CN114742288A - Intelligent prediction method for chenopodium quinoa ear-sprouting yield - Google Patents
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Abstract
The invention provides an intelligent prediction method for the ear-sprouting yield of quinoa, which belongs to the technical field of yield prediction and comprises the following steps: collecting environmental information such as temperature, humidity, illumination time and illumination intensity in the planting area, detecting nutrient information of soil in the planting area, and combining the nutrient information and the environmental information into a supply system. Scanning quinoa particles in the planting area, acquiring the size parameter of the current quinoa particles, deducing the number of the quinoa particles in the planting area, and calculating the total quantity of the quinoa in the planting area according to the number and the size parameter. And generating a checking coefficient according to the empirical coefficient of the chenopodium quinoa yield and the supply system, and predicting the target yield of the planting area according to the checking coefficient and the chenopodium quinoa total amount. The target yield obtained by the intelligent prediction method for the chenopodium quinoa ear-sprouting yield is based on actual starting, the data result is accurate, the reference is strong, and powerful data support is provided for the development of agriculture.
Description
Technical Field
The invention belongs to the technical field of yield prediction, and particularly relates to an intelligent prediction method for the extraction yield of quinoa.
Background
The planting and cultivating area of quinoa is increased year by year in the three provinces of east China, and the quinoa is generally sown in the early 5 months and harvested in the middle 9 days or at the end 9 months. The traditional quinoa is generally planted in a single ridge and single row, the ridge width range is 60-65 cm, the quinoa plant is tall and big, the lateral branches are developed, and the nutritive value is high.
The yield of agricultural products is closely related to the daily life of people, the past yield data and the historical data of a series of influence factors are utilized to fully mine the trend of the data and the influence relationship and degree of each factor, and a certain method and skill are utilized to predict and prejudge the future yield, so that the method is beneficial to the country, the producer and the consumer to better judge the economic situation and make a correct decision.
Because many quinoa ears grow on quinoa seedlings and each quinoa ear is combined with a plurality of approximately spherical quinoa particles, a lot of troubles are caused for predicting the yield of quinoa planting areas, the existing yield prediction method is mostly to carry out general judgment according to varieties and environmental factors, and the final data referential property is poor and the data reliability is not high because the self condition of the planted quinoa particles is not combined.
Disclosure of Invention
The invention aims to provide an intelligent chenopodium quinoa ear-sprouting yield prediction method, and aims to solve the problems of poor data referential property and low data credibility during chenopodium quinoa yield prediction.
In order to achieve the purpose, the invention adopts the technical scheme that: the provided intelligent prediction method for the chenopodium quinoa ear-sprouting yield comprises the following steps:
collecting environmental information such as temperature, humidity, illumination time, illumination intensity and the like in a planting area, detecting nutrient information of soil in the planting area, and combining the nutrient information and the environmental information into a supply system;
scanning the quinoa particles in the planting area, acquiring the size parameter of the quinoa particles at present, deducing the number of the quinoa particles in the planting area, and calculating the total quantity of the quinoa in the planting area according to the number and the size parameter;
and generating a checking coefficient according to the empirical model of the chenopodium quinoa yield and the supply system, and predicting the target yield of the planting area according to the checking coefficient and the chenopodium quinoa total amount.
In a possible implementation manner, the scanning the quinoa particles in the planting region, and acquiring the current size parameter of the quinoa particles includes:
dividing the planting area into a plurality of blocks, and scanning the quinoa particles in the blocks in sequence by using acquisition equipment;
the acquisition equipment transmits the scanned information to an upper computer, and the upper computer determines the size parameters according to the received information.
In one possible implementation, the sequentially scanning, by a collection device, the quinoa particles within the block comprises:
the acquisition equipment is stabilized at a specific angle at one side of the corresponding block;
the collecting device scans the quinoa particles through pictures, videos and structured light.
In one possible implementation manner, the obtaining the current size parameter of the quinoa particles includes:
the collecting device obtains a plurality of characteristic points of the chenopodium quinoa grain edge, and determines the size parameters such as the volume of the chenopodium quinoa grain through the plurality of characteristic points.
In one possible implementation, the inferring the number of quinoa particles within the planting region comprises:
the upper computer determines the number of quinoa ears on quinoa seedlings and the size of the quinoa ears by means of pictures, videos and the like, deduces the number of quinoa particles on the quinoa ears, and adds the obtained number of quinoa particles in each block to finally determine the number.
In one possible implementation, the upper computer deducing the number of the quinoa particles on the quinoa ear comprises:
according to the variety of quinoa, the number of quinoa particles of which the quinoa ear faces one side of the collecting device is determined, and the total number of quinoa particles on the quinoa ear is deduced according to the size of the quinoa ear.
In one possible implementation manner, the generating the check coefficient according to the empirical model of the chenopodium quinoa yield and the supply system includes:
setting corresponding influence factors for each component in the supply system, wherein the empirical model comprises empirical parameters aiming at each component, comparing the value of each component in the supply system with the value of each component in the empirical parameters, and obtaining the check coefficient from the empirical model according to the influence factors.
In one possible implementation, the comparing the values of the components in the supply system with the values of the components in the empirical parameters and obtaining the check coefficient from the empirical model according to the influence factor includes:
predicting a supply system of each section from the current time point of the quinoa to the harvest period, comparing the predicted supply system with the empirical parameters, determining section parameters in each section according to the compared result and the influence factors, and finally obtaining the checking coefficient according to the determined section parameters.
In a possible implementation manner, the determining, by combining the compared result with the influence factor, an interval parameter in each of the interval sections, and finally deriving the check coefficient from the determined plurality of interval parameters includes:
within the same interval, subtracting the value of each component in the supply system from the value of the corresponding component in the empirical parameters, multiplying a plurality of subtraction results by the corresponding influence factors, adding the multiplied results to obtain a coefficient deviation, and adding the coefficient deviation and the empirical model to obtain the interval parameters; and multiplying the interval parameters to obtain the check coefficient.
In a possible implementation manner, the predicting the target yield of the planting area through the checking coefficient and the chenopodium quinoa total amount includes:
and multiplying the checking coefficient and the total quinoa amount to obtain the target yield.
The intelligent prediction method for the chenopodium quinoa ear-sprouting yield has the beneficial effects that: compared with the prior art, the method for intelligently predicting the sprouting yield of the quinoa is characterized in that the environmental information including temperature, humidity, illumination time, illumination intensity and the like in a planting area is collected, and the nutrient information of soil in the planting area needs to be detected. Scanning the quinoa particles in the planting area, deducing the quantity of the quinoa particles, and calculating the total quantity of the quinoa in the planting area according to the quantity and the size parameters of the quinoa particles. And generating a checking coefficient according to the empirical model and the supply system, and predicting the target yield according to the checking coefficient and the chenopodium quinoa total amount.
In the application, environmental information such as temperature, humidity, illumination time and illumination intensity and nutrient information of soil form a supply system, and the supply system is a necessary condition for growth of plants such as quinoa and is an important reference factor for predicting the yield of the quinoa. Through scanning and deducing the chenopodium quinoa particles, the size parameters and the number of the chenopodium quinoa particles are determined, so that accurate data support is provided for yield prediction. The supply system needs to generate a check coefficient according to a previous empirical model, the target yield obtained through the check coefficient and the chenopodium quinoa total amount is based on actual starting, the data result is accurate, the reference is strong, and powerful data support is provided for the development of agriculture.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of an intelligent prediction method for the swarming yield of quinoa according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, the intelligent prediction method for the amount of the ear-sprouting of quinoa provided by the present invention will now be described. The intelligent prediction method for the chenopodium quinoa ear-sprouting yield comprises the following steps:
collecting environmental information such as temperature, humidity, illumination time and illumination intensity in the planting area, detecting nutrient information of soil in the planting area, and combining the nutrient information and the environmental information into a supply system.
Scanning quinoa particles in the planting area, acquiring the size parameter of the current quinoa particles, deducing the number of the quinoa particles in the planting area, and calculating the total quantity of the quinoa in the planting area according to the number and the size parameter.
And generating a checking coefficient according to the empirical model of the chenopodium quinoa yield and the supply system, and predicting the target yield of the planting area according to the checking coefficient and the chenopodium quinoa total amount.
The intelligent prediction method for the chenopodium quinoa ear-sprouting yield has the beneficial effects that: compared with the prior art, the method for intelligently predicting the sprouting yield of the quinoa is characterized in that the environmental information including temperature, humidity, illumination time, illumination intensity and the like in a planting area is collected, and the nutrient information of soil in the planting area needs to be detected. Scanning the quinoa particles in the planting area, deducing the quantity of the quinoa particles, and calculating the total quantity of the quinoa in the planting area according to the quantity and the size parameters of the quinoa particles. And generating a checking coefficient according to the empirical model and the supply system, and predicting the target yield according to the checking coefficient and the chenopodium quinoa total amount.
In the application, environmental information such as temperature, humidity, illumination time and illumination intensity and nutrient information of soil form a supply system, and the supply system is a necessary condition for growth of plants such as quinoa and is an important reference factor for predicting the yield of the quinoa. By scanning and deducing the quinoa particles, the size parameters and the number of the quinoa particles are determined, so that accurate data support is provided for yield prediction. The supply system needs to generate a check coefficient according to the previous empirical model, the target yield obtained through the check coefficient and the chenopodium quinoa total amount is based on actual starting, the data result is accurate, the reference is strong, and powerful data support is provided for the development of agriculture.
In some embodiments of the intelligent predicting method for the extraction yield of quinoa provided by the application, scanning quinoa particles in a planting area, and obtaining the size parameters of the current quinoa particles comprises:
the planting area is divided into a plurality of blocks, and the quinoa particles in the blocks are scanned by the collecting equipment in sequence.
The acquisition equipment transmits the scanned information to the upper computer, and the upper computer determines the size parameters according to the received information.
Because the areas for growing the quinoa are more, and the growing quinoa particles on each quinoa seedling are more, the scanning of the quinoa particles is difficult, and the quinoa seedlings swing along with wind due to the interference of factors such as wind and the like in the surrounding environment, so that the scanning of the quinoa particles is an almost impossible thing. Because the computing power of the current chip is remarkably improved, in order to complete the size determination work timely and efficiently, the planting area needs to be divided at first, the current method is to divide the planting area into a plurality of blocks with equal areas according to a certain sequence, and the plurality of blocks are continuously arranged according to the certain sequence. It should be noted that, in order to be able to collect chenopodium quinoa dimension information of a specific angle of a specific block, an unmanned aerial vehicle and other devices are usually needed, and a coverage area of a collecting device carried by the unmanned aerial vehicle is limited, so that the area of each block needs to be determined according to the specific model and performance adaptability of the collecting device when the block is actually divided.
In some embodiments of the present disclosure, the method for intelligently predicting the veranda citrifolia heading yield, the scanning, by a collection device, of the veranda citrifolia particles in a block in sequence includes:
the acquisition device is stabilized at a particular angle to one side of the corresponding block.
The collection device scans quinoa particles through pictures, video and structured light.
Collecting device is used for obtaining the three-dimensional information of quinoa granule, and unmanned aerial vehicle is used for making collecting device stabilize the specific position in corresponding block, and the information that collecting device acquireed passes through wireless network transmission to the host computer, accomplishes the affirmation of quinoa granule size with the help of the powerful data processing ability of host computer, when the data bulk of gathering is great, can backup the information of collecting device collection earlier, then handles in proper order.
The collecting device can collect a plurality of high-resolution pictures, the high-resolution pictures are shot according to a certain sequence, and the specific size parameters of the quinoa particles are obtained through synthesis of the pictures at different moments. At this moment, the stability of the distance between the collecting device and the quinoa particles needs to be ensured, and the problems of image amplification and image reduction are avoided. The collecting device can also collect a section of video information, and the upper computer determines the size of the quinoa particles shot in the video information.
In some embodiments of the intelligent chenopodium quinoa ear-sprouting yield prediction method provided by the present application, obtaining the size parameters of the current chenopodium quinoa particles comprises:
the collecting device obtains a plurality of characteristic points of the chenopodium quinoa grain edge, and determines the size and other size parameters of the chenopodium quinoa grain through the plurality of characteristic points.
It should be noted that there are many quinoa particles on quinoa seedlings, and if each quinoa particle is scanned in a high-precision and all-around manner, very high requirements are put forward on the precision of the acquisition device and the processing capacity of the upper computer, and the processing efficiency is low due to the adoption of the method. In order to solve the problems, the quinoa is of an approximate spherical structure, so that the size parameters of the quinoa can be obtained only by determining a plurality of important characteristic points of quinoa particles. Therefore, when the collecting equipment is actually applied, only a few points at the edge of the chenopodium quinoa needs to be determined, and the circle center and the volume of the chenopodium quinoa particles can be determined through the points.
In practical application, the collection equipment transmits detection waves to the block, the detection waves can be 3D structured light, and the collection equipment scans quinoa particles and detects characteristic points of several different positions of the quinoa particles so as to calculate the volume of the quinoa particles.
In some embodiments of the intelligent prediction method for the veratrum heading yield provided by the present application, the step of inferring the number of the veratrum particles in the planting area comprises:
the upper computer determines the number of quinoa ears on the quinoa seedlings and the size of the quinoa ears by using methods such as pictures, videos and the like, the upper computer deduces the number of quinoa particles on the quinoa ears, and the number of the quinoa particles obtained in each block is added to determine the number finally.
A plurality of quinoa ears grow on one quinoa seedling, a plurality of quinoa particles are clustered on each quinoa ear, the whole quinoa ear can be approximately seen as a conical structure, if all quinoa particles on the whole quinoa ear can be scanned, the accuracy of final yield prediction can be increased undoubtedly, but the method firstly improves higher requirements on acquisition equipment, and meanwhile, due to shielding among the quinoa ears, part of quinoa particles cannot be detected, so that all detection becomes impossible.
In order to improve the efficiency of the whole operation, several quinoa particles at specific positions can be selected on each quinoa ear, the number of the quinoa particles bonded on one quinoa ear is calculated according to the size of the quinoa ear, and the total amount of the quinoa particles on the quinoa seedling can be calculated by the method on the basis of not scanning all the quinoa particles, so that the workload of data processing is greatly reduced.
In order to solve the problems, the collecting device scans chenopodium quinoa grains at a specific position on one chenopodium quinoa spike based on the growth characteristics of the chenopodium quinoa seedlings. The radius of the chenopodium quinoa particles on the chenopodium quinoa ear is gradually increased from the bottom to the top, so the chenopodium quinoa particles at the top, the middle part and the bottom are scanned by the collecting device in the application, the size of the chenopodium quinoa particles at other positions is calculated, and finally the yield of the chenopodium quinoa ear is determined.
In some embodiments of the intelligent chenopodium quinoa ear yield prediction method provided by the application, the upper computer deduces the number of chenopodium quinoa particles on the chenopodium quinoa ear, including:
and determining the number of quinoa particles on the side of the quinoa ear facing the collecting equipment according to the variety of the quinoa, and deducing the total number of the quinoa particles on the quinoa ear according to the size of the quinoa ear.
The sizes of the quinoa ears at different positions on one quinoa seedling can be different, in order to realize accurate estimation on quinoa particles of the whole quinoa seedling, after the approximate size of the quinoa particles is determined, the upper computer approximately calculates the number of the quinoa seedling according to the information obtained by the acquisition equipment and experience and the variety of the planted quinoa, and the yield can be obtained by multiplying the calculated number of the quinoa particles by the detected volume of the quinoa particles.
In some embodiments of the intelligent chenopodium quinoa ear-sprouting yield prediction method provided by the application, generating a check coefficient according to an empirical model of chenopodium quinoa yield and a supply system includes:
and setting corresponding influence factors for each component in the supply system, wherein the empirical model comprises empirical parameters aiming at each component, comparing the value of each component in the supply system with the value of each component in the empirical parameters, and obtaining a check coefficient from the empirical model according to the influence factors.
For convenience of explanation, only the factors of temperature, humidity, illumination time, illumination intensity and nutrient information influencing the growth of the quinoa are set to determine, and the coefficients of the temperature, humidity, illumination time, illumination intensity and nutrient information are preliminarily set to be 0.2, 0.1, 0.3, 0.2 and 0.2 respectively. According to past experience, when there is a difference between actually planted quinoa and a value corresponding to a historical empirical model, that is, an empirical model, taking temperature as an example, when the actual temperature is 5 ° higher than that in the empirical model, 5 is multiplied by 0.2 to finally obtain 1, and then 1 is added on the basis of the original empirical model when determining an actual checking coefficient. Note that this method is only used to briefly explain the idea of calculation, and particularly when the temperature is too high, the yield of quinoa will decrease instead.
In some embodiments of the present invention, the method for intelligently predicting the heading yield of quinoa includes the steps of comparing the values of the components in the supply system with the values of the components in the empirical parameters, and obtaining the check coefficient from the empirical model according to the influence factor:
predicting the supply system of each section from the current time point of the quinoa to the harvest period, comparing the predicted supply system with empirical parameters, determining section parameters in each section according to the compared result and influence factors, and finally obtaining a checking coefficient according to the determined section parameters.
In order to accurately predict the target yield, a supply system from the current prediction date to the maturity of chenopodium quinoa needs to be predicted, the average level of temperature, humidity, illumination time and illumination intensity of each section needs to be checked by matching with a meteorological department at the moment, and then the predicted supply system is compared with the temperature, humidity, illumination intensity and illumination time corresponding to an empirical model, so that data support is provided for the determination of the target yield.
The volume change rates of the quinoa particles at different periods are different, and in order to accurately predict the final quinoa yield, it is necessary to determine which stage the present quinoa particles are in and predict the supply system of the subsequent section. Firstly, the growth rate of the same variety of quinoa under the approximate external environment can be known by looking up data and other ways, the growth rate can be used as an empirical model, and then the influence coefficient of each factor in the supply system on the target yield is determined according to experience and related experiments.
In actual operation, nutrient information is easy to control, but temperature, humidity, illumination intensity and illumination time cannot be accurately mastered, and particularly actual temperature is greatly changed within a period of time after detection is finished, so that target yield prediction is inaccurate.
In order to solve the above problem, taking temperature as an example, not only the change in temperature from the time of sowing to the time of detection completion to the reference standard but also the change in temperature from the time of detection completion to the time of growth completion should be considered in practical use.
In some embodiments of the intelligent chenopodium quinoa ear-sprouting yield prediction method provided by the application, the comparison result is combined with the influence factor to determine the interval parameters in each interval, and finally obtaining the checking coefficient according to the determined multiple interval parameters includes:
within the same interval, subtracting the value of each component in the supply system from the value of the corresponding component in the empirical parameters, multiplying a plurality of subtraction results by corresponding influence factors, adding the multiplied results to obtain a coefficient deviation, and adding the coefficient deviation and the empirical model to obtain an interval parameter; and multiplying the plurality of interval parameters to obtain a check coefficient.
In this application, need carry out the detection many times to the chenopodium quinoa granule, the growth rate of the volume of chenopodium quinoa granule of different periods can have the difference, consequently when actual operation, at first need set for a reference standard, this reference standard is the chenopodium quinoa of same variety and the similar preceding output change of feed system, this reference standard contains the parameter at different district section chenopodium quinoa, through with the feed system of reality and reference standard also experience model compare, can obtain the check coefficient finally, obtain the target output of fitting according to check coefficient and chenopodium quinoa total amount. The growth rate of the quinoa particles is divided into several stages, and the influence of a supply system on the quinoa in each stage is different, so that in actual analysis, a checking parameter needs to be comprehensively considered from several sections.
Taking temperature as an example, since the change of temperature of each block section has an influence on the growth of quinoa, when determining the target yield, the influence quantities of the respective block sections need to be added, and then a coefficient deviation is obtained.
In some embodiments of the intelligent chenopodium quinoa ear-sprouting yield prediction method provided by the application, predicting the target yield of the planting area through the checking coefficient and the chenopodium quinoa total amount comprises:
and multiplying the checking coefficient by the total amount of the chenopodium quinoa to obtain the target yield.
After the current quinoa volume is determined by the acquisition equipment, the final target yield can be obtained by multiplying the determined checking coefficient and the total quinoa volume.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The intelligent prediction method for the chenopodium quinoa ear-sprouting yield is characterized by comprising the following steps:
collecting environmental information such as temperature, humidity, illumination time, illumination intensity and the like in a planting area, detecting nutrient information of soil in the planting area, and combining the nutrient information and the environmental information into a supply system;
scanning the quinoa particles in the planting area, acquiring the size parameter of the quinoa particles at present, deducing the number of the quinoa particles in the planting area, and calculating the total quantity of the quinoa in the planting area according to the number and the size parameter;
and generating a checking coefficient according to the empirical coefficient of the chenopodium quinoa yield and the supply system, and predicting the target yield of the planting area according to the checking coefficient and the total chenopodium quinoa yield.
2. The intelligent chenopodium quinoa spilt yield prediction method of claim 1, wherein the scanning of the chenopodium quinoa particles in the planting region to obtain the current size parameters of the chenopodium quinoa particles comprises:
dividing the planting area into a plurality of blocks, and scanning the quinoa particles in the blocks in sequence by using acquisition equipment;
the acquisition equipment transmits the scanned information to an upper computer, and the upper computer determines the size parameters according to the received information.
3. The intelligent chenopodium quinoa spilt yield prediction method of claim 2, wherein the sequentially scanning the chenopodium quinoa particles in the block by a collection device comprises:
the acquisition equipment is stabilized at a specific angle at one side of the corresponding block;
the collecting device scans the quinoa particles through pictures, videos and structured light.
4. The intelligent chenopodium quinoa heading yield prediction method of claim 2, wherein the obtaining of the current size parameters of the chenopodium quinoa particles comprises:
the collecting device obtains a plurality of characteristic points of the chenopodium quinoa grain edge, and determines the size parameters such as the volume of the chenopodium quinoa grain through the plurality of characteristic points.
5. The intelligent chenopodium quinoa heading yield prediction method of claim 2, wherein the inferring the number of chenopodium quinoa particles in the planting region comprises:
the upper computer determines the number of quinoa ears on quinoa seedlings and the size of the quinoa ears by means of pictures, videos and the like, deduces the number of quinoa particles on the quinoa ears, and adds the obtained number of quinoa particles in each block to finally determine the number.
6. The intelligent chenopodium quinoa ear-sprouting yield prediction method of claim 5, wherein the step of deducing the number of chenopodium quinoa particles on the chenopodium quinoa ear by the upper computer comprises the following steps:
according to the variety of quinoa, determining the number of quinoa particles of which the quinoa ear faces one side of the collecting device, and deducing the total number of quinoa particles on the quinoa ear according to the size of the quinoa ear.
7. The intelligent prediction method for the lamb's-quarters wheat ear yield according to claim 1, characterized in that said generating a check coefficient according to the empirical coefficient of lamb's-quarters wheat yield and said supply system includes:
setting corresponding influence factors for all components in the supply system, wherein the empirical coefficients comprise empirical parameters aiming at all the components, comparing the values of all the components in the supply system with the values of all the components in the empirical parameters, and obtaining the check coefficients from the empirical coefficients according to the influence factors.
8. The intelligent chenopodium quinoa ear-snapping yield prediction method of claim 7, wherein the comparing the values of the components in the supply system with the values of the components in the empirical parameters and obtaining the check coefficient from the empirical coefficient according to the influence factor comprises:
predicting a supply system of each section from the current time point of the quinoa to the harvest period, comparing the predicted supply system with the empirical parameters, determining section parameters in each section according to the compared result and the influence factors, and finally obtaining the checking coefficient according to the determined section parameters.
9. The method of claim 8, wherein the comparing the results with the influence factors to determine interval parameters within each of the intervals, and the deriving the checking coefficient from the determined interval parameters comprises:
within the same interval, subtracting the value of each component in the supply system from the value of the corresponding component in the empirical parameters, multiplying a plurality of subtraction results by the corresponding influence factors, adding the multiplied results to obtain a coefficient deviation, and adding the coefficient deviation and the empirical model to obtain the interval parameters; and multiplying the interval parameters to obtain the check coefficient.
10. The intelligent quinoa heading yield prediction method according to claim 1, wherein the predicting of the target yield of the planting area through the check coefficient and the quinoa total amount comprises:
and multiplying the checking coefficient and the total quinoa amount to obtain the target yield.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102954816A (en) * | 2012-01-13 | 2013-03-06 | 北京盈胜泰科技术有限公司 | Crop growth monitoring method |
| CN107229999A (en) * | 2017-05-31 | 2017-10-03 | 深圳前海弘稼科技有限公司 | Method, system, computer device and readable storage medium for predicting crop yield |
| CN113222991A (en) * | 2021-06-16 | 2021-08-06 | 南京农业大学 | Deep learning network-based field ear counting and wheat yield prediction |
-
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102954816A (en) * | 2012-01-13 | 2013-03-06 | 北京盈胜泰科技术有限公司 | Crop growth monitoring method |
| CN107229999A (en) * | 2017-05-31 | 2017-10-03 | 深圳前海弘稼科技有限公司 | Method, system, computer device and readable storage medium for predicting crop yield |
| CN113222991A (en) * | 2021-06-16 | 2021-08-06 | 南京农业大学 | Deep learning network-based field ear counting and wheat yield prediction |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117541084A (en) * | 2024-01-10 | 2024-02-09 | 河北省科技创新服务中心 | Method and system for predicting yield of quinoa in grouting period |
| CN117541084B (en) * | 2024-01-10 | 2024-05-10 | 河北省科技创新服务中心 | Method and system for predicting yield of quinoa in grouting period |
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