CN112784735B - Method for identifying string-type fruit mother branches based on monocular camera and binocular camera - Google Patents

Method for identifying string-type fruit mother branches based on monocular camera and binocular camera Download PDF

Info

Publication number
CN112784735B
CN112784735B CN202110081724.3A CN202110081724A CN112784735B CN 112784735 B CN112784735 B CN 112784735B CN 202110081724 A CN202110081724 A CN 202110081724A CN 112784735 B CN112784735 B CN 112784735B
Authority
CN
China
Prior art keywords
fruit
point
fruits
image
color image
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110081724.3A
Other languages
Chinese (zh)
Other versions
CN112784735A (en
Inventor
王成琳
罗天洪
柳苏纯
王雅薇
罗陆锋
熊俊涛
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chongqing University of Arts and Sciences
Original Assignee
Chongqing University of Arts and Sciences
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Chongqing University of Arts and Sciences filed Critical Chongqing University of Arts and Sciences
Priority to CN202110081724.3A priority Critical patent/CN112784735B/en
Publication of CN112784735A publication Critical patent/CN112784735A/en
Application granted granted Critical
Publication of CN112784735B publication Critical patent/CN112784735B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G06F18/2148—Generating training patterns; Bootstrap methods, e.g. bagging or boosting characterised by the process organisation or structure, e.g. boosting cascade
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/68—Food, e.g. fruit or vegetables

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Multimedia (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

Abstract

The invention provides a method for identifying string-type fruit mother branches based on a monocular camera and a binocular camera, which comprises the steps of firstly, acquiring a color image of string-type fruit characteristics including fruits, leaves and branches by using the monocular camera; then, training, segmenting, identifying and extracting the color image by using a support vector machine and an AdaBoost algorithm to obtain the color image of the fruit, the leaf and the branch; then, classifying the fruits by using a fruit cluster classification principle to perform classification of single-fruit clusters, double-fruit clusters and multi-fruit clusters, and then identifying fruit cluster mother branches by using the perpendicular bisectors of the upper and lower bottoms of the outline circumscribed rectangle of the fruit clusters; and finally, acquiring binocular stereo images of the fruit cluster mother branches by using a binocular camera, and realizing the characteristic matching of the left image and the right image by using a normalized cross-correlation function to finally obtain the space coordinates of the mother branches. The method can accurately and effectively identify the fruit clusters and position the mother branches of the fruit clusters, realizes intelligent picking of cluster-type fruits, is high in picking efficiency, and effectively reduces the labor cost.

Description

Method for identifying string-type fruit mother branches based on monocular camera and binocular camera
Technical Field
The invention relates to the technical field of intelligent fruit picking, in particular to a method for identifying string-type fruit mother branches based on a monocular camera and a binocular camera.
Background
China is a main producing country of string-type fruits such as longan, grapes and litchi, the total planting and production amount is in the forefront of the world, and the method has great economic potential; for example, litchi, as a typical bunch type fruit in subtropical zone, in south China, planting litchi has become one of the important economic pillars for farmers to delight poverty and become rich.
At present, picking of the string-shaped fruits is basically carried out manually, the picking labor intensity is high, the picking cost is high, and a large amount of manpower and material resources are wasted. Along with the progress of science and technology and the development of intelligent level, the research and the application of picking robot are more and more, bring certain convenience for orchard management and fruit picking, simultaneously, also effectively liberated the manpower, reduced the human cost. In the process of picking the cluster-shaped fruits manually, the mother branches of the fruit clusters need to be determined preferentially, and the fruit clusters are separated from the fruit trees in a picking and cutting mode; the cutting of the fruit clusters from the mother branches ensures the integrity of the fruit clusters and avoids the fruit clusters from being scattered into a plurality of small clusters, thereby being beneficial to storage and transportation, ensuring the aesthetic property of the fruit clusters and ensuring the economic value of the fruit clusters. However, due to the self-growing characteristics of the string-type fruits, the whole fruit string is easy to randomly grow, so that the problems that the fruit string is difficult to identify, the mother branch is difficult to identify and position, and the whole string-type fruit mother branch cannot be accurately found are caused, and the intelligent string-type fruit picking system replacing manual picking operation faces huge challenges in the aspect of wide application.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide a method for identifying string-type fruit mother branches based on a monocular camera and a binocular camera, which can accurately and effectively identify fruit strings and position the fruit string mother branches, so that intelligent picking of string-type fruits is realized, picking efficiency is high, and labor cost is effectively reduced.
The purpose of the invention is realized by the following technical scheme:
a method for identifying string-type fruit mother branches based on a monocular camera and a binocular camera is characterized in that:
s001, randomly acquiring a plurality of color images including the characteristics of the string-type fruits by adopting a monocular CCD (charge coupled device) camera;
s002, selecting and dividing a plurality of fruit targets and non-fruit targets in the color image, and respectively extracting texture characteristic values and color characteristic values of the fruit targets and the non-fruit targets as positive and negative samples;
s003, training the positive and negative samples by adopting a Support Vector Machine (SVM) to generate a plurality of weak classifiers; then, an AdaBoost algorithm is adopted to construct a strong classifier, the color image acquired by the monocular CCD camera is segmented by the strong classifier, and the fruit target identified from the image is separately stored into a color image I1；
S004, repeating the steps S002-S003 to respectively obtain strong classifiers for identifying branches and leaves; then respectively removing the image I1The branches and leaves are extracted from the original color image and stored into a color image I respectively2And a color image I3；
S005, dividing the fruit clusters into single fruit clusters, double fruit clusters and multi-fruit clusters according to the distribution positions of the fruits, and further determining the color image I1The fruit cluster type of the randomly distributed fruits;
s006, determining mother branches according to the fruit string type determined in the step S005:
for a single fruit cluster, the branch connected with the fruit is the mother branch;
for the double fruit cluster and the multi-fruit cluster, firstly, in the color image I1The middle making fruit string is combined with the color image I through the perpendicular bisector of the upper and lower bottoms of the outline circumscribed rectangle1With color images I2Performing fusion analysis by color image I1Perpendicular bisector and color image I2Determining the parent branch of the fruit bunch according to the branch tangency condition;
s007, acquiring binocular stereo images of fruit cluster mother branches by using a binocular CCD camera, taking a mother branch circumscribed rectangular geometric central point in a left image of the binocular CCD camera as a feature matching point, searching a point which is closest to a gray value of the feature matching point and enables a normalized cross-correlation function to reach a maximum value in a right image, realizing feature matching, and acquiring a geometric central point of the mother branch;
and S008, finally, calculating the space coordinate of the geometric center point of the parent branch to realize the positioning of the parent branch.
The fruit cluster type classification and the mother branch determination by using the perpendicular bisector of the through-string contour line circumscribed rectangle are adopted, so that firstly, the interference of branches connected with each fruit can be effectively eliminated, and the recognition precision is improved; secondly, the mother branches of the fruit clusters are quickly identified, so that the identification efficiency is improved, and the identification time is shortened.
For further optimization, the string-shaped fruit features in the step S001 include fruits, leaves and branches.
For further optimization, in the step S005, a fruit cluster classification principle is adopted to classify the single fruit cluster, the double fruit cluster and the multi-fruit cluster;
the method specifically comprises the following steps: single fruit bunch: if the Euclidean distance between the geometric center of one fruit and the geometric center of any other fruit is larger than the average diameter of a single fruit, the fruit is a single fruit cluster; double fruit bunch: if the Euclidean distance between the geometric centers of two adjacent fruits is smaller than the sum of the diameters of the two fruits, the two fruits are a double-fruit cluster; multi-fruit clusters: if the Euclidean distance between the geometric centers of any two fruits in more than two fruits is smaller than the sum of the diameters of the two fruits, the group of fruits is a multi-fruit cluster.
Further optimized, the step S006 is to pass the color image I1Perpendicular bisector and color image I2The method is characterized in that the method comprises the following steps of determining the mother branch of a fruit cluster according to the branch tangency condition: and rotating the perpendicular bisector of the upper and lower bottoms of the outline circumscribed rectangle of the fruit cluster around a point below the perpendicular bisector to one side close to the branches, wherein the branch which is firstly tangent to the perpendicular bisector is the parent branch of the fruit cluster.
Further optimization is performed, the step of searching a point which is closest to the gray value of the feature matching point and enables the normalized cross-correlation function to reach the maximum value in the right image in the step S007 specifically includes:
first, a left image P is obtained by a binocular CCD camera1The feature matching point (x, y) of (a) constructs a matching window while the right image P is taken2The point (x + m, y + m) to be matched also constructs a matching window, and measures the correlation degree by normalizing the correlation function, wherein the specific formula is as follows:
Figure BDA0002909356550000031
wherein F (m, n) represents a normalized correlation function; (m, n) represents a position vector of the right image relative to the left image; wpRepresenting a matching window taking the coordinates of the point to be matched as a center; p2(x + m, y + n) represents the gray value of the point to be matched of the right image;
Figure BDA0002909356550000032
representing a gray level mean of the right image matching window; p1(x, y) represents the gray value of the left image feature matching point;
Figure BDA0002909356550000033
representing a gray level mean of the left image matching window;
wherein, the value range of F (m, n) is [ -1,1 ];
when F (m, n) is-1, the feature matching point of the left image is completely unrelated to the point to be matched of the right image;
when F (m, n) is 1, the feature matching point of the left image is completely matched with the point to be matched of the right image, that is, the point to be matched is the point at which the gray value of the feature matching point is closest and the normalized cross-correlation function of the point to be matched reaches the maximum value.
Preferably, the matching window is typically a 3 x 3 matching window.
Further preferably, the left image and the right image are subjected to epipolar line correction before feature matching is carried out, so that the epipolar lines of the left image and the right image are in the horizontal direction, even if the optical centers of the left image and the right image are in the same horizontal line.
Preferably, the epipolar correction is performed using the Bouguet epipolar correction algorithm.
And further optimizing, wherein the spatial coordinates of the geometric center points of the mother branches are calculated by adopting a triangulation principle in the step S008.
The invention has the following technical effects:
the method provided by the invention can quickly, effectively and accurately determine the mother branches of the string-shaped fruits, and avoids the interference of fruit branches and the influence of the shielding of fruits and leaves on the recognition of the mother branches, thereby avoiding the problem that the mother branches cannot be accurately cut during picking, the string-shaped fruits are scattered or the fruits are easy to fall off, and further influencing the subsequent storage and transportation.
The method effectively improves the intelligent level in the process of picking the string-shaped fruits, automatically takes pictures and analyzes the fruit trees and confirms the spatial coordinate point of the mother branch, and improves the working efficiency of picking and the yield of the string-shaped fruits.
Drawings
FIG. 1 is a flow chart of the method for identifying the fruit stem of the fruit string in the embodiment of the invention.
Fig. 2 is a schematic diagram of the principle of identifying the string-type fruit mother branch in the embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example (b):
as shown in fig. 1-2, a method for identifying a string-type fruit mother branch based on a monocular camera and a binocular camera, taking a litchi string-type fruit as an example, is characterized in that:
s001, randomly acquiring a plurality of color images together with the characteristics of the fruit, the leaf, the branch and the like of the string-type fruit by adopting a monocular CCD (charge coupled device) camera;
s002, selecting and dividing a plurality of fruit targets and non-fruit targets in the color image, and respectively extracting texture characteristic values and color characteristic values of the fruit targets and the non-fruit targets as positive and negative samples;
s003, training the positive and negative samples by adopting a Support Vector Machine (SVM) to generate a plurality of weak classifiers; then, an AdaBoost algorithm is adopted to construct a strong classifier, the color image acquired by the monocular CCD camera is segmented by the strong classifier, and the fruit target identified from the image is separately stored into a color image I1；
S004, repeating the steps S002-S003 to respectively obtain strong classifiers for identifying branches and leaves; then respectively removing the image I1The branches and leaves are extracted from the original color image and stored into a color image I respectively2And a color image I3；
S005, dividing the fruit clusters into single fruit clusters, double fruit clusters and multi-fruit clusters according to the distribution positions of the fruits and by adopting a fruit cluster classification principle, and further determining a color image I1The fruit cluster type of the randomly distributed fruits;
the method specifically comprises the following steps: single fruit bunch: if the Euclidean distance between the geometric center of one fruit and the geometric center of any other fruit is larger than the average diameter of a single fruit, the fruit is a single fruit cluster; double fruit bunch: if the Euclidean distance between the geometric centers of two adjacent fruits is smaller than the sum of the diameters of the two fruits, the two fruits are a double-fruit cluster; multi-fruit clusters: if the Euclidean distance between the geometric centers of any two fruits in more than two fruits is smaller than the sum of the diameters of the two fruits, the group of fruits is a multi-fruit cluster.
S006, determining mother branches according to the fruit string type determined in the step S005:
for a single fruit cluster, the branch connected with the fruit is the mother branch;
for the double fruit cluster and the multi-fruit cluster, firstly, in the color image I1The middle making fruit string is combined with the color image I through the perpendicular bisector of the upper and lower bottoms of the outline circumscribed rectangle1With color images I2Performing fusion analysis by color image I1Perpendicular bisector and color image I2Determining the parent branch of the fruit bunch according to the branch tangency condition; the method specifically comprises the following steps: and rotating the perpendicular bisector of the upper and lower bottoms of the outline circumscribed rectangle of the fruit cluster around a point below the perpendicular bisector to one side close to the branches, wherein the branch which is firstly tangent to the perpendicular bisector is the parent branch of the fruit cluster.
S007, acquiring binocular stereo images of fruit cluster mother branches by using a binocular CCD camera, taking a mother branch circumscribed rectangle geometric center point in a left image of the binocular CCD camera as a feature matching point, and searching a point which is closest to the gray value of the feature matching point and enables a normalized cross-correlation function to reach the maximum value in a right image, wherein the specific steps are as follows:
first, a left image P is obtained by a binocular CCD camera1The feature matching point (x, y) of (a) constructs a matching window while the right image P is taken2The point (x + m, y + m) to be matched also constructs a matching window, and measures the correlation degree by normalizing the correlation function, wherein the specific formula is as follows:
Figure BDA0002909356550000061
wherein F (m, n) represents a normalized correlation function; (m, n) represents a position vector of the right image relative to the left image; wpRepresenting a matching window taking the coordinates of the point to be matched as a center; p2(x + m, y + n) represents the gray value of the point to be matched of the right image;
Figure BDA0002909356550000062
representing a gray level mean of the right image matching window; p1(x, y) represents the gray value of the left image feature matching point;
Figure BDA0002909356550000063
representing a gray level mean of the left image matching window;
wherein, the value range of F (m, n) is [ -1,1 ];
when F (m, n) is-1, the feature matching point of the left image is completely unrelated to the point to be matched of the right image;
when F (m, n) is 1, the feature matching point of the left image is completely matched with the point to be matched of the right image, that is, the point to be matched is the point at which the gray value of the feature matching point is closest and the normalized cross-correlation function of the point to be matched reaches the maximum value. Realizing the characteristic matching of the left image and the right image to obtain the geometric central point of the mother branch;
the matching window is typically a 3 x 3 matching window.
Epipolar line correction is needed to be carried out on the left image and the right image before feature matching is carried out on the left image and the right image, so that the epipolar lines of the left image and the right image are in the horizontal direction, even if the optical centers of the left image and the right image are in the same horizontal line; epipolar rectification was performed using the Bouguet epipolar rectification algorithm.
And S008, calculating the space coordinate of the geometric center point of the mother branch by adopting a triangulation principle, and positioning the mother branch.
The fruit cluster type classification and the mother branch determination by using the perpendicular bisector of the through-string contour line circumscribed rectangle are adopted, so that firstly, the interference of branches connected with each fruit can be effectively eliminated, and the recognition precision is improved; secondly, the mother branches of the fruit clusters are quickly identified, so that the identification efficiency is improved, and the identification time is shortened.
According to the method, the monocular camera and the binocular camera are combined to identify the mother branches of the string-shaped fruits, the fruit strings can be accurately and effectively identified, the mother branches of the fruit strings can be positioned, so that the intelligent picking of the string-shaped fruits is realized, the picking efficiency is high, and the labor cost is effectively reduced.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (2)

1. A method for identifying string-type fruit mother branches based on a monocular camera and a binocular camera is characterized in that:
s001, randomly acquiring a plurality of color images including the characteristics of the string-type fruits by adopting a monocular CCD (charge coupled device) camera;
s002, selecting and dividing a plurality of fruit targets and non-fruit targets in the color image, and respectively extracting texture characteristic values and color characteristic values of the fruit targets and the non-fruit targets as positive and negative samples;
s003, training the positive and negative samples by adopting a Support Vector Machine (SVM) to generate a plurality of weak classifiers; then, an AdaBoost algorithm is adopted to construct a strong classifier, the color image acquired by the monocular CCD camera is segmented by the strong classifier, and the fruit target identified from the image is separately stored into a color image I1；
S004, repeating the steps S002-S003 to respectively obtain strong classifiers for identifying branches and leaves; then respectively removing the color image I1The branches and leaves are extracted from the color image and stored into a color image I respectively2And a color image I3；
S005, dividing the fruit clusters into single fruit clusters, double fruit clusters and multi-fruit clusters according to the distribution positions of the fruits, and further determining the color image I1The fruit cluster type of the randomly distributed fruits;
the method specifically comprises the following steps: single fruit bunch: if the Euclidean distance between the geometric center of one fruit and the geometric center of any other fruit is larger than the average diameter of a single fruit, the fruit is a single fruit cluster; double fruit bunch: if the Euclidean distance between the geometric centers of two adjacent fruits is smaller than the sum of the diameters of the two fruits, the two fruits are a double-fruit cluster; multi-fruit clusters: if the Euclidean distance between the geometric centers of any two fruits in more than two fruits is smaller than the sum of the diameters of the two fruits, the group of fruits is a multi-fruit cluster;
s006, determining mother branches according to the fruit string type determined in the step S005:
for a single fruit cluster, the branch connected with the fruit is the mother branch;
for the double fruit cluster and the multi-fruit cluster, firstly, in the color image I1The middle making fruit string is combined with the color image I through the perpendicular bisector of the upper and lower bottoms of the outline circumscribed rectangle1With color images I2Performing fusion analysis by color image I1Perpendicular bisector and color image I2Determining the parent branch of the fruit bunch according to the branch tangency condition;
the method specifically comprises the following steps: rotating the perpendicular bisector of the upper and lower bottoms of the outline circumscribed rectangle of the fruit cluster around a point below the perpendicular bisector to one side close to the branches, wherein the branch which is firstly tangent to the perpendicular bisector is a parent branch of the fruit cluster;
s007, acquiring binocular stereo images of fruit cluster mother branches by using a binocular CCD camera, taking a mother branch circumscribed rectangular geometric central point in a left image of the binocular CCD camera as a feature matching point, searching a point which is closest to a gray value of the feature matching point and enables a normalized cross-correlation function to reach a maximum value in a right image, realizing feature matching, and acquiring a geometric central point of the mother branch;
the method comprises the following specific steps:
first, a left image P is obtained by a binocular CCD camera1The feature matching point (x, y) of (a) constructs a matching window while the right image P is taken2The point (x + m, y + n) to be matched also constructs a matching window, and measures the correlation degree by normalizing the cross-correlation function, wherein the specific formula is as follows:
Figure FDA0003181890870000021
wherein F (m, n) represents a normalized cross-correlation function; (m, n) represents a position vector of the right image relative to the left image; wpRepresenting a matching window taking the coordinates of the point to be matched as a center; p2(x + m, y + n) represents the gray value of the point to be matched of the right image;
Figure FDA0003181890870000022
representing a gray level mean of the right image matching window; p1(x, y) represents the gray value of the left image feature matching point;
Figure FDA0003181890870000031
representing a gray level mean of the left image matching window;
wherein, the value range of F (m, n) is [ -1,1 ];
when F (m, n) is-1, the feature matching point of the left image is completely unrelated to the point to be matched of the right image;
when F (m, n) is 1, completely matching the feature matching point of the left image with the point to be matched of the right image, namely the point to be matched is the point which has the closest gray value of the feature matching point and is the point which enables the normalized cross-correlation function of the point to reach the maximum value;
and S008, finally, calculating the space coordinate of the geometric center point of the parent branch to realize the positioning of the parent branch.
2. The method for identifying string-type fruit mother branches based on the monocular and binocular cameras as claimed in claim 1, wherein: the string-shaped fruit features in the step S001 include fruits, leaves and branches.
CN202110081724.3A 2021-01-21 2021-01-21 Method for identifying string-type fruit mother branches based on monocular camera and binocular camera Active CN112784735B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110081724.3A CN112784735B (en) 2021-01-21 2021-01-21 Method for identifying string-type fruit mother branches based on monocular camera and binocular camera

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110081724.3A CN112784735B (en) 2021-01-21 2021-01-21 Method for identifying string-type fruit mother branches based on monocular camera and binocular camera

Publications (2)

Publication Number Publication Date
CN112784735A CN112784735A (en) 2021-05-11
CN112784735B true CN112784735B (en) 2021-10-01

Family

ID=75758022

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110081724.3A Active CN112784735B (en) 2021-01-21 2021-01-21 Method for identifying string-type fruit mother branches based on monocular camera and binocular camera

Country Status (1)

Country Link
CN (1) CN112784735B (en)

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104636722B (en) * 2015-01-26 2018-02-27 江苏大学 A kind of overlapping fruit quick Tracking Recognition method of picking robot
CN108318494B (en) * 2018-05-15 2019-10-08 华中农业大学 The red online vision detection and classification devices and methods therefor for proposing fruit powder
CN109002851B (en) * 2018-07-06 2021-04-02 东北大学 Fruit classification method based on image multi-feature fusion and application
CN109684941B (en) * 2018-12-07 2021-09-24 华南农业大学 Litchi fruit picking area division method based on MATLAB image processing
CN109711325B (en) * 2018-12-25 2023-05-23 华南农业大学 Mango picking point identification method
CN109784204B (en) * 2018-12-25 2023-04-07 江苏大学 Method for identifying and extracting main fruit stalks of stacked cluster fruits for parallel robot
CN110059573A (en) * 2019-03-22 2019-07-26 浙江大学城市学院 Wild ginseng based on image recognition is classified calibration method
CN111062988B (en) * 2019-11-29 2024-02-13 佛山科学技术学院 Grape pose estimation method based on local point cloud
CN111602517B (en) * 2020-05-28 2021-09-24 华南农业大学 Distributed visual active perception method for string-type fruits and application of distributed visual active perception method
CN111783693A (en) * 2020-07-06 2020-10-16 深圳市多彩汇通实业有限公司 Intelligent identification method of fruit and vegetable picking robot

Also Published As

Publication number Publication date
CN112784735A (en) 2021-05-11

Similar Documents

Publication Publication Date Title
Li et al. Detection of fruit-bearing branches and localization of litchi clusters for vision-based harvesting robots
Wang et al. Localisation of litchi in an unstructured environment using binocular stereo vision
CN112715162B (en) System for intelligent string type fruit of picking
Majeed et al. Apple tree trunk and branch segmentation for automatic trellis training using convolutional neural network based semantic segmentation
CN112990103B (en) String mining secondary positioning method based on machine vision
Puttemans et al. Automated visual fruit detection for harvest estimation and robotic harvesting
Fu et al. Fast detection of banana bunches and stalks in the natural environment based on deep learning
Lv et al. A method to obtain the near-large fruit from apple image in orchard for single-arm apple harvesting robot
Kalampokas et al. Grape stem detection using regression convolutional neural networks
CN113255434B (en) Apple identification method integrating fruit characteristics and deep convolutional neural network
CN112802099A (en) Picking method suitable for string-shaped fruits
CN111445482A (en) Segmentation and identification method of overlapped agaricus bisporus
CN111783693A (en) Intelligent identification method of fruit and vegetable picking robot
CN109684941A (en) One kind picking region partitioning method based on MATLAB image procossing litchi fruits
CN114677674A (en) Apple rapid identification and positioning method based on binocular point cloud
CN114067206A (en) Spherical fruit identification and positioning method based on depth image
CN104992448B (en) The automatic positioning method of robot antisitic defect grape-picking
CN114842337A (en) Fruit picking point identification method based on deep learning and multidimensional information fusion clustering
Shao et al. Research on the tea bud recognition based on improved k-means algorithm
Rahman et al. Identification of mature grape bunches using image processing and computational intelligence methods
CN112784735B (en) Method for identifying string-type fruit mother branches based on monocular camera and binocular camera
Wang et al. A transformer-based mask R-CNN for tomato detection and segmentation
CN113111830A (en) Grape vine winter pruning point detection algorithm
CN111369497B (en) Walking type tree fruit continuous counting method and device
CN115937314B (en) Method for detecting growth posture of oil tea fruits

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant