Citrus fruit yield prediction and adjustment method and system
By combining information on the yield demand, remote sensing images and planting parameters of citrus, a yield prediction model is constructed, and accurate prediction of citrus fruit yield and optimization of planting management is achieved, the problem of citrus yield and quality being sensitive to changes in environmental parameters is solved, yield and quality are improved, and production costs are reduced.
Patent Information
- Application Number
- CN202411864099.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-17
- Publication Date
- 2025-06-06
AI Technical Summary
The yield and quality of citrus fruits are more sensitive to changes in environmental parameters, and it is difficult for the existing technology to achieve accurate yield prediction and planting management optimization.
By obtaining the yield demand of citrus, remote sensing images of citrus fruits, and citrus planting parameters, multi-source information fusion processing is carried out, yield prediction models are constructed, yield predictions, and planting parameters are adjusted according to the prediction results to achieve accurate yield prediction and optimization of planting management.
It improves the prediction accuracy of citrus yield, optimizes planting management strategies, improves fruit yield and quality, and reduces production costs.
Smart Images
Figure CN120106263A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of yield prediction, and more particularly to a method and system for predicting and adjusting the yield of citrus fruits. Background Art
[0002] At present, citrus (Citrus reticulata Blanco), also known as tangerine, orange, tangerine, etc., is a small tree of the genus Citrus in the family Rutaceae. Citrus pulp is sweet and sour, and has multiple functions. It can be processed into juice, jam, fruit cake and other foods, and can also be used as medicine. It is one of the fruits with high demand on consumers' tables.
[0003] However, with the continuous enrichment of agricultural products and the improvement of people's living standards, consumers have higher and higher requirements for the quality of citrus. In this context, producers also seek to increase the yield per unit area while ensuring quality. However, the yield and quality of citrus are sensitive to changes in environmental parameters. Therefore, applying technologies such as sensor detection, remote sensing image processing, and artificial intelligence to agriculture, collecting and remotely processing and analyzing citrus growth information in agricultural environments, and combining agronomic requirements to prompt managers to perform correct management will be conducive to the healthy growth of citrus trees and produce high-quality fruits.
[0004] Therefore, how to provide a citrus fruit yield prediction and adjustment method that can solve the above problems is an issue that technical personnel in this field urgently need to solve. Summary of the invention
[0005] In view of this, the present invention provides a citrus fruit yield prediction and adjustment method and system, which fuses multi-source information from citrus planting to harvesting and combines it with relevant planting requirements to provide a yield adjustment strategy.
[0006] In order to achieve the above object, the present invention adopts the following technical solution:
[0007] A method for predicting and adjusting citrus fruit yield comprises the following steps:
[0008] Obtain citrus production requirements, citrus fruit remote sensing images, and citrus planting parameters;
[0009] Processing the citrus fruit remote sensing image to obtain the predicted yield of the citrus and the growth period of the citrus;
[0010] The citrus planting parameters are adjusted according to the predicted yield and the yield demand.
[0011] Preferably, the specific process of obtaining the predicted yield of citrus includes:
[0012] Preprocessing and feature extraction are performed on the citrus fruit remote sensing image and the citrus planting parameters to obtain corresponding image feature vectors and environmental parameter feature vectors;
[0013] A yield prediction model is constructed, and the image feature vector and the environmental parameter feature vector are input into the yield prediction model for prediction to obtain the corresponding predicted yield.
[0014] Preferably, the specific process of adjusting the citrus planting parameters according to the predicted yield and the yield demand includes:
[0015] When the predicted yield is greater than or equal to the yield requirement, continue to monitor citrus planting parameters and citrus fruit remote sensing images;
[0016] When the difference between the predicted yield and the yield requirement is less than or equal to a first preset threshold, adjusting the planting conditions of citrus fruits in combination with the citrus fruit remote sensing image and citrus planting parameters;
[0017] When the difference between the predicted yield and the yield requirement is greater than a first preset threshold, short-term and long-term analyses are performed on the citrus planting environment to obtain an adjustment plan.
[0018] Preferably, the specific process of adjusting the planting conditions of citrus fruits in combination with the citrus fruit remote sensing image and citrus planting parameters includes:
[0019] Processing the remote sensing image of the citrus fruit to obtain the growth stage of the citrus fruit;
[0020] The optimal planting operation parameters corresponding to the growth period are obtained through big data, and the citrus planting parameters are adjusted according to the optimal planting operation parameters.
[0021] Preferably, when the difference between the predicted output and the output demand is greater than a first preset threshold, the specific processing process includes:
[0022] Constructing a citrus yield impact index system, and dividing the citrus planting parameters according to the citrus yield impact index system to obtain corresponding short-term impact parameters and long-term impact parameters;
[0023] The citrus fruit remote sensing image is processed, and the adjustment sequence of the short-term impact parameter and the long-term impact parameter is determined according to the processing result of the citrus fruit remote sensing image.
[0024] Preferably, the specific processing process of determining the adjustment order of the short-term impact parameters and the long-term impact parameters according to the processing result of the citrus fruit remote sensing image includes:
[0025] Processing the citrus fruit remote sensing image to determine whether there is fruit in the citrus fruit remote sensing image;
[0026] If it does not exist, adjust the long-term impact parameters first, and then adjust the short-term impact parameters.
[0027] Preferably, the specific processing process of determining the adjustment order of the short-term impact parameters and the long-term impact parameters according to the processing result of the citrus fruit remote sensing image also includes:
[0028] Processing the citrus fruit remote sensing image to determine whether there is fruit in the citrus fruit remote sensing image;
[0029] If it exists, adjust the short-term impact parameters first.
[0030] The present invention also provides a citrus fruit yield prediction and adjustment system, comprising:
[0031] An acquisition module is used to obtain citrus production requirements, citrus fruit remote sensing images, and citrus planting parameters;
[0032] A processing module, used for processing the citrus fruit remote sensing image to obtain the predicted yield of the citrus and the growth period of the citrus;
[0033] An adjustment module is used to adjust the citrus planting parameters according to the predicted yield and the yield demand.
[0034] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method and system for predicting and adjusting citrus fruit yield, which has the following beneficial effects:
[0035] 1. Improve prediction accuracy: Through comprehensive analysis of citrus production demand, citrus fruit remote sensing images, and citrus planting parameters, accurate prediction of citrus production can be achieved;
[0036] 2. Optimize planting management: According to the difference between the prediction results and the yield demand of citrus, the citrus planting parameters are divided and adjusted in time to optimize the planting management strategy and effectively increase the yield;
[0037] 3. Improve yield and quality: Through accurate forecasting and timely adjustments, ensure that citrus production meets market demand while improving fruit quality.
[0038] 4. Reduce production costs: By optimizing planting management, reducing unnecessary inputs, and reducing production costs. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.
[0040] Figure 1 An overall flow chart of a citrus fruit yield prediction and adjustment method provided by the present invention;
[0041] Figure 2 A structural principle block diagram of a citrus fruit yield prediction and adjustment system provided by the present invention. DETAILED DESCRIPTION
[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0043] See also Figure 1 As shown, the embodiment of the present invention discloses a method for predicting and adjusting citrus fruit yield, comprising the following steps:
[0044] Obtaining citrus production requirements, citrus fruit remote sensing images, and citrus planting parameters, where citrus planting parameters may include: climate parameters (such as precipitation, extreme weather events, temperature, humidity, light intensity, etc.), planting parameters (such as irrigation conditions, fertilization conditions, pest and disease management parameters, planting levels, etc.), soil condition parameters (such as soil moisture content, soil quality, etc.);
[0045] Process the remote sensing images of citrus fruits to obtain the predicted yield and growth period of citrus fruits;
[0046] Adjust citrus planting parameters based on predicted yield and yield demand.
[0047] In a specific embodiment, the specific process of obtaining the predicted yield of citrus includes:
[0048] Preprocessing and feature extraction of citrus fruit remote sensing images and citrus planting parameters to obtain corresponding image feature vectors and environmental parameter feature vectors, wherein the citrus fruit remote sensing image preprocessing process may include geometric precision correction and image registration, cropping and binarization, and the citrus planting parameter preprocessing process may include normalization processing;
[0049] Construct a yield prediction model, input the image feature vector and the environmental parameter feature vector into the yield prediction model for prediction, and obtain the corresponding predicted yield. The yield prediction model can use a comprehensive model of a neural network model and a decision tree to achieve yield prediction. The combination of the two models can better improve the prediction accuracy.
[0050] In a specific embodiment, the specific process of adjusting citrus planting parameters according to predicted yield and yield demand includes:
[0051] When the predicted yield is greater than or equal to the yield demand, continue to monitor citrus planting parameters and citrus fruit remote sensing images;
[0052] When the difference between the predicted yield and the yield requirement is less than or equal to a first preset threshold, adjusting the planting conditions of citrus fruits in combination with the citrus fruit remote sensing image and citrus planting parameters;
[0053] When the difference between the predicted yield and the yield demand is greater than a first preset threshold, short-term and long-term analyses are performed on the citrus planting environment to obtain an adjustment plan.
[0054] Specifically, when the predicted yield is greater than or equal to the yield demand, the actual yield of citrus under the same period or similar planting conditions in multiple past stages is obtained through big data and compared with the predicted yield. When the difference exceeds the first preset threshold, it is recorded as the number of mutations; when it is less than or equal to the first preset threshold, it is recorded as normal. The mutation probability is calculated. When the mutation probability is less than or equal to the corresponding threshold, the above result is determined to be credible, thereby improving the accuracy of data judgment.
[0055] In a specific embodiment, the specific process of adjusting the planting conditions of citrus fruits in combination with the citrus fruit remote sensing image and the citrus planting parameters includes:
[0056] Process the remote sensing images of citrus fruits to obtain the growth period of citrus fruits;
[0057] The optimal planting operation parameters corresponding to the growth period are obtained through big data, and the citrus planting parameters are adjusted according to the optimal planting operation parameters.
[0058] In a specific embodiment, when the difference between the predicted output and the output demand is greater than the first preset threshold, the specific processing process includes:
[0059] Construct an index system affecting citrus yield, and divide citrus planting parameters according to the index system, and obtain corresponding short-term and long-term impact parameters;
[0060] The remote sensing images of citrus fruits are processed, and the adjustment order of short-term influencing parameters and long-term influencing parameters are determined according to the processing results of the remote sensing images of citrus fruits.
[0061] In a specific embodiment, the specific processing process of determining the adjustment order of the short-term impact parameters and the long-term impact parameters according to the processing results of the citrus fruit remote sensing image includes:
[0062] Processing the remote sensing image of citrus fruit to determine whether there is fruit in the remote sensing image of citrus fruit;
[0063] If it does not exist, adjust the long-term influencing parameters first, and then adjust the short-term influencing parameters. This means that the citrus is in the early stages of growth and is far from maturity. Since the citrus still has a long time to adapt and adjust, you can consider solving the long-term factors first and then adjusting the short-term factors, which is more conducive to the adjustment of citrus production.
[0064] In a specific embodiment, the specific processing process of determining the adjustment order of the short-term impact parameters and the long-term impact parameters according to the processing results of the citrus fruit remote sensing image also includes:
[0065] Processing the remote sensing image of citrus fruit to determine whether there is fruit in the remote sensing image of citrus fruit;
[0066] If it exists, adjust the short-term impact parameters first.
[0067] Specifically, the remote sensing image of citrus fruit is processed, and when there is fruit in the image, the remote sensing image of citrus fruit is processed again to obtain the size and color of the fruit, and the maturity of the citrus fruit is determined according to the size and color. If any one of the size and color meets the requirements of the parameters of the initial fruiting period, the short-term impact parameters are adjusted first and then the long-term impact parameters are adjusted. If any one of the size and color meets the requirements of the parameters of the peak fruiting period and the late fruiting period, only the short-term impact parameters are adjusted. Since citrus needs to go through multiple stages and growth processes. At each stage, corresponding management measures and technical means need to be taken. Through the above adjustment process, the healthy growth, high yield and high quality of citrus trees can be ensured, and the efficiency and accuracy of yield adjustment can be improved.
[0068] See also Figure 2 As shown, an embodiment of the present invention further provides a system using a citrus fruit yield prediction and adjustment method as described in any one of the above embodiments, comprising:
[0069] An acquisition module is used to obtain citrus production requirements, citrus fruit remote sensing images, and citrus planting parameters;
[0070] A processing module is used to process the remote sensing images of citrus fruits to obtain the predicted yield of citrus fruits and the growth period of citrus fruits;
[0071] The adjustment module is used to adjust citrus planting parameters according to predicted yield and yield demand.
[0072] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.
[0073] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for predicting and adjusting citrus fruit yield, characterized in that: The following steps are involved: Obtain citrus production requirements, citrus fruit remote sensing images, and citrus planting parameters; Processing the citrus fruit remote sensing image to obtain the predicted yield of the citrus and the growth period of the citrus; The citrus planting parameters are adjusted according to the predicted yield and the yield demand.
2. A method for predicting and adjusting citrus fruit yield according to claim 1, characterized in that: The specific process of obtaining the predicted yield of citrus includes: Preprocessing and feature extraction are performed on the citrus fruit remote sensing image and the citrus planting parameters to obtain corresponding image feature vectors and environmental parameter feature vectors; A yield prediction model is constructed, and the image feature vector and the environmental parameter feature vector are input into the yield prediction model for prediction to obtain the corresponding predicted yield.
3. A method for predicting and adjusting citrus fruit yield according to claim 1, characterized in that: The specific process of adjusting the citrus planting parameters according to the predicted yield and the yield demand includes: When the predicted yield is greater than or equal to the yield requirement, continue to monitor citrus planting parameters and citrus fruit remote sensing images; When the difference between the predicted yield and the yield requirement is less than or equal to a first preset threshold, adjusting the planting conditions of citrus fruits in combination with the citrus fruit remote sensing image and citrus planting parameters; When the difference between the predicted yield and the yield requirement is greater than a first preset threshold, short-term and long-term analyses are performed on the citrus planting environment to obtain an adjustment plan.
4. A method for predicting and adjusting citrus fruit yield according to claim 3, characterized in that: The specific process of adjusting the planting conditions of citrus fruits in combination with the citrus fruit remote sensing image and citrus planting parameters includes: Processing the remote sensing image of the citrus fruit to obtain the growth stage of the citrus fruit; The optimal planting operation parameters corresponding to the growth period are obtained through big data, and the citrus planting parameters are adjusted according to the optimal planting operation parameters.
5. A method for predicting and adjusting citrus fruit yield according to claim 4, characterized in that: When the difference between the predicted output and the output demand is greater than the first preset threshold, the specific processing process includes: Constructing a citrus yield impact index system, and dividing the citrus planting parameters according to the citrus yield impact index system to obtain corresponding short-term impact parameters and long-term impact parameters; The citrus fruit remote sensing image is processed, and the adjustment sequence of the short-term impact parameter and the long-term impact parameter is determined according to the processing result of the citrus fruit remote sensing image.
6. A method for predicting and adjusting citrus fruit yield according to claim 4, characterized in that: The specific processing process of determining the adjustment order of the short-term impact parameters and the long-term impact parameters according to the processing results of the citrus fruit remote sensing image includes: Processing the citrus fruit remote sensing image to determine whether there is fruit in the citrus fruit remote sensing image; If it does not exist, adjust the long-term impact parameters first, and then adjust the short-term impact parameters.
7. A method for predicting and adjusting citrus fruit yield according to claim 4, characterized in that: The specific processing process of determining the adjustment order of the short-term impact parameters and the long-term impact parameters according to the processing result of the citrus fruit remote sensing image also includes: Processing the citrus fruit remote sensing image to determine whether there is fruit in the citrus fruit remote sensing image; If it exists, adjust the short-term impact parameters first.
8. A system using the citrus fruit yield prediction and adjustment method according to any one of claims 1 to 7, characterized in that: include: An acquisition module is used to obtain citrus production requirements, citrus fruit remote sensing images, and citrus planting parameters; A processing module, used for processing the citrus fruit remote sensing image to obtain the predicted yield of the citrus and the growth period of the citrus; An adjustment module is used to adjust the citrus planting parameters according to the predicted yield and the yield demand.