CN114155214A - Information management system for agricultural planting park - Google Patents
Information management system for agricultural planting park Download PDFInfo
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- CN114155214A CN114155214A CN202111407523.4A CN202111407523A CN114155214A CN 114155214 A CN114155214 A CN 114155214A CN 202111407523 A CN202111407523 A CN 202111407523A CN 114155214 A CN114155214 A CN 114155214A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Mining
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
- G06T2207/30188—Vegetation; Agriculture
Abstract
The utility model provides an agricultural planting garden information management system, relates to agricultural information management technical field, acquires the problem of plant growth information inefficiency to the mode that adopts artificial observation among the prior art, includes: the system comprises an image acquisition module, a growth state detection module, a disposal module, a disease detection module and a path generation module, wherein the image acquisition module is used for acquiring an image of plants in a plantation; the growth state detection module identifies the current growth state of the plants in the plantation according to the plant images in the plantation by using the trained neural network; the treatment module gives a plant current treatment suggestion to the current growth state of the plants in the planting park according to the pre-stored information; the disease detection module identifies whether plant diseases and insect pests exist in plants in the plantation according to the plant images in the plantation by using the trained neural network; the path generation module is used for generating a navigation road map reaching the plantation. The application can efficiently obtain the growth information of the plant.
Description
Technical Field
The invention relates to the technical field of agricultural information management, in particular to an information management system for an agricultural plantation.
Background
Currently, agricultural cultivation is to cultivate and shape plants to create agricultural landscape to achieve relevant effects. The agricultural and farming can play a role in absorbing harmful gases and releasing clean air in the aspect of improving the ecological environment. The cultivation has a self-evident importance for agricultural cultivation, and only with good quality plant resources, sufficient resources can be provided for agricultural cultivation, and agricultural cultivation with high ecological value and rich ornamental value can be designed. In order to make plants thrive, it is necessary to apply fertilizer, prune, supply nutrient solution, and the like to the plants at different growth periods. In the prior art, the plant growth information is generally acquired by adopting a manual observation mode, so that the efficiency is very low.
Disclosure of Invention
The purpose of the invention is: aiming at the problem that the efficiency of acquiring plant growth information by adopting a manual observation mode in the prior art is low, the agricultural planting park information management system is provided.
The technical scheme adopted by the invention to solve the technical problems is as follows:
an information management system for an agricultural plantation farm, comprising: an image acquisition module, a growth state detection module, a disposal module, a disease detection module and a path generation module,
the image acquisition module is used for acquiring an image of the plant in the plantation;
the growth state detection module identifies the current growth state of the plants in the plantation according to the plant images in the plantation by using the trained neural network;
the treatment module gives a plant current treatment suggestion to the current growth state of the plants in the planting park according to the pre-stored information;
the disease detection module identifies whether plant diseases and insect pests exist in plants in the plantation according to the plant images in the plantation and by using the trained neural network;
the path generation module is used for generating a navigation route map reaching the plantation.
The invention has the beneficial effects that: according to the method and the system, the growth information of the plants can be efficiently obtained, the corresponding processing information can be given according to the obtained growth information of the plants, a route map can be generated, an optimal path can be provided for a grower aiming at a large-area plantation, the time spent on the path is reduced, and the grower is prevented from getting lost.
Drawings
Fig. 1 is a flow chart of the present application.
Detailed Description
It should be noted that, in the present invention, the embodiments disclosed in the present application may be combined with each other without conflict.
The first embodiment is as follows: the present embodiment will be described in detail below, and an agricultural plantation farm information management system according to the present embodiment includes: an image acquisition module, a growth state detection module, a disposal module, a disease detection module and a path generation module,
the image acquisition module is used for acquiring an image of the plant in the plantation;
the growth state detection module identifies the current growth state of the plants in the plantation according to the plant images in the plantation by using the trained neural network;
the treatment module gives a plant current treatment suggestion to the current growth state of the plants in the planting park according to the pre-stored information;
the disease detection module identifies whether plant diseases and insect pests exist in plants in the plantation according to the plant images in the plantation and by using the trained neural network;
the path generation module is used for generating a navigation route map reaching the plantation.
The second embodiment is as follows: the embodiment is a further description of the first specific embodiment, and the difference between the first specific embodiment and the second specific embodiment is that the specific process for acquiring the plant image in the plantation by the image acquisition module is as follows:
the method comprises the following steps: presetting imaging proportion mu in monitoring image1；
Step two: acquiring a current monitoring image, and preprocessing the monitoring image;
step three: carrying out proportional transformation and Hough transformation on the preprocessed image according to the angle model and the imaging proportion to obtain a corrected image with the actual size;
step four: and carrying out binarization processing on the image with the actual size.
The third concrete implementation mode: the second embodiment is further described, and the difference between the second embodiment and the first embodiment is that the preprocessing includes graying and neighborhood average smoothing denoising.
The fourth concrete implementation mode: this embodiment is a further description of the second embodiment, and is different from the second embodiment in that the binarization processing is performed by an Otsu adaptive threshold algorithm.
The fifth concrete implementation mode: the embodiment is further described with respect to the first embodiment, and the difference between the embodiment and the first embodiment is that the training step of the neural network in the growth state detection module is as follows:
the method comprises the following steps: acquiring an image of each stage in the plant growth process, extracting the characteristics of the image of each stage, and acquiring the corresponding color of each stage;
step two: carrying out growth state information marking on the image of each stage;
step three; and training the neural network by using the image of each stage, the corresponding color of each stage and the marking information.
The sixth specific implementation mode: this embodiment mode is a further description of a fifth embodiment mode, and the difference between this embodiment mode and the fifth embodiment mode is that the features include: textural features and shape features.
The seventh embodiment: the embodiment is further described with respect to the first embodiment, and the difference between the first embodiment and the second embodiment is that the specific steps of the route generation module generating the navigation route map to the plantation are as follows:
the method comprises the following steps: adding the starting point into the open set;
step two: judging whether the open set is empty, if so, ending, and if not, executing the step three;
step three: finding out a node U with the minimum evaluation function value in the open set, taking the node U as a current node to be processed, and finally moving the node U into the close set;
step four: judging whether the current node U is an end point, if so, moving from the end point to a starting point along a father node to further obtain a path, and then executing the step nine, otherwise, continuing to execute the step five;
step five: judging whether the adjacent nodes of the current node U can be expanded or not, if yes, continuing to execute the step six, and if not, turning to the step two;
step six: connecting the current node U and the terminal, projecting the expandable node of the node U in the connecting direction, reserving the expandable forward sub-node projected on the connecting line, and forming a set V;
step seven: for each child node in the set V, if the node is in a close set, no processing is performed, if the node is not in an open set, the node is added into the open set, a node U which expands the node is defined as a parent node of the node, an evaluation function of the node is calculated, if the node is in the open set, whether the evaluation function value of the node is smaller than the original evaluation function value is checked, and if the evaluation function value of the node is smaller than the original evaluation function value, the evaluation function and the parent node of the expandable node in the open set are updated;
step eight: turning to the third step;
step nine: for the path obtained in the step four, the path between every two nodes in the path is divided into three equal parts, and the path points obtained after path division are taken as a set, wherein A is ═ a1,...,ai,...,aNIn which a is1As a starting point, aNIs the end point;
step ten: defining c as a current path point subscript, setting c to be 1, defining m as a path point subscript to be connected, setting m to be N, defining i as a new path point subscript, setting i to be 2, and finally defining the obtained new path point set as B to be { B ═ B {1,...,bi,...,bN}；
Step eleven: a is tocAnd amAre connected in a straight line lcm；
Step (ii) ofTwelve: judgment of lcmIf the obstacle is passed, making m equal to m-1, and turning to step eleven, if the obstacle is not passed, connecting acAnd ac+1To obtain a straight line lc,c+1Executing a step thirteen;
step thirteen: judgment of lcmAnd lc,c+1If the new paths are not on the same straight line, the step B is executed to make the new path set point B in the path point set Bi＝ac，bi+1＝amI +2, c m, executing a step fourteen;
fourteen steps: judging the current point acWhether or not it is the end point aNIf not, making m equal to N, and turning to the step eleven; if it is the end point, let bi＝aNB are connected in increasing order of subscriptiAnd obtaining the path.
It should be noted that the detailed description is only for explaining and explaining the technical solution of the present invention, and the scope of protection of the claims is not limited thereby. It is intended that all such modifications and variations be included within the scope of the invention as defined in the following claims and the description.
Claims (7)
1. The utility model provides an agricultural plantation park information management system which characterized in that includes: an image acquisition module, a growth state detection module, a disposal module, a disease detection module and a path generation module,
the image acquisition module is used for acquiring an image of the plant in the plantation;
the growth state detection module identifies the current growth state of the plants in the plantation according to the plant images in the plantation by using the trained neural network;
the treatment module gives a plant current treatment suggestion to the current growth state of the plants in the planting park according to the pre-stored information;
the disease detection module identifies whether plant diseases and insect pests exist in plants in the plantation according to the plant images in the plantation and by using the trained neural network;
the path generation module is used for generating a navigation route map reaching the plantation.
2. The information management system for agricultural plantation park according to claim 1 wherein the specific process of the image acquisition module for acquiring the plant images in the plantation park is as follows:
the method comprises the following steps: presetting imaging proportion mu in monitoring image1；
Step two: acquiring a current monitoring image, and preprocessing the monitoring image;
step three: carrying out proportional transformation and Hough transformation on the preprocessed image according to the angle model and the imaging proportion to obtain a corrected image with the actual size;
step four: and carrying out binarization processing on the image with the actual size.
3. The agricultural plantation park information management system of claim 2 wherein the pre-processing includes graying and neighborhood averaging denoising.
4. The agricultural plantation park information management system of claim 2 wherein the binarization process is performed by Otsu adaptive threshold algorithm.
5. The agricultural plantation park information management system of claim 1 wherein the training step of the neural network in the growth status detection module is:
the method comprises the following steps: acquiring an image of each stage in the plant growth process, extracting the characteristics of the image of each stage, and acquiring the corresponding color of each stage;
step two: carrying out growth state information marking on the image of each stage;
step three; and training the neural network by using the image of each stage, the corresponding color of each stage and the marking information.
6. The agricultural plantation park information management system of claim 5 wherein the characteristics include: textural features and shape features.
7. The agricultural plantation park information management system of claim 1 wherein the specific steps of the path generation module generating a navigation roadmap to the plantation park are:
the method comprises the following steps: adding the starting point into the open set;
step two: judging whether the open set is empty, if so, ending, and if not, executing the step three;
step three: finding out a node U with the minimum evaluation function value in the open set, taking the node U as a current node to be processed, and finally moving the node U into the close set;
step four: judging whether the current node U is an end point, if so, moving from the end point to a starting point along a father node to further obtain a path, and then executing the step nine, otherwise, continuing to execute the step five;
step five: judging whether the adjacent nodes of the current node U can be expanded or not, if yes, continuing to execute the step six, and if not, turning to the step two;
step six: connecting the current node U and the terminal, projecting the expandable node of the node U in the connecting direction, reserving the expandable forward sub-node projected on the connecting line, and forming a set V;
step seven: for each child node in the set V, if the node is in a close set, no processing is performed, if the node is not in an open set, the node is added into the open set, a node U which expands the node is defined as a parent node of the node, an evaluation function of the node is calculated, if the node is in the open set, whether the evaluation function value of the node is smaller than the original evaluation function value is checked, and if the evaluation function value of the node is smaller than the original evaluation function value, the evaluation function and the parent node of the expandable node in the open set are updated;
step eight: turning to the third step;
step nine: for the path obtained in the step four, the path between every two nodes in the path is divided into three equal parts, and the path points obtained after path division are taken as a set, wherein A is ═ a1,...,ai,...,aNIn which a is1As a starting point, aNIs the end point;
step ten: defining c as a current path point subscript, setting c to be 1, defining m as a path point subscript to be connected, setting m to be N, defining i as a new path point subscript, setting i to be 2, and finally defining the obtained new path point set as B to be { B ═ B {1,...,bi,...,bN}；
Step eleven: a is tocAnd amAre connected in a straight line lcm；
Step twelve: judgment of lcmIf the obstacle is passed, making m equal to m-1, and turning to step eleven, if the obstacle is not passed, connecting acAnd ac+1To obtain a straight line lc,c+1Executing a step thirteen;
step thirteen: judgment of lcmAnd lc,c+1If the new paths are not on the same straight line, the step B is executed to make the new path set point B in the path point set Bi＝ac，bi+1＝amI +2, c m, executing a step fourteen;
fourteen steps: judging the current point acWhether or not it is the end point aNIf not, making m equal to N, and turning to the step eleven; if it is the end point, let bi＝aNB are connected in increasing order of subscriptiAnd obtaining the path.
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