plant pear tree output detection system of electronic recognition, it is characterized in that:
Described first preset percentage and described second preset percentage are pre-stored in described portable hard drive, and described first preset percentage is less than described second preset percentage.
6., as claimed in claim 2 based on the individual plant pear tree output detection system of electronic recognition, it is characterized in that:
Described flush bonding processor and described one-sided output recognizer are integrated on one piece of surface-mounted integrated circuit.
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| CN201510178864.7A CN104794712B (en) | 2015-04-16 | 2015-04-16 | Individual plant pear tree yield detecting system based on electronic recognition |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104999200A (en) * | 2015-08-04 | 2015-10-28 | 李勇妹 | Diver portable system |
| CN110222677A (en) * | 2019-07-04 | 2019-09-10 | 苏州经贸职业技术学院 | A kind of loquat output statistics system based on raspberry pie |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109447945B (en) * | 2018-09-21 | 2021-11-30 | 河南农业大学 | Quick counting method for basic wheat seedlings based on machine vision and graphic processing |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201600330U (en) * | 2009-09-23 | 2010-10-06 | 中国农业大学 | System for recognizing and locating mature pineapples |
| WO2012116339A1 (en) * | 2011-02-24 | 2012-08-30 | Olive Medical Corporation | Imaging sensor providing improved visualization for surgical scopes |
| CN103065285A (en) * | 2012-12-30 | 2013-04-24 | 信帧电子技术（北京）有限公司 | Defogging method and device for image data |
| CN104182943A (en) * | 2014-08-27 | 2014-12-03 | 湖南大学 | Single image haze removal method combined with human vision characteristic |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103049888A (en) * | 2012-12-07 | 2013-04-17 | 西安电子科技大学 | Image/video demisting method based on combination of dark primary color of atmospheric scattered light |
| CN104360669B (en) * | 2014-11-27 | 2016-09-28 | 江苏华艺服饰有限公司 | A kind of spinning and weaving workshop environment control method |
| CN104474714B (en) * | 2014-11-27 | 2016-08-31 | 海安索菲亚生态环境新材料科技有限公司 | Roller-coaster passenger status real-time detecting system |
-
2015
- 2015-04-16 CN CN201510178864.7A patent/CN104794712B/en active Active
- 2015-04-16 CN CN201710448181.8A patent/CN107169940B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201600330U (en) * | 2009-09-23 | 2010-10-06 | 中国农业大学 | System for recognizing and locating mature pineapples |
| WO2012116339A1 (en) * | 2011-02-24 | 2012-08-30 | Olive Medical Corporation | Imaging sensor providing improved visualization for surgical scopes |
| CN103065285A (en) * | 2012-12-30 | 2013-04-24 | 信帧电子技术（北京）有限公司 | Defogging method and device for image data |
| CN104182943A (en) * | 2014-08-27 | 2014-12-03 | 湖南大学 | Single image haze removal method combined with human vision characteristic |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104999200A (en) * | 2015-08-04 | 2015-10-28 | 李勇妹 | Diver portable system |
| CN104999200B (en) * | 2015-08-04 | 2016-08-24 | 青岛青知企业管理咨询有限公司 | Diver portable system |
| CN110222677A (en) * | 2019-07-04 | 2019-09-10 | 苏州经贸职业技术学院 | A kind of loquat output statistics system based on raspberry pie |
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| CN107169940A8 (en) | 2019-04-05 |
| CN104794712B (en) | 2017-12-01 |
| CN107169940A (en) | 2017-09-15 |
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