CN103090946B - Method and system for measuring single fruit tree yield - Google Patents
Method and system for measuring single fruit tree yield Download PDFInfo
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Abstract
The invention discloses a method and a system for measuring a single fruit tree yield. Three-dimensional point cloud data of a fruit tree are obtained and are pre-processed to obtain a three-dimensional point cloud data set of the fruit tree; the three-dimensional point cloud data set of the fruit tree is divided to obtain a fruit three-dimensional point cloud data set which only comprises fruit information; statistics is carried on the fruit three-dimensional point cloud data set to obtain N fruit three-dimensional point cloud data subsets and the number of fruits N; the fruit radius of each corresponding fruit is calculated according to the fruit three-dimensional point cloud data subsets to obtain a fruit radius digit group; fruit weight corresponding to each fruit radius in the fruit radius digit group is calculated one by one according to basic parameters of a relation model of fruit radius and fruit weight, and the yield of the whole fruit tree is calculated through accumulation. The method and the system for measuring the single fruit tree yield are capable of measuring the single fruit tree yield accurately, quickly and harmlessly.
Description
Technical field
The present invention relates to output of the fruit tree intelligent measure field, relate in particular to the method and system of the single tree of fruit tree yield monitoring.
Background technology
The measurement of output of the fruit tree all has important application demand in Agriculture production and management and agronomy research.The harmless major part of accurately measuring of output of the fruit tree is the method that adopts complicate statistics fruit number and estimate the single tree of fruit tree total production traditionally, this method wastes time and energy, and because fruit in fruit tree is often more, be difficult to remember which fruit is to add up, so the method for complicate statistics fruit number is easy to cause larger error.
In recent years, along with reaching its maturity of the technology such as the fast development of infotech, particularly machine vision, sonic detection, laser measurement, for the intelligent measure of output of the fruit tree provides new approach.
< < estimates to have designed in citrus production colorized vision system > > mono-literary composition a real-time oranges and tangerines system for measuring yield based on machine vision in real time, this system is walked on fruit tree limit by a robot with digital camera, and obtain the digital picture of fruit tree canopy, by utilizing fruit and leaf and the difference of other background informations in color in digital picture, oranges and tangerines are separated from digital picture, then oranges and tangerines are identified number, and according to each oranges and tangerines the weight of these oranges and tangerines of magnitude estimation in image, realize on this basis the measurement statistics of whole orchard output of the fruit tree.
< < estimates to adopt digital picture to carry out the measurement of wild blueberry output in wild blueberry fruit yield > > mono-literary composition based on color digital image, the method utilizes digital camera to take a digital pictures directly over blueberry plant colony, then utilize fruit and the difference of other partial informations in color in image to carry out background rejecting, make only to retain in digital picture the pixel information of fruit, and by adding up the quantity of these pixels, realize the output of captured blueberry colony.
Similarly, the oranges and tangerines that < < processes based on image are surveyed in product method > > mono-literary composition and are utilized machine vision technique to carry out the nondestructive measurement of mandarin tree output, the method is first by the digital pictures from an angle shot individual plant oranges and tangerines fruit tree, then based on different other the RGB color model of predefined mandarin tree to obtained oranges and tangerines Image Segmentation Using, from cut apart the citrusfruit image obtaining, extract the overall circumference of fruit number and each fruit, the characteristic parameters such as the fruit total area, finally calculate the total production of fruit tree, thereby realized the measurement of individual plant output of the fruit tree.
The apple garden recovery prediction > > that < < processes based on image utilizes Digital image technology, by obtain the digital picture in orchard florescence in fruit tree, the relation that fruit tree by prior foundation blooms between density and output of the fruit tree is simultaneously extracted the bloom fruit tree total production in density prediction orchard of the fruit tree obtaining from the digital picture of obtaining.
In the existing output of the fruit tree measuring method based on machine vision, only from a side for fruit tree, obtain the fruit tree digital picture that comprises fruit information, because fruit tree canopy is with luxuriant foliage and spreading branches in leafy profusion, serious shielding, therefore no matter from which side photographic images, in image, also cannot comprise all fruits on fruit tree, finally there is larger error by the fruit quantity of extracting in captured digital picture with real quantity by causing in this, and causes the larger error of fruit yield calculating from fruit quantity., owing to taking from different perspectives the fruit comprising in the fruit tree digital picture obtaining, all can there are differences, there is the feature that measurement result is not unique, have manual operation randomness in the method for therefore only measuring output of the fruit tree by taking the method for an image meanwhile.In addition, this method calculates the parameters such as the girth of fruit and radius by detecting the fruit pixel size obtain in captured digital picture, all there is the problem of distortion in the image of taking due to digital camera, this also can cause that the output of the fruit tree finally calculating produces another kind of error.
In output of the fruit tree evaluation method described in the apple garden recovery prediction > > that < < processes based on image, by the relation of bloom density and the actual measurement output of the fruit tree of prior foundation, again by obtaining the digital picture that fruit tree blooms and calculating the density of blooming, thereby derive the possible output of these fruit trees.This method is not analyzed by directly obtaining the digital picture of fruit tree fruiting period, the error of result of calculation may be larger, because the actual output of fruit tree also can be subject to the impact of multiple external condition, comprise disease and pest, rain, snow etc., the existing density of blooming of fruit tree might not be brought expected yield.Therefore this method is relatively suitable for large-scale orchard to carry out output estimation, and is not suitable for the measurement of individual plant output of the fruit tree.So said method all cannot accomplish the output of individual plant fruit tree to measure accurately.
Summary of the invention
(1) technical matters that will solve
How accurately and fast, nondestructively the output of individual plant fruit tree is measured the technical problem to be solved in the present invention is, for above-mentioned defect.
(2) technical scheme
For addressing the above problem, the invention provides the method and system of the single tree of fruit tree yield monitoring, described method comprises:
A: obtain the three dimensional point cloud of fruit tree, and described three dimensional point cloud is carried out to pre-service obtain fruit tree three dimensional point cloud collection;
B: described fruit tree three dimensional point cloud collection is cut apart to the fruit three dimensional point cloud collection that obtains only comprising fruit information;
C: described fruit three dimensional point cloud collection is added up, obtained N fruit three dimensional point cloud subset and fruit number N, N is positive integer;
D: calculate the fruit radius of each corresponding fruit according to described fruit three dimensional point cloud subset, obtain fruit radius array;
E: calculate one by one fruit quality corresponding to each fruit radius in fruit radius array according to the underlying parameter in the relational model of fruit radius and fruit quality, accumulation calculating goes out the output of whole strain fruit tree;
Before described step e, also comprise:
S: measure fruit radius and the fruit quality of sample fruit, set up the relational model of fruit radius and fruit quality, obtain described underlying parameter.
Preferably, described steps A specifically comprises:
A1: the fruit tree to band fruit state obtains the three dimensional point cloud of different angles, and forms fruit tree three-dimensional point cloud raw data set, comprises colouring information in wherein said three dimensional point cloud;
A2: the concentrated noise spot of described fruit tree three-dimensional point cloud raw data is tentatively rejected;
A3: the more concentrated noise spot of fruit tree three-dimensional point cloud raw data after preliminary rejecting is carried out to secondary rejecting, obtain only comprising the fruit tree three dimensional point cloud collection of fruit tree information.
Preferably, described step B specifically comprises:
B1: the color characteristic that obtains the data point that fruit organ is corresponding;
B2: the color value of each data point and the distance of described color characteristic according to described fruit tree three dimensional point cloud, concentrated, be partitioned into fruit organ data point in addition, obtain described fruit three dimensional point cloud collection.
Preferably, in described step C, described fruit three dimensional point cloud collection is added up and is specially: described fruit three dimensional point cloud collection is carried out to cluster, the data point that belongs to same fruit is divided in a fruit three dimensional point cloud subset, and the subset number obtaining is exactly fruit number.
Preferably, described step D specifically comprises:
D1: calculate the length and width parameter in each fruit three dimensional point cloud subset;
D2: the center point coordinate that calculates each fruit three dimensional point cloud subset;
D3: calculate all data points in each fruit three dimensional point cloud subset and the distance of described center point coordinate, and calculate mean distance;
D4: calculate fruit radius corresponding to each fruit three dimensional point cloud subset according to described length and width parameter and described mean distance, all fruit radiuses form described fruit radius array.
For addressing the above problem, the present invention also provides the system of the single tree of fruit tree yield monitoring, and described system comprises:
Pretreatment module, three dimensional point cloud are cut apart module, fruit counting module, fruit radius calculation module and output of the fruit tree computing module;
Described pretreatment module, obtains the three dimensional point cloud of fruit tree, and described three dimensional point cloud is carried out to pre-service obtains fruit tree three dimensional point cloud collection;
Described three dimensional point cloud is cut apart module, described fruit tree three dimensional point cloud collection is cut apart to the fruit three dimensional point cloud collection that obtains only comprising fruit information;
Described fruit counting module, adds up described fruit three dimensional point cloud collection, obtains fruit number;
Described fruit radius calculation module, calculates the fruit radius of each corresponding fruit according to described fruit three dimensional point cloud subset, obtain fruit radius array;
Described output of the fruit tree computing module, calculates fruit quality corresponding to each fruit radius in fruit radius array one by one according to the underlying parameter in the relational model of fruit radius and fruit quality, and accumulation calculating goes out the output of whole strain fruit tree;
Described system also comprises: underlying parameter module, measure fruit radius and the fruit quality of sample fruit, and set up the relational model of fruit radius and fruit quality, obtain described underlying parameter.
Preferably, described pretreatment module comprises: raw data set acquisition module, tentatively reject module and secondary and put forward rejecting module;
Described raw data set acquisition module, obtains the three dimensional point cloud of different angles to the fruit tree of band fruit state, and forms fruit tree three-dimensional point cloud raw data set, in wherein said three dimensional point cloud, comprises colouring information;
Described preliminary rejecting module, tentatively rejects the concentrated noise spot of described fruit tree three-dimensional point cloud raw data;
Described secondary is put forward rejecting module, and the concentrated noise spot of fruit tree three-dimensional point cloud raw data after preliminary rejecting is carried out to secondary rejecting, obtains only comprising the fruit tree three dimensional point cloud collection of fruit tree information.
Preferably, described three dimensional point cloud is cut apart module and is comprised: color characteristic acquisition module and fruit three dimensional point cloud collection acquisition module;
Described color characteristic acquisition module, obtains the color characteristic of the data point that fruit organ is corresponding;
Described fruit three dimensional point cloud collection acquisition module, the color value of each data point and the distance of described color characteristic according to described fruit tree three dimensional point cloud, concentrated, be partitioned into fruit organ data point in addition, obtains described fruit three dimensional point cloud collection.
Preferably, described fruit radius calculation module comprises: length and width acquisition module, center point coordinate computing module, mean distance computing module and fruit radius array acquisition module;
Described length and width acquisition module, calculates the length and width parameter in each fruit three dimensional point cloud subset;
Described center point coordinate computing module, calculates the center point coordinate of each fruit three dimensional point cloud subset;
Described mean distance computing module, calculates all data points in each fruit three dimensional point cloud subset and the distance of described center point coordinate, and calculates mean distance;
Described fruit radius array acquisition module, calculates fruit radius corresponding to each fruit three dimensional point cloud subset according to described length and width parameter and described mean distance, and all fruit radiuses form described fruit radius array.
(3) beneficial effect
The present invention proposes the method and system of the single tree of fruit tree yield monitoring, by obtaining the three dimensional point cloud of fruit tree, and carry out pre-service and obtain fruit tree three dimensional point cloud collection; Utilize fruit to obtain fruit three dimensional point cloud collection different cutting apart from the color characteristic of other organs, in the employing method of birdsing of the same feather flock together, fruit three dimensional point cloud collection is cut apart, guarantee treatment effeciency and the accuracy of fruit counting; According to fruit three dimensional point cloud subset, calculate the fruit radius of each corresponding fruit, obtain fruit radius array; According to the underlying parameter in the relational model of fruit radius and fruit quality, calculate one by one fruit quality corresponding to each fruit radius in fruit radius array, accumulation calculating goes out the output of whole strain fruit tree, can also calculate the weight of fruit mean diameter and average each fruit, can be accurately and fast, nondestructively the single tree of fruit tree output is measured.
Accompanying drawing explanation
Fig. 1 is the flow chart of steps of the method for the single tree of fruit tree yield monitoring;
Fig. 2 is the particular flow sheet of steps A in the method for the single tree of fruit tree yield monitoring;
Fig. 3 is the particular flow sheet of step B in the method for the single tree of fruit tree yield monitoring;
Fig. 4 is the particular flow sheet of step D in the method for the single tree of fruit tree yield monitoring;
Fig. 5 is the composition schematic diagram of the system of the single tree of fruit tree yield monitoring.
Embodiment
Below in conjunction with drawings and Examples, the specific embodiment of the present invention is described in further detail.Following examples are used for illustrating the present invention, but are not used for limiting the scope of the invention.
Embodiment mono-
The method that the single tree of fruit tree yield monitoring is provided in the embodiment of the present invention one, steps flow chart as shown in Figure 1, specifically comprises the following steps:
Steps A: obtain the three dimensional point cloud of fruit tree, and three dimensional point cloud is carried out to pre-service obtain fruit tree three dimensional point cloud collection D.
Utilize large laser spatial digitizer from field, directly to obtain the three dimensional point cloud of fruit tree, and the three dimensional point cloud obtaining is carried out to the pre-service such as noise points deleting, steps flow chart as shown in Figure 2, specifically comprises the following steps:
Steps A 1: the fruit tree to band fruit state obtains the three dimensional point cloud of different angles, and forms fruit tree three-dimensional point cloud raw data set, wherein comprises colouring information in three dimensional point cloud.
In orchard, to carrying out the achievement phase fruit tree of yield monitoring, utilize laser 3 d scanner (for example FARO focus3D120) to carry out 3-D scanning, during scanning, at least 3 angles from fruit tree are carried out multistation scanning, collect the fruit tree three-dimensional point cloud raw data set with colouring information.But utilize three-dimensional laser scanner under the environment of orchard, fruit tree to be carried out in three dimensional point cloud acquisition process, due to blocking between different fruit trees and between organ, and the impact of other external conditions (as wind), the three dimensional point cloud obtaining inevitably can, with noise spot, therefore need to carry out the noise spot of following two steps and remove operation.
Steps A 2: the concentrated noise spot of fruit tree three-dimensional point cloud raw data is tentatively rejected, concrete grammar is as follows: first adopt minimum neighbours' method to carry out the preliminary rejecting of noise spot, each data point of fruit tree three-dimensional point cloud raw data being concentrated is carried out minimum neighbours' inspection, if the neighbours of certain data point (data point that is not more than 0.5cm apart from the air line distance of this point is the neighbours of this point) number is less than 30, is about to this data point and concentrates and delete from three-dimensional point cloud raw data.
Steps A 3: the more concentrated noise spot of fruit tree three-dimensional point cloud raw data after preliminary rejecting is carried out to secondary rejecting, obtain only comprising the fruit tree three dimensional point cloud collection D of fruit tree information.The concentrated noise spot of fruit tree three dimensional point cloud after preliminary rejecting is carried out to quadratic noise and reject processing, to remove those, use minimum nearest neighbour method cannot reject and don't belong to the data point of fruit tree its data, method is to utilize general three-dimensional point cloud process software to import the above fruit tree three-dimensional point cloud raw data set obtaining, and rejects those in three-dimensional point cloud do not belong to the data point of the organs such as fruit tree leaf, fruit, limb by the method for choosing alternately data point and deleting.Noise points deleting through above two steps is processed, and concentrates the noise spot that does not belong to fruit tree partly to remove the three-dimensional point cloud raw data of fruit tree, obtains only comprising the fruit tree three dimensional point cloud collection D of fruit tree information.
Step B: fruit tree three dimensional point cloud collection D is cut apart to the fruit three dimensional point cloud collection D that obtains only comprising fruit information
f, this step is mainly previous step to be processed to the fruit tree three dimensional point cloud collection D obtaining cut apart, and the data point that does not wherein belong to fruit is rejected, specific implementation process as shown in Figure 3, specifically comprises the following steps:
Step B1: the color characteristic that obtains the data point that fruit organ is corresponding.According to fruit tree three dimensional point cloud collection D, calculate the color characteristic of the data point that in three dimensional point cloud, fruit organ is corresponding, concrete grammar is: first by interactive mode, from fruit tree three dimensional point cloud collection D, choose 50 of the data points that fruit organ is corresponding, then calculate the color average of these 50 data points.The color of supposing certain data point i in 50 selected data points is c
i(r, g, b), wherein i value is the natural number between 1 to 50, the computing formula of the color characteristic of fruit organ is:
C wherein
irbe the r component value of the color of i data point, c
igbe the g component value of the color of i data point, c
ibit is the b component value of the color of i data point.
Step B2: according to the color value of each data point and the distance d of color characteristic in fruit tree three dimensional point cloud collection D
f, be partitioned into fruit organ data point in addition, obtain fruit three dimensional point cloud collection D
f.To each the data point p in the three dimensional point cloud collection D obtaining in steps A 3, calculate respectively the color c of this data point
pthe distance d of the color characteristic of (r, g, b) and fruit
f=| c
pr-OC
r|+| c
pg-OC
g|+| c
pb-OC
b|, c wherein
pr, c
pgand c
pbbe respectively r component value, g component value and the b component value of the color of data point p, OC
r, OC
gand OC
bbe respectively r component value, g component value and the b component value of the color characteristic of fruit organ.If d
f> 50, data point p rejected from data set D.Finally obtain only retaining the fruit three dimensional point cloud collection D of fruit information
f.
Step C: to fruit three dimensional point cloud collection D
fadd up, obtain N fruit three dimensional point cloud subset D
fiwith fruit number N, concrete grammar is: to fruit three dimensional point cloud collection D
fanalyze, utilize the clustering method based on distance to carry out cluster to data point, the data point that is about to belong to same fruit is divided into a son and concentrates, thereby by whole fruit 3-D data set D
fbe divided into N little fruit three dimensional point cloud subset (remembered to each subset is D
fi, wherein i value is the natural number between 1 to N), N is the fruit number calculating.
Step D: according to fruit three dimensional point cloud subset D
ficalculate the fruit radius r of each corresponding fruit
i, obtaining fruit radius array, steps flow chart as shown in Figure 4, specifically comprises the following steps:
Step D1: calculate each fruit three dimensional point cloud subset D
fiinterior length and width parameter.First calculate fruit three dimensional point cloud subset D
fithe external rectangular parallelepiped of minimum, the more rectangular length l of the intercepting in xoy plane from the external rectangular parallelepiped of calculated minimum and width w.
Step D2: calculate each fruit three dimensional point cloud subset D
ficenter point coordinate v
fi.
Step D3: calculate each fruit three dimensional point cloud subset D
fiin all data points and center point coordinate v
fidistance, and calculate fruit three dimensional point cloud subset D
fimiddle a little with center point coordinate v
fimean distance d
fi.
Step D4: calculate each fruit three dimensional point cloud subset D according to length and width parameter and mean distance
ficorresponding fruit radius r
i=((l+w)/2+d
fi/ 2)/2, all fruit radius r
iform fruit radius array.
E: calculate one by one fruit quality corresponding to each fruit radius in fruit radius array according to the underlying parameter in the relational model of fruit radius and fruit quality, accumulation calculating goes out the output of whole strain fruit tree.
Before step e, also comprise:
Step S: measure fruit radius and the fruit quality of sample fruit, set up the relational model of fruit radius and fruit quality, obtain underlying parameter.This step is carried out before steps A again, also can be after step D, carry out before step e, and the step S of take in the process flow diagram of Fig. 1 carried out as example before steps A.
The Main Function of step S is to set up the fruit radius of fruit tree and the relational model of fruit quality by actual measurement, and method is:
In orchard to carrying out the achievement phase fruit tree of yield monitoring, from setting, gather different big or small fruit 8-20, measure radius and the weight of each fruit, and utilize the Mathematical Fitting methods such as regretional analysis to set up the relational model g=r * λ of fruit radius and weight, wherein g is fruit weight, r is fruit radius, and λ is the underlying parameter that matching obtains.
Adopt fruit radius that step S sets up and the relational model g=r * λ of fruit weight, take out the radius of each fruit and calculate the weight of each fruit from step D the fruit radius array calculating, accumulation calculating can obtain the output of whole strain fruit tree.
Can also calculate other parameters by the result of calculation of utilizing above several step to obtain, as calculated the mean radius of fruit according to the fruit radius of all fruits of whole strain fruit tree and fruit number, according to the fruit quality of all fruits of whole strain fruit tree and fruit number, calculate mean fruit weight amount etc.
By said method, all harmless to fruit tree and fruit in the situation that, can to the single tree of fruit tree output, measure accurately and fast.
Embodiment bis-
For achieving the above object, the system of the single tree of fruit tree yield monitoring is also provided in embodiments of the invention two, form schematic diagram as shown in Figure 5, specifically comprise:
Pretreatment module 510, three dimensional point cloud are cut apart module 520, fruit counting module 530, fruit radius calculation module 540 and output of the fruit tree computing module 550.
Pretreatment module 510, for obtaining the three dimensional point cloud of fruit tree, and carries out pre-service to three dimensional point cloud and obtains fruit tree three dimensional point cloud collection D.
Pretreatment module 510 specifically comprises: raw data set acquisition module 511, tentatively reject module 512 and secondary and carry and reject module 513.
Raw data set acquisition module 511, for the fruit tree of band fruit state is obtained to the three dimensional point cloud of different angles, and forms fruit tree three-dimensional point cloud raw data set, wherein in three dimensional point cloud, comprises colouring information.In orchard, to carrying out the achievement phase fruit tree of yield monitoring, utilize laser 3 d scanner (for example FARO focus3D120) to carry out 3-D scanning, during scanning, at least 3 angles from fruit tree are carried out multistation scanning, collect the fruit tree three-dimensional point cloud raw data set with colouring information.But utilize three-dimensional laser scanner under the environment of orchard, fruit tree to be carried out in three dimensional point cloud acquisition process, due to blocking between different fruit trees and between organ, and the impact of other external conditions (as wind), the three dimensional point cloud obtaining inevitably can, with noise spot, therefore need to carry out the noise spot of following two steps and remove operation.
The preliminary module 512 of rejecting, for the concentrated noise spot of fruit tree three-dimensional point cloud raw data is tentatively rejected, concrete grammar is as follows: first adopt minimum neighbours' method to carry out the preliminary rejecting of noise spot, each data point of fruit tree three-dimensional point cloud raw data being concentrated is carried out minimum neighbours' inspection, if the neighbours of certain data point (data point that is not more than 0.5cm apart from the air line distance of this point is the neighbours of this point) number is less than 30, is about to this data point and concentrates and delete from three-dimensional point cloud raw data.
Secondary is carried and is rejected module 513, for the concentrated noise spot of fruit tree three-dimensional point cloud raw data to after preliminary rejecting, carries out secondary rejecting, obtains only comprising the fruit tree three dimensional point cloud collection D of fruit tree information.Carry out secondary rejecting and use minimum nearest neighbour method cannot reject and don't belong to the data point of fruit tree its data to remove those, method is to utilize general three-dimensional point cloud process software to import the above fruit tree three-dimensional point cloud raw data set obtaining, and rejects those in three-dimensional point cloud do not belong to the data point of the organs such as fruit tree leaf, fruit, limb by the method for choosing alternately data point and deleting.Noise points deleting through above two steps is processed, and concentrates the noise spot that does not belong to fruit tree partly to remove the three-dimensional point cloud raw data of fruit tree, obtains only comprising the fruit tree three dimensional point cloud collection D of fruit tree information.
Three dimensional point cloud is cut apart module 520, for fruit tree three dimensional point cloud collection being cut apart to the fruit three dimensional point cloud collection D that obtains only comprising fruit information
f.
Three dimensional point cloud is cut apart module 520 and is specifically comprised: color characteristic acquisition module 521 and fruit three dimensional point cloud collection acquisition module 522.
Color characteristic acquisition module 521, obtains the color characteristic of the data point that fruit organ is corresponding.First by interactive mode, from fruit tree three dimensional point cloud collection D, choose 50 of the data points that fruit organ is corresponding, then calculate the color average of these 50 data points.The color of supposing certain data point i in 50 selected data points is c
i(r, g, b), wherein i value is the natural number between 1 to 50, the computing formula of the color characteristic of fruit organ is:
C wherein
irbe the r component value of the color of i data point, c
igbe the g component value of the color of i data point, c
ibit is the b component value of the color of i data point.
Fruit three dimensional point cloud collection acquisition module 522, according to the color value of each data point and the distance of color characteristic in fruit tree three dimensional point cloud collection D, is partitioned into fruit organ data point in addition, obtains fruit three dimensional point cloud collection D
f.Secondary is carried to each the data point p rejecting in the three dimensional point cloud collection D obtaining in module 513, calculated respectively the color c of this data point
pthe distance d of the color characteristic of (r, g, b) and fruit
f=| c
pr-OC
r|+| c
pg-OC
g|+| c
pb-OC
b|, c wherein
pr, c
pgand c
pbbe respectively r component value, g component value and the b component value of the color of data point p, OC
r, OC
gand OC
bbe respectively r component value, g component value and the b component value of the color characteristic of fruit organ.If d
f> 50, data point p rejected from data set D.Finally obtain only retaining the fruit three dimensional point cloud collection D of fruit information
f.
Fruit counting module 530, for to fruit three dimensional point cloud collection D
fadd up, obtain fruit number N.The method of statistics is: to fruit three dimensional point cloud collection D
fanalyze, utilize the clustering method based on distance to carry out cluster to data point, the data point that is about to belong to same fruit is divided into a son and concentrates, thereby by whole fruit 3-D data set D
fbe divided into N little fruit three dimensional point cloud subset (remember that each subset is, wherein i value is the natural number between 1 to N), N is the fruit number calculating.
Fruit radius calculation module 540, for according to fruit three dimensional point cloud subset D
ficalculate the fruit radius r of each corresponding fruit
i, obtain fruit radius array.
Fruit radius calculation module 540 specifically comprises: length and width acquisition module 541, centre coordinate computing module 542, mean distance computing module 543 and fruit radius array acquisition module 544.
Length and width acquisition module 541, calculates each fruit three dimensional point cloud subset D
fiinterior length and width parameter, length and width parameter comprises length l and width w.
Centre coordinate computing module 542, calculates each fruit three dimensional point cloud subset D
ficenter point coordinate v
fi.
Mean distance computing module 543, calculates each fruit three dimensional point cloud subset D
fiin all data points and center point coordinate v
fidistance, and calculate fruit three dimensional point cloud subset D
fimiddle a little with center point coordinate v
fimean distance d
fi.
Fruit radius array acquisition module 544, the mean distance d that the length l obtaining according to length and width acquisition module 541, width w and average distance calculation module 543 obtain
ficalculate fruit radius r corresponding to each fruit three dimensional point cloud subset
i=((l+w)/2+d
fi/ 2)/2, all fruit radius r
iform fruit radius array.
Output of the fruit tree computing module 550, calculates fruit quality corresponding to each fruit radius in fruit radius array one by one according to the underlying parameter in the relational model of fruit radius and fruit quality, and accumulation calculating goes out the output of whole strain fruit tree.
This system also comprises: underlying parameter module 560, measure fruit radius and the fruit quality of sample fruit, and set up the relational model of fruit radius and fruit quality, obtain underlying parameter.By actual measurement, set up the fruit radius of fruit tree and the relational model of fruit quality, method is: in orchard to carrying out the achievement phase fruit tree of yield monitoring, from setting, gather different big or small fruit 8-20, measure radius and the weight of each fruit, and utilize the Mathematical Fitting methods such as regretional analysis to set up the relational model g=r * λ of fruit radius and weight, wherein g is fruit weight, and r is fruit radius, and λ is the underlying parameter that matching obtains.
By said apparatus, all harmless to fruit tree and fruit in the situation that, can to the single tree of fruit tree output, measure accurately and fast.
Above embodiment is only for illustrating the present invention; and be not limitation of the present invention; the those of ordinary skill in relevant technologies field; without departing from the spirit and scope of the present invention; can also make a variety of changes and modification; therefore all technical schemes that are equal to also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.
Claims (9)
1. the method for the single tree of fruit tree yield monitoring, is characterized in that, described method specifically comprises:
A: obtain the three dimensional point cloud of fruit tree, and described three dimensional point cloud is carried out to pre-service obtain fruit tree three dimensional point cloud collection;
B: described fruit tree three dimensional point cloud collection is cut apart to the fruit three dimensional point cloud collection that obtains only comprising fruit information;
C: described fruit three dimensional point cloud collection is added up, obtained N fruit three dimensional point cloud subset and fruit number N, N is positive integer;
D: calculate the fruit radius of each corresponding fruit according to described fruit three dimensional point cloud subset, obtain fruit radius array;
E: calculate one by one fruit quality corresponding to each fruit radius in fruit radius array according to the underlying parameter in the relational model of fruit radius and fruit quality, accumulation calculating goes out the output of whole strain fruit tree;
Before described step e, also comprise:
S: measure fruit radius and the fruit quality of sample fruit, set up the relational model of fruit radius and fruit quality, obtain described underlying parameter.
2. the method for claim 1, is characterized in that, described steps A specifically comprises:
A1: the fruit tree to band fruit state obtains the three dimensional point cloud of different angles, and forms fruit tree three-dimensional point cloud raw data set, comprises colouring information in wherein said three dimensional point cloud;
A2: the concentrated noise spot of described fruit tree three-dimensional point cloud raw data is tentatively rejected;
A3: the more concentrated noise spot of fruit tree three-dimensional point cloud raw data after preliminary rejecting is carried out to secondary rejecting, obtain only comprising the fruit tree three dimensional point cloud collection of fruit tree information.
3. the method for claim 1, is characterized in that, described step B specifically comprises:
B1: the color characteristic that obtains the data point that fruit organ is corresponding;
B2: the color value of each data point and the distance of described color characteristic according to described fruit tree three dimensional point cloud, concentrated, be partitioned into fruit organ data point in addition, obtain described fruit three dimensional point cloud collection.
4. the method for claim 1, it is characterized in that, in described step C, described fruit three dimensional point cloud collection is added up and is specially: described fruit three dimensional point cloud collection is carried out to cluster, the data point that belongs to same fruit is divided in a fruit three dimensional point cloud subset, and the subset number obtaining is exactly fruit number.
5. the method for claim 1, is characterized in that, described step D specifically comprises:
D1: calculate the length and width parameter in each fruit three dimensional point cloud subset;
D2; Calculate the center point coordinate of each fruit three dimensional point cloud subset;
D3: calculate all data points in each fruit three dimensional point cloud subset and the distance between described center point coordinate, and calculate mean distance;
D4: calculate fruit radius corresponding to each fruit three dimensional point cloud subset according to described length and width parameter and described mean distance, all fruit radiuses form described fruit radius array.
6. the system of the single tree of fruit tree yield monitoring, is characterized in that, described system specifically comprises:
Pretreatment module, three dimensional point cloud are cut apart module, fruit counting module, fruit radius calculation module and output of the fruit tree computing module;
Described pretreatment module, obtains the three dimensional point cloud of fruit tree, and described three dimensional point cloud is carried out to pre-service obtains fruit tree three dimensional point cloud collection;
Described three dimensional point cloud is cut apart module, described fruit tree three dimensional point cloud collection is cut apart to the fruit three dimensional point cloud collection that obtains only comprising fruit information;
Described fruit counting module, adds up described fruit three dimensional point cloud collection, obtains fruit number;
Described fruit radius calculation module, calculates the fruit radius of each corresponding fruit according to described fruit three dimensional point cloud subset, obtain fruit radius array;
Described output of the fruit tree computing module, calculates fruit quality corresponding to each fruit radius in fruit radius array one by one according to the underlying parameter in the relational model of fruit radius and fruit quality, and accumulation calculating goes out the output of whole strain fruit tree;
Described system also comprises: underlying parameter module, measure fruit radius and the fruit quality of sample fruit, and set up the relational model of fruit radius and fruit quality, obtain described underlying parameter.
7. system as claimed in claim 6, is characterized in that, described pretreatment module comprises: raw data set acquisition module, tentatively reject module and secondary and put forward rejecting module;
Described raw data set acquisition module, obtains the three dimensional point cloud of different angles to the fruit tree of band fruit state, and forms fruit tree three-dimensional point cloud raw data set, in wherein said three dimensional point cloud, comprises colouring information;
Described preliminary rejecting module, tentatively rejects the concentrated noise spot of described fruit tree three-dimensional point cloud raw data;
Described secondary is put forward rejecting module, and the concentrated noise spot of fruit tree three-dimensional point cloud raw data after preliminary rejecting is carried out to secondary rejecting, obtains only comprising the fruit tree three dimensional point cloud collection of fruit tree information.
8. system as claimed in claim 6, is characterized in that, described three dimensional point cloud is cut apart module and comprised: color characteristic acquisition module and fruit three dimensional point cloud collection acquisition module;
Described color characteristic acquisition module, obtains the color characteristic of the data point that fruit organ is corresponding;
Described fruit three dimensional point cloud collection acquisition module, the color value of each data point and the distance of described color characteristic according to described fruit tree three dimensional point cloud, concentrated, be partitioned into fruit organ data point in addition, obtains described fruit three dimensional point cloud collection.
9. system as claimed in claim 6, is characterized in that, described fruit radius calculation module comprises: length and width acquisition module, centre coordinate computing module, mean distance computing module and fruit radius array acquisition module;
Described length and width acquisition module, calculates the length and width parameter in each fruit three dimensional point cloud subset;
Described centre coordinate computing module, calculates the center point coordinate of each fruit three dimensional point cloud subset;
Described mean distance computing module, calculates all data points in each fruit three dimensional point cloud subset and the distance of described center point coordinate, and calculates mean distance;
Described fruit radius array acquisition module, calculates fruit radius corresponding to each fruit three dimensional point cloud subset according to described length and width parameter and described mean distance, and all fruit radiuses form described fruit radius array.
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| CN104881626B (en) * | 2015-01-19 | 2017-12-29 | 新疆农业大学 | The recognition methods of Tree Fruit |
| CN104677342A (en) * | 2015-03-08 | 2015-06-03 | 无锡桑尼安科技有限公司 | Apple orchard yield measuring method |
| CN112215184A (en) * | 2020-10-21 | 2021-01-12 | 安徽农业大学 | Camellia oleifera fruit tree yield detection method based on three-dimensional laser scanner |
| CN112556606A (en) * | 2020-12-24 | 2021-03-26 | 宁夏农林科学院农业经济与信息技术研究所（宁夏农业科技图书馆） | Self-propelled wolfberry fruit actual measurement method and device based on binocular vision |
| CN115388993A (en) * | 2022-10-08 | 2022-11-25 | 山东省果树研究所 | Fruit tree yield measuring device and measuring method |
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