CN102564593B - Plant growth condition monitoring system based on compute vision and internet of things - Google Patents
Plant growth condition monitoring system based on compute vision and internet of things Download PDFInfo
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Abstract
The invention discloses a plant growth condition monitoring system based on compute vision and internet of things. The plant growth condition monitoring system is characterized by comprising a high speed digital signal processor (DSP) computer vision module for shooting plant leaves and acquiring and analyzing the shot images, a control execution module for watering plants, and an internet of things module for transmitting the plant leaf information to a remote terminal through a remote server and providing the information for workers to consult and operate, wherein the high speed DSP computer vision module, the control execution module and the internet of things module are connected through data. The traditional detection system is improved, a computer vision-based DSP image processing technology and an improved processing algorithm are used for acquiring and processing leaf images of luxurious flowers and trees, the image acquiring and processing speed and accuracy are improved, theautomation level of the system is improved and the production cost is reduced; and the acquired data is timely transmitted to the workers at the terminal through the remote server by the internet of things technology.
Description
Technical field
The present invention relates to a kind of vegetation growth state monitoring system based on computer vision and Internet of Things and utilize this system to carry out the method realization that the famous flower and tree blade detects, belong to digital image processing techniques in the application of agriculture detection range.
Background technology
The blade of plant is the important photosynthetic organ of plant, also is the main path that plant carries out transpiration.Photosynthesis, i.e. luminous energy synthesis, be the blade of plant under visible light radiation, utilize photosynthetic pigments, carbon dioxide (or sulfuretted hydrogen) and water are transformed into organism, and discharge the biochemical process of oxygen (or hydrogen).Photosynthesis is the summation of the metabolic response of a series of complexity, is the basis that whole organic sphere is depended on for existence, also is the important media of earth carbon oxygen cycle.
We are not difficult to find, in today that precision agriculture constantly develops, carry out the precision agriculture practice and not only can make rational use of resources, improve irrigation quality, and can reduce production costs, protect environment, raising crop competitive power.Meanwhile, the various parameters of research plant leaf blade are for growth and development of plant, irrigable culture, and crop yield etc. all has very important meaning.Make up real-time, quick, effective plant leaf blade analytical approach, for adjusting group structure, taking full advantage of the photo-thermal resource, all have important practical significance thereby instruct arable farming density and the rational application of fertilizer to irrigate.
Traditional vegetation growth state monitoring method too relies on manually-operated, needs flowers and trees cultivation personnel deeply to plant booth inside and carries out information acquisition and fertigation, so monitoring inefficiency and cost are higher.Along with the continuous development of computer technology in recent years, the continuous maturation of digital image processing techniques can consider to obtain and analyze by computer vision the external appearance characteristic of flowers and trees blade.Computer vision is to use computing machine and relevant device to a kind of simulation of biological vision, and its main task obtains corresponding information by gathering picture or video exactly, thereby it is analyzed and handles.At present, be that the based computer vision detection technology has been applied to the various aspects in the social production with digital image processing techniques, as recognition of face, vehicle identification, industrial detection and medical image analysis etc., its distinctive unartificial property, accuracy and stability, and to the adaptability of environmental change, be that other traditional detection methods are difficult to match in excellence or beauty.
Can the quality of image processing algorithm be the computer vision technique key in application of succeeing.Compare with otherwise application, the color of flowers and trees blade to be detected, texture, shape and size all are its important external appearance characteristics.But the growing environment of plant causes image-forming condition undesirable greatly because of illumination variation, and blade can not have regular as industrial part in the outward appearance performance and can be descriptive simultaneously, so brought great difficulty for the identification of objectives.Since the last century the nineties, a lot of scholars are utilizing computer vision technique that the direction that plant leaf blade detects analysis has been launched research: [Ji Shouwen, Wang Rongben, Chen Jiajuan, Deng. the research [J] of appliance computer image processing techniques identification corn weeds in field in seedling stage. Transactions of the Chinese Society of Agricultural Engineering, 2001,17 (2): 201-204] utilize image processing techniques to obtain shape description parameters such as projected area of blade, leaf length, Ye Kuan in the literary composition, corn and weeds to seedling stage identify, thereby provide effective foundation for accurate spraying herbicide; [Mao Hanping, Xu Guili, Li Pingping, Deng. the identification [J] that wanes based on the tomato nutrient element of computer vision. Transactions of the Chinese Society of Agricultural Engineering, 2003,34 (2): 73-75] in the literary composition by the carry out feature extraction of computer vision to tomato leaf, set up the framework that the tomato nutritional deficiency symptom is carried out pattern-recognition by the binary tree sort method, thereby tomato nitrogen stress and potassium deficiency initial stage blade that naked eyes be difficult for to be differentiated identified; [Singh N., Casady W.W., Costello T.A.Machine-vision-based Nitrogen management models for rice[J] .Transaction of the ASAE.1996,39 (5): 1899-1904] use image to cut apart to obtain the morphological feature of paddy rice in the literary composition, and combine with the vegetation growth state analysis, invented a kind of method of utilizing morphological features such as blade area to judge the N nutritional deficiency situation of plant.
Internet of Things is considered to the information technology revolution again after computing machine, Internet technology as an emerging in recent years infotech.Technology of Internet of things makes people can understand the information of all environment, equipment, facility in the reality whenever and wherever possible by by computer internet, sensor and terminal device, has broken away from the restriction of time and region.Internet of Things is of many uses, has spreaded all over numerous areas such as intelligent transportation, environmental protection, public safety, personal health, arable farming and industry monitoring at present.The widespread use of technology of Internet of things in agricultural is expected to bring for agricultural the change of epoch-making significance: people not only can understand growing environment and the growing way situation of crop by online, and can make the arid situation of crop and disease and pest situation and judge and in time take appropriate measures.The agricultural technology of Internet of things also has huge development prospect in modern famous flower and tree growth monitoring.As everyone knows, the upgrowth situation of plant and illumination condition, environment temperature and factors such as humidity, soil moisture content are all closely related.All the time, the flowers and trees industry can only depend on the experience management owing to lack accurate measurement means.This extensive way to manage has greatly restricted the raising of flowers and trees growth qualities, uses agriculture technology of Internet of things then can improve the efficient of famous flower and tree industry in producing and managing significantly.
China is in that to utilize computer vision to carry out the technology ability that plant detects aspect analysis at the early-stage.The flow process of general computer vision system detection system is: adopt CCD camera collection image, subsequently it is sent into and carry out the A/D conversion in the data collecting card, call corresponding application program by operating system and these data are handled extracted feature thereby data enter behind the computing machine, finish the detection task to target.But because the data transmission of this type systematic need expend a large amount of clock period, and general computing machine does not adopt the hardware configuration of specialty that data are handled to be optimized, cause algorithm consuming time more, greatly reduced real-time and the detection efficiency of this system.Therefore needing to adopt the high-speed dsp chip of handling at image specially to come substituting for computer to carry out data handles.Optimization process algorithm how, seeking simple detection method fast is to improve the key of system availability.
Summary of the invention
The present invention is directed to the demand that flowers and trees leaf growth situation detects automatically, proposed a kind of vegetation growth state monitoring system based on computer vision and Internet of Things.This system's utilization is carried out image acquisition and processing at the high-speed dsp computer vision module of digital picture, high-speed dsp chip in the module is as a kind of special microprocessor, its distinctive Harvard structure and stream line operation can make it that treatment of picture arithmetic speed is far surpassed general micro controller, have improved the real-time that detects greatly; On the other hand, by technology of Internet of things is combined with the blade detection technique, not only can realize the real-time monitoring of famous flower and tree upgrowth situation and irrigation in time, more relevant information can be conveyed to the staff that flowers and trees are cultivated enterprise by remote server and terminal device, thereby reach the purpose of Remote, make things convenient for them in time the famous flower and tree in the booth to be irrigated.
Technical scheme of the present invention provides a kind of vegetation growth state monitoring system based on computer vision and Internet of Things, it is characterized in that: the control execution module that it comprises high-speed dsp computer vision module that plant leaf blade is taken and taken image is gathered and analyzed, plant is watered, and sending plant leaf blade information to remote terminal for the Internet of Things module of staff's reference and operation by remote server, data connect between described high-speed dsp computer vision module, control execution module and the Internet of Things module.
Preferably, described high-speed dsp computer vision module comprises the industrial camera that is positioned at directly over the plant to be monitored, is used for the DSP image processing module that obtains the ccd image acquisition module of the taken image of described industrial camera and include the high-speed dsp chip TMS320C6000 that handles for image.
Preferably, described industrial camera is wrapped with one deck waterproof membrane all over the body, and described industrial camera is other to be provided with lighting source, and described lighting source is led light source.
Preferably, described ccd image acquisition module and DSP image processing module all are loaded on the portable dsp board card of encapsulation type.
Preferably, described control execution module comprises: a plurality of sprinkling irrigation shower nozzles that are positioned at plant to be monitored top and the aqueduct that supplies water for described sprinkling irrigation shower nozzle, described sprinkling irrigation shower nozzle is controlled by strong power controller, and described strong power controller all is connected industrial computer with described high-speed dsp computer vision module, Internet of Things module.
Preferably, the step of its work is:
1) described industrial camera is regularly taken the coloured image of the blade of plant to be monitored;
2) described ccd image acquisition module sends described DSP image processing module to after obtaining described coloured image;
3) described DSP image processing module detects the plant leaf blade in the described coloured image, if find plant hydropenia then notify described control execution module that plant is watered.
Preferably, in the described step 3), described DSP image processing module carries out in the following detection one or more to the plant leaf blade in the described coloured image: leaf color detects, the blade texture detects and blade shape detects.
Preferably, the step of described leaf color detection is:
1) plant leaf blade in the described coloured image being removed petiole handles;
2) with described coloured image by the RGB color space conversion to the hsv color space, set H component and S component variation scope and the two divided equally;
3) create the two-dimensional histogram that each dimension is divided equally according to the data statistics on H plane and S plane;
4) utilize former described coloured image described two-dimensional histogram to be converted to the color 2 D histogram of the rgb space of blade;
5) setting threshold by the flowers and trees blade in the described color 2 D histogram is analyzed, determines plant and whether belongs to exsiccosis.
Preferably, the step of described blade texture detection is:
1) with described coloured image gray processing and adopt mean filter;
2) set dynamic threshold at different types of flowers and trees blade, use the edge in the detection of dual threshold algorithm and the connection layout picture;
3) the texture quantity of blade unit area is added up, judge by the density degree of texture whether plant belongs to exsiccosis.
Preferably, described blade shape detects and comprises that blade shape parameter detecting and blade area detect, and the step of wherein said blade shape parameter detecting is:
1) plant leaf blade in the described coloured image being removed petiole handles;
2) with the gray level image binaryzation of the plant leaf blade in the described coloured image, and extract the profile of plant leaf blade;
3) mean value of the pixel coordinate of the profile by calculate extracting plant leaf blade obtains blade centre of form point coordinate, and is that the maximum circumradius of blade and minimum inscribed circle radius are asked in the center of circle with the centroid point, with the two ratio as the blade shape parameter;
4) compare by the blade shape parameter that calculates and the blade shape parameter under the normal condition, judge whether plant belongs to exsiccosis.
Preferably, the step of described blade area detection is:
1) with the gray level image binaryzation of the plant leaf blade in the described coloured image, obtains binary image;
2) the described binary image of traversal is added up the sum that all gray-scale values are all pixels of 0, with its blade area as plant leaf blade;
3) historical variations of the blade area of this plant leaf blade relatively judges that if blade area diminishes this plant leaf blade is for belonging to exsiccosis.
Preferably, wherein the plant leaf blade in the described coloured image being removed the step that petiole handles comprises:
1) at first will comprise the leaf image binaryzation of petiole, and with its rotate to be blade upwards, petiole position down, to obtain bianry image;
2) described bianry image is carried out transversal scanning from bottom to top, to determine the breadth extreme of petiole part;
3) by to the continuously transversal scanning of black pixel point and number statistics in the bianry image, with less than step 2) in the part of the petiole part breadth extreme determined be petiole, thereby determine the definite position of petiole;
4) with the petiole image inverse that obtains, itself and former described coloured image are carried out or operate, obtained rejecting the blade coloured image of petiole.
The present invention is scientific and reasonable, taken into full account the real-time that detects, and traditional detection system improved, use the leaf image that obtains and handle famous flower and tree based on DSP image processing techniques and the Processing Algorithm after the improvement of computer vision, improved speed and the accuracy rate of Image Acquisition and processing.After detecting the plant that need water, can utilize industrial computer to control the sprinkling irrigation shower nozzle and finish corresponding operation, improved the automatization level of system, reduced production cost.In addition, technology of Internet of things has been introduced the analysis monitoring system of plant leaf blade.Send the data of collecting to the terminal works personnel in time by remote server, broken away from the restriction on traditional flowers and trees breeding method time and the region, be very easy to the staff to the remote monitoring of flowers and trees.When the staff need water to the flowers and trees in the booth, only need in office, send instruction by PC or smart mobile phone Remote industrial computer and get final product.Should quite good detecting real-time and stronger stability, accuracy be arranged based on vegetation growth state monitoring system and the method for computer vision and Internet of Things, also support Remote simultaneously, cultivate enterprise's use so be fit to very much famous flower and tree.
Description of drawings
Fig. 1 is the system architecture synoptic diagram of the vegetation growth state monitoring system based on computer vision and Internet of Things of the present invention;
Fig. 2 is the system algorithm schematic flow sheet of the vegetation growth state monitoring system based on computer vision and Internet of Things of the present invention;
Among Fig. 1: 1.LED light source (100w), 2. industrial camera, 3. high-speed dsp chip TMS320C6000,4. aqueduct, 5. sprinkling irrigation shower nozzle, 6. strong power controller, 7. industrial computer, 8. remote server, 9. PC, 10. smart mobile phone.
Embodiment
Below the specific embodiment of the present invention is described in further detail.
As depicted in figs. 1 and 2, the hardware components of a kind of vegetation growth state monitoring system based on computer vision and Internet of Things of the present invention comprises: the high-speed dsp computer vision module that the flowers and trees blade is taken and taken image is gathered and analyzed, the control execution module that the lack of water flowers and trees are watered, and send blade information to terminal for three parts of Internet of Things module of staff's reference and operation by remote server:
One, high-speed dsp computer vision module comprises industrial camera, special-purpose ccd image acquisition module and DSP image processing module.Industrial camera is positioned at about 1 meter directly over the flowers and trees to be detected, and the camera next door is furnished with the led light source of 100w as illumination, and camera is enclosed with one deck waterproof membrane all over the body.Special-purpose ccd image acquisition module is used for obtaining by the taken image of industrial camera, then contain in the DSP image processing module and be specifically designed to the high-speed dsp chip TMS320C6000 that image is handled, the two all is loaded on the dsp board card, and integrated circuit board is that encapsulation type is portable.
Two, the control execution module comprises industrial computer, strong power controller, aqueduct and sprinkling irrigation shower nozzle.Industrial computer is placed on flowers and trees and cultivates in the booth, directly links to each other with high-speed dsp computer vision module by pci interface, and controls the sprinkling irrigation shower nozzle by strong power controller and water.Aqueduct is made by resistant material, is positioned at about 0.2 meter, dead ahead of plant.The sprinkling irrigation shower nozzle is placed on the aqueduct, can arrange one at a certain distance according to actual conditions, and main being responsible for sprayed to the flowers and trees of lack of water according to the signal that industrial computer transmits.
Three, the Internet of Things module comprises terminal devices such as remote server, PC and smart mobile phone.Remote server is arranged on flowers and trees and cultivates in enterprise's office block, it is the bridge block between flowers and trees monitoring point and the client terminal, the flowers and trees blade information that the interior industrial computer of booth transmits is collected and stored to main being responsible for, and is communicated with member in the net, and provides data, services to terminal device at any time.PC and smart mobile phone belong to terminal device, main being responsible for is shown to the staff that flowers and trees are cultivated enterprise with the plant information that server sends, can send instruction to the industrial computer of cultivating in the booth in real time simultaneously, make things convenient for them that the upgrowth situation of famous flower and tree is carried out remote monitoring.
Vegetation growth state monitoring system based on computer vision and Internet of Things of the present invention is as follows to the specific implementation step that blade detects:
Industrial camera photographs a color image frame of current flowers and trees blade, and view data is obtained by special-purpose ccd image acquisition module, carries out the blade testing by the DSP image processing module afterwards.Testing mainly comprises: leaf color detects, and the blade texture detects and blade shape detects three parts.In a single day find that the flowers and trees blade is in poor shape in the whole detection link, the lack of water phenomenon is arranged, just will produce trigger pip, industrial computer is analyzed this signal and is controlled the sprinkling irrigation shower nozzle by strong power controller and finish the task of watering subsequently.In addition, the staff that famous flower and tree is cultivated enterprise can obtain plant leaf blade information in real time by PC and smart mobile phone, and the industrial computer in the Long-distance Control booth sends the sprinkling irrigation instruction.Wherein:
One, after high-speed dsp computer vision module gets access to the coloured image of the frame flowers and trees blade that the current industrial camera photographs, through after the analog to digital conversion with image data storage in the image acquisition data storage area of high-speed dsp computer vision module.
What two, at first will carry out is the image pre-service.With the new coloured image gray processing of taking of industrial camera, then adopt the method for gray scale nonlinear transformation that its degree of comparing is stretched, again by the salt-pepper noise in the medium filtering filtering image.Use adaptive algorithm that gray level image is carried out threshold value then and cut apart, blade (comprising petiole) part is set to black and background is set to white.Again itself and initial coloured image are carried out the blade coloured image that OR operation can obtain wiping out background and comprise petiole at last.
Three, because the phyllome picture that industrial camera obtains comprises petiole mostly, brought difficulty for detection analysis subsequently.The present invention proposes a kind of petiole elimination method based on the transversal scanning technology: at first will comprise the leaf image binaryzation of petiole, and with its rotate to be blade upwards, petiole position down.Next utilize transversal scanning to determine the breadth extreme of petiole part, and then by to the transversal scanning of black pixel point and the definite position that number adds up to determine petiole continuously in the bianry image.With isolated petiole image inverse, itself and the blade coloured image that comprises petiole are carried out OR operation, the blade coloured image that can obtain rejecting petiole and have complete display leaf margin information at last.
Four, carry out the color detection of blade below.Here at first with the blade coloured image that obtains in the step 3 by the RGB color space conversion to the hsv color space, set the variation range of H component and S component and the two realization divided equally.Then create the two-dimensional histogram that each dimension is divided equally according to the data statistics on H plane and S plane, finally be transformed into the color 2 D histogram that rgb space is drawn the blade coloured image.By the color 2 D histogram of blade the flowers and trees blade under the different conditions is analyzed at last, whether belonged to exsiccosis thereby setting threshold determines plant.
Five, the texture that next will carry out blade detects.The detection effect of blade textural characteristics is closely related with factors such as floristics, illumination condition and industrial camera resolution, so the present invention is provided with fixing led light source, and at common flowers and trees blade, used a kind of Canny operator detection method based on dynamic threshold.This method is at first with blade coloured image gray processing and adopt mean filter, to reduce the influence to subsequent algorithm.Then set dynamic threshold at different types of flowers and trees blade, and use the edge in the detection of dual threshold algorithm and the connection layout picture.At last the texture quantity of blade unit area is added up, judge whether lack of water of plant by the density degree of texture.
What six, will carry out at last is the SHAPE DETECTION of blade, comprises that blade shape parameter detecting and blade area detect two parts.At first realize the detection of blade shape parameter: the blade gray level image binaryzation to obtaining in the step 5 at first, and make profile and extract.Then obtain blade centre of form point coordinate by the mean value that calculates the contour pixel coordinate, and be maximum circumradius and minimum inscribed circle radius that blade is asked in the center of circle with the centroid point, with the ratio value defined blade shape parameter of the two.At last at different time point determinings and calculate the form parameter of blade, by with normal condition under the blade shape parameter relatively judge whether lack of water of plant.
Seven, blade area also is the important parameter of reflection vegetation growth state, degree of water shortage.Flowers and trees blade area detection method in the native system is different from the area detection method of General System.Because the blade real area is closely related with factors such as object of reference size, camera shooting angle and distances, so be difficult to obtain the accurate area of blade.So used a kind of blade area analytical approach based on pixel among the present invention, determined the reference point of plant hydropenia feature by the variation of comparing the flowers and trees blade area.
Eight, be combined in detected leaf color distribution, texture density degree, form parameter and blade area change to judge whether flowers and trees are in exsiccosis in the step 4, five, six, seven, control the sprinkling irrigation shower nozzle by industrial computer simultaneously it is watered.And for the leaf color that has obtained in the above-mentioned steps, texture, shape and area information, then send to terminal device in time by remote server, realized the Internet of Things remote monitoring that famous flower and tree is cultivated.
In conjunction with example specifically:
As shown in fig. 1, famous flower and tree is cultivated in the booth, is provided with led light source 1 and industrial camera 2 directly over the flowers and trees blade to be detected.This moment, industrial camera photographed the coloured image of a frame flowers and trees blade, view data is obtained by the ccd image acquisition module in the high-speed dsp chip 2 simultaneously, and these data are passed to DSP image processing module in the chip, the leaf image data that photograph are carried out high speed processing, thus the result of obtaining.After detection was finished, the high-speed dsp chip sent the view data result to industrial computer 7 by its built-in pci interface.Industrial computer sends processing signals to strong power controller 6 by built-in I/O interface on the one hand, and strong power controller will be according to the processing signals of receiving, 5 pairs of qualified flowers and trees of blade of sprinkling irrigation shower nozzle that control is placed on the aqueduct 4 are watered; On the other hand, industrial computer sends the plant leaf blade information that collects to famous flower and tree and cultivates remote server 8 in enterprise's office building, by server the blade data are collected and stored, and provide data, services to terminal device PC 9 and smart mobile phone 10 in real time, make things convenient for flowers and trees to cultivate the enterprise work personnel and carry out remote monitoring, and can be at any time industrial computer in the booth send instruction, control sprinkling irrigation shower nozzle is finished the task of watering.System algorithm flow process of the present invention as shown in Figure 2, its concrete steps comprise:
One, the high-speed dsp vision module gets access to the coloured image I of the current taken frame flowers and trees blade of industrial camera
1, through after the analog to digital conversion, deposit in the image acquisition data storage area in this module.
What two, at first will carry out is the image pre-service.The flowers and trees blade coloured image I that industrial camera is newly photographed
1Gray processing, the method for then utilizing the gray scale nonlinear transformation stretches to its degree of comparing, and can give prominence to the leaf characteristic in the gray level image like this, and nonlinear function uses exponential function to finish, and comes salt-pepper noise in the filtering gray level image by medium filtering again.Use adaptive algorithm that gray level image is carried out threshold value then and cut apart, blade (comprising petiole) part is set to black and background is set to white, can obtain to comprise the blade bianry image I2 of petiole.Again itself and initial coloured image are carried out the blade coloured image I3 that OR operation can obtain wiping out background and comprise petiole at last.
Three, because the phyllome picture that industrial camera obtains comprises petiole mostly, brought difficulty for analytical work subsequently.The method of petiole generally all is the opening operation that adopts in the morphology processing in the rejecting image of Shi Yonging in the past, namely earlier it is carried out erosion operation, carries out dilation operation again and finishes.This method not only be difficult to realize separating fully of plant leaf blade and petiole in actual mechanical process, and the sawtooth information of blade edge is also had in various degree damage.At the problem that occurs in the said method, the present invention proposes a kind of petiole elimination method based on the transversal scanning technology: at first the blade bianry image I of petiole will be obtained in the step 2 comprising
2Rotate to blade up, petiole position vertically downward, from the image bottom, from down to up, scanning element point one by one from left to right, the row number note that first black pixel point place that scans is walked crosswise is made i, i.e. first pixel of petiole.Then at bianry image I
2[i-19, i] amount to the black pixel point numbers in the every row of statistics in 20 row, draw petiole width maximal value max by the mode that compares one by one.The bianry image of lining by line scan from top to bottom, from left to right then is petiole with the continuous black picture element location determination that is less than or equal to max of counting out.Here must emphasize it is the number of " continuously " pixel, because the blade of a lot of plant leaf blade images and petiole have situation about being distributed in same the walking crosswise, mistake just occurs if the mode that adopts statistics to walk crosswise interior black pixel point sum is judged, causing part to walk crosswise interior petiole can't be identified.Also need at last the petiole that has identified separated with inverse and obtain petiole bianry image I
4Again with itself and the blade coloured image I that comprises petiole
3Carry out OR operation, the blade coloured image I that can obtain rejecting petiole and have complete display leaf margin information
5The designed this separation method of the present invention has all produced good effect through repeatedly practice to various types of plant leaf blade images.
Four, the following leaf color that at first carries out detects.Color detection is the important component part of foliar analysis, lack of water or sunshine deficiency the ability of plant synthesize chlorophyll (chl) be severely limited, cause that leaf green content presents negative growth on the blade, the pigment of other colors just displays gradually, occurs so visually just have the blade of colors such as yellow, brownish red.To rejecting the blade coloured image I of petiole
5Handle, need to draw its color histogram.At first with its by the RGB color space conversion to the hsv color space; Set H component variation scope (0,180), S component variation scope (0,255) is divided into 16 grades with the H component, and the S component is divided into 8 grades.Create the two-dimensional histogram that each dimension is divided equally according to the data statistics on H plane and S plane then, obtain the color that current histogram represents by initial pictures, be transformed into the color 2 D histogram that rgb space is drawn leaf image.Analyze the lack of water situation of plant leaf blade at last by the pixels statistics mean value that calculates color histogram.The present invention here analyzes at the fruit taro blade coloured image that closes of different conditions, and concrete statistics is as shown in table 1.Listed respectively in the table 1 be in new life, normal growth, slightly lack of water, extremely close the assembly average of fruit taro leaf image pixel in the RGB passage under lack of water and the withered state.Be not difficult to find that G passage and R passage average present the trend of successively decreasing and increasing progressively respectively by data in the analytical table, and the variation of B passage is less relatively.Might as well setting threshold T[G]=148 and T[R]=120, when closing the average of fruit taro blade at G passage and the R passage G＜T[G that can satisfy condition simultaneously] and R＞T[R] time judge that it is in exsiccosis, the control shower nozzle of spraying waters then.
Table 1 plant leaf blade colouring information and vegetation growth state relation
What five, next will carry out is that the blade texture detects.The detection of texture is the important component part of foliar analysis equally, and leaf curling appears in plant under the lack of water condition easily, vein is gathered and phenomenon such as jaundice, so can realize the analysis to the plant hydropenia situation by the detection to the blade textural characteristics.Here mainly be at the main texture number in the leaf image unit area: at first to rejecting the blade coloured image I of petiole
5Gray processing then carries out the blade gray level image I after mean filter obtains smoothly
6Use a kind of Canny operator detection method based on dynamic threshold then, at different flowers and trees leaf images, by setting dynamic dual threshold T
1(the control edge connects) and T
2(controlling the initialization of strong edge) realizes the detection to the blade texture.Can make T generally speaking
1: T
2Can obtain comparatively desirable effect at=1: 3, for example: the threshold value T that closes fruit taro blade
1: T
2Can get 75: 225, begonia blade threshold value was generally got 88: 264, Liriodendron blade threshold value then get 69: 207 comparatively suitable.Single length is regarded as blade master texture greater than the situation of 500 pixels in the interior profile of image.To detect the image obtain be I if the blade gray level image is finished texture
7, use the unit template of 256 * 256 sizes this moment, statistics blade texture image I
7Main texture bar in the unit template is counted K.Count Aver[K by the main texture bar in the unit of account area again] analyze the density degree of blade texture, thus judge this moment, whether plant was in exsiccosis.Listed in the table 2 the present invention respectively unit area master's texture bar number of involutory fruit taro blade normal condition, exsiccosis and withered state get the testing result that obtains behind four position effective digitals.Can set decision threshold T[Aver[K this moment equally]]=3.5, then judge plant hydropenia during greater than this value when testing result, needs water to it at once.
Table 2 plant leaf blade texture information and vegetation growth state relation
What six, will carry out at last is the SHAPE DETECTION of blade, comprises that blade shape parameter detecting and blade area detect two parts.The flowers and trees blade under exsiccosis, usually present in the leaf margin turn over, vein is gathered and a series of features such as blade fold, and these features finally can show in the variation of blade shape, this also provides strong foundation for we judge the blade water shortage status.The present invention at first at the change of blade shape feature, realizes the detection to the blade shape parameter: at first with the flowers and trees blade gray level image I that obtains in the step 5
6Binaryzation obtains the desirable bianry image I of blade
7Then its scaling to 800 * 800 sizes are carried out profile and extract, obtain the contour images I8 of blade.Add up the coordinate figure of blade profile pixel then successively, calculate the mean value of profile coordinate again, be the centroid point coordinate P of flowers and trees blade
(x, y)Next find out the some B apart from the some A of maximum and distance minimum with centroid point P on blade profile respectively, then the length of AP is the maximum circumradius L of blade profile
1, the length of BP is the minimum inscribed circle radius L of blade profile
2Here by calculating L
1And L
2Ratio define blade shape parameter lambda: λ=L
1/ L
2
Parameter lambda can be used as the parameter of real-time reflection leaf growth situation, and can not be subjected to the influence of blade rotation, translation and scaled, but relevant with the kind of flowers and trees blade to be detected.The present invention will drip kwan-yin here as research object, by the kwan-yin that drips under the normal condition being statically placed in dry environment and detecting and calculate form parameter λ at different time point, two groups of GUANYINYE plate shape parameter lambda of dripping that under different conditions, detect have been listed in the table 3.Can reach a conclusion by analysis: the blade shape parameter lambda under the normal condition is in the 2.2-2.5 scope, and the form parameter λ of exsiccosis lower blade is generally more than 2.6, so can be as criterion, control sprinkling irrigation shower nozzle waters to the flowers and trees of lack of water.
Table 3 plant leaf blade form parameter and vegetation growth state relation
Seven, blade area also is the important parameter of reflection vegetation growth state, and the feature that blade area significantly reduces can appear in plant under the water shortage status.Flowers and trees blade area detection method in the native system is different from the area detection method of General System.Because the blade real area is closely related with factors such as object of reference size, camera shooting angle and distances, so be difficult to obtain the accurate area value of blade.Therefore the present invention has used the blade area assay method based on pixel: read in the desirable bianry image I of the blade that obtains in the step 6
7, and two initial value m are set and n is 0.Each pixel is a point in the coordinate system in the image, has two dimensional character.Begin to travel through the view picture bianry image from first point: if pixel (x, y) gray-scale value equals 255, illustrates that this pixel belongs to the pixel of background, and m is from increasing 1; If pixel (x, y) gray-scale value equals 0, illustrates that this pixel belongs to the pixel of blade, and n is from increasing 1.Finish up to traversal, can draw namely that the blade sum of all pixels is n in the bianry image, and the sum of all pixels of entire image is (m+n).Draw blade number of pixels function distribution plan according to the sum of all pixels of different period flowers and trees blades at last, judge that according to the remarkable minimizing of blade number of pixels blade is in exsiccosis.Here still 800 * 800 big small drops of water GUANYINYE pictures in the step 6 are carried out the statistics of blade area information as research object, listed two groups in the table 3 to resting on the result that the kwan-yin that drips in the dry environment carries out the blade area statistics.We may safely draw the conclusion by analysis: the blade sum of all pixels under the normal condition is between 388500-390500, and the blade sum of all pixels under the exsiccosis all is down to below 387500, so can be as criterion, control sprinkling irrigation shower nozzle waters to the flowers and trees of lack of water.
Table 4 plant blade area information and vegetation growth state relation
Eight, be combined in the leaf color distribution, blade shape parameter, blade texture density degree and the blade area that detect in the step 4, five, six, seven and change to judge whether flowers and trees are in exsiccosis, control the sprinkling irrigation shower nozzle by industrial computer it is watered.And for the leaf color that has obtained in the above-mentioned steps, texture, shape and area information, then send to terminal device in time by remote server, realized the Internet of Things remote monitoring that famous flower and tree is cultivated.Hereto, just be through with to the blade testing of a strain famous flower and tree.
Above embodiment only is the present invention's a kind of embodiment wherein, and it describes comparatively concrete and detailed, but can not therefore be interpreted as the restriction to claim of the present invention.Should be pointed out that for the person of ordinary skill of the art without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.
Claims (8)
1. based on the vegetation growth state monitoring system of computer vision and Internet of Things, it is characterized in that: the control execution module that it comprises high-speed dsp computer vision module that plant leaf blade is taken and taken image is gathered and analyzed, plant is watered, and sending plant leaf blade information to remote terminal for the Internet of Things module of staff's reference and operation by remote server, data connect between described high-speed dsp computer vision module, control execution module and the Internet of Things module;
Described high-speed dsp computer vision module comprises the industrial camera that is positioned at directly over the plant to be monitored, be used for the DSP image processing module that obtains the ccd image acquisition module of the taken image of described industrial camera and include the high-speed dsp chip of handling for image;
The step of its work is:
1) described industrial camera is regularly taken the coloured image of the blade of plant to be monitored;
2) described ccd image acquisition module sends described DSP image processing module to after obtaining described coloured image;
3) described DSP image processing module detects the plant leaf blade in the described coloured image, if find plant hydropenia then notify described control execution module that plant is watered;
In the described step 3), described DSP image processing module carries out in the following detection one or more to the plant leaf blade in the described coloured image: leaf color detects, the blade texture detects and blade shape detects;
The step that described leaf color detects is:
1) plant leaf blade in the described coloured image being removed petiole handles;
2) with described coloured image by the RGB color space conversion to the hsv color space, set H component and S component variation scope and the two divided equally;
3) create the two-dimensional histogram that each dimension is divided equally according to the data statistics on H plane and S plane;
4) utilize former described coloured image described two-dimensional histogram to be converted to the color 2 D histogram of the rgb space of blade;
5) setting threshold by the flowers and trees blade in the described color 2 D histogram is analyzed, determines plant and whether belongs to exsiccosis.
2. the vegetation growth state monitoring system based on computer vision and Internet of Things according to claim 1, it is characterized in that: described industrial camera is wrapped with one deck waterproof membrane all over the body, described industrial camera is other to be provided with lighting source, and described lighting source is led light source.
3. the vegetation growth state monitoring system based on computer vision and Internet of Things according to claim 1, it is characterized in that: described ccd image acquisition module and DSP image processing module all are loaded on the portable dsp board card of encapsulation type.
4. the vegetation growth state monitoring system based on computer vision and Internet of Things according to claim 1, it is characterized in that: described control execution module comprises: a plurality of sprinkling irrigation shower nozzles that are positioned at plant to be monitored top and the aqueduct that supplies water for described sprinkling irrigation shower nozzle, described sprinkling irrigation shower nozzle is controlled by strong power controller, and described strong power controller all is connected industrial computer with described high-speed dsp computer vision module, Internet of Things module.
5. the vegetation growth state monitoring system based on computer vision and Internet of Things according to claim 1 is characterized in that: the step that described blade texture detects is:
1) with described coloured image gray processing and adopt mean filter;
2) set dynamic threshold at different types of flowers and trees blade, use the edge in the detection of dual threshold algorithm and the connection layout picture;
3) the texture quantity of blade unit area is added up, judge by the density degree of texture whether plant belongs to exsiccosis.
6. the vegetation growth state monitoring system based on computer vision and Internet of Things according to claim 5, it is characterized in that: described blade shape detects and comprises that blade shape parameter detecting and blade area detect, and the step of wherein said blade shape parameter detecting is:
1) plant leaf blade in the described coloured image being removed petiole handles;
2) with the gray level image binaryzation of the plant leaf blade in the described coloured image, and extract the profile of plant leaf blade;
3) mean value of the pixel coordinate of the profile by calculate extracting plant leaf blade obtains blade centre of form point coordinate, and is that the maximum circumradius of blade and minimum inscribed circle radius are asked in the center of circle with the centroid point, with the two ratio as the blade shape parameter;
4) compare by the blade shape parameter that calculates and the blade shape parameter under the normal condition, judge whether plant belongs to exsiccosis.
7. the vegetation growth state monitoring system based on computer vision and Internet of Things according to claim 6 is characterized in that: the step that described blade area detects is:
1) with the gray level image binaryzation of the plant leaf blade in the described coloured image, obtains binary image;
2) the described binary image of traversal is added up the sum that all gray-scale values are all pixels of 0, with its blade area as plant leaf blade;
3) historical variations of the blade area of this plant leaf blade relatively judges that if blade area diminishes this plant leaf blade is for belonging to exsiccosis.
8. the vegetation growth state monitoring system based on computer vision and Internet of Things according to claim 6 is characterized in that: wherein the plant leaf blade in the described coloured image is removed the step that petiole handles and comprise:
1) at first will comprise the leaf image binaryzation of petiole, and with its rotate to be blade upwards, petiole position down, to obtain bianry image;
2) described bianry image is carried out transversal scanning from bottom to top, to determine the breadth extreme of petiole part;
3) by to the continuously transversal scanning of black pixel point and number statistics in the bianry image, with less than step 2) in the part of the petiole part breadth extreme determined be petiole, thereby determine the definite position of petiole;
4) with the petiole image inverse that obtains, itself and former described coloured image are carried out or operate, obtained rejecting the blade coloured image of petiole.
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Families Citing this family (52)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN113674274A (en) * | 2021-09-30 | 2021-11-19 | 贵州省生物技术研究所(贵州省生物技术重点实验室、贵州省马铃薯研究所、贵州省食品加工研究所) | Method and system for determining growth vigor of konjak symbiotic plant |
| CN114600751A (en) * | 2022-03-23 | 2022-06-10 | 西安建筑科技大学 | Irrigation system and method based on computer vision monitoring |
| CN114898364B (en) * | 2022-06-28 | 2023-03-10 | 安徽未来种业有限公司 | Efficient grape cultivation method and system |
| CN115170573B (en) * | 2022-09-08 | 2022-11-29 | 山东政信大数据科技有限责任公司 | Intelligent agricultural monitoring and management system based on big data and Internet of things |
| CN115240126B (en) * | 2022-09-23 | 2022-12-20 | 江苏景瑞农业科技发展有限公司 | Intelligent drip irrigation method based on artificial intelligence |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7617057B2 (en) * | 2005-12-21 | 2009-11-10 | Inst Technology Development | Expert system for controlling plant growth in a contained environment |
| EP2337441B1 (en) * | 2008-09-16 | 2016-08-10 | BASF Plant Science GmbH | Method for improved plant breeding |
| CN201298902Y (en) * | 2008-11-04 | 2009-08-26 | 北京中科嘉和科技发展有限公司 | Video terminal with environmental monitoring data |
| CN102156923A (en) * | 2011-04-22 | 2011-08-17 | 华建武 | Comprehensive plant production management system and method |
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