CN114910147A - Internet of things-based maturity and yield estimation method and device - Google Patents
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Abstract
The application discloses a maturity and yield estimation method and device based on the Internet of things, and the method comprises the following steps: acquiring the number of fruits falling on a collector within a preset time; and determining the fruit maturity of the plant to be detected according to the number of the fruits falling onto the collector within a preset time and a preset fruit falling rate. According to the method and the device for estimating the maturity and the yield based on the Internet of things, the maturity and the yield of the fruits can be automatically identified without manual participation, the accuracy and the efficiency of identifying the maturity of the fruits are improved, a reference is provided for selecting the harvesting time point with the maximum value, and the labor cost can be effectively reduced.
Description
Technical Field
The application relates to the technical field of fruit maturity detection, in particular to a maturity and yield estimation method and device based on the Internet of things.
Background
In recent years, the internet of things is more mature, and the ubiquitous connection between objects and people is realized through various possible network accesses, so that the intelligent perception, identification and management of the objects and the processes are realized. The internet of things is an information bearer based on the internet, a traditional telecommunication network and the like, and all common physical objects which can be independently addressed form an interconnected network. At present, the technology of the Internet of things is widely applied to monitoring the influence of soil on plant growth, evaluating fruit quality, evaluating fruit harvesting maturity and the like.
The quality index of the fruits in the process from development to maturity is dynamic change, the maturity is used as an index for measuring the maturity of the fruits and the optimal cold chain transportation picking time node, the quality and the taste of the fruits are determined to a great extent, if the maturity is insufficient, the appearance quality of the fruits is poor, and the variety characteristics of the internal quality cannot be reflected. If the maturity is too high, the taste of the fruit becomes light, and the fruit is easy to soften and bruise, which affects the logistics transportation and storability of the fruit, and the fruit yield, flower bud development and fruit quality of the next year are affected if the fruit is harvested too early or too late. In the traditional method, the maturity of the fruit during fruit collection is judged according to experience by taking the fruit peel color as a standard, more important factors such as fruit diameter, single fruit weight, sweetness and the like are not considered, and wrong judgment is probably generated due to the morning and evening of the expression of different peel pigment genes.
Disclosure of Invention
In order to solve the technical problems or at least partially solve the technical problems, the present application provides a maturity and yield estimation method based on the internet of things, including:
acquiring the number of fruits falling on a collector within a preset time;
and determining the fruit maturity of the plant to be detected according to the number of the fruits falling on the collector within a preset time and a preset fruit falling rate.
Preferably, the acquiring the number of fruits falling on the collector within the preset time includes:
acquiring the actual number of fruits falling on a collector within a preset time;
determining the number of mature fruits from the actual number of fruits falling on a collector within a preset time;
the method for determining the fruit maturity of the plant to be detected according to the number of fruits falling on the collector within the preset time and the preset fruit falling rate comprises the following steps:
and determining the fruit maturity of the plant to be detected according to the number of mature fruits falling on the collector within a preset time and a preset fruit falling rate.
Preferably, the determining the number of ripe fruits from the actual number of fruits dropped on the harvester within the preset time comprises:
obtaining the impact times of a single fruit on the collector;
and determining the number of the mature fruits from the actual number of the fruits falling on the collector within the preset time according to the impact times.
Preferably, the determining the number of ripe fruits from the actual number of fruits falling on the collector within a preset time according to the number of impacts comprises:
counting the number of fruits falling on the collector within a preset time, wherein the number of the fruits with the impact frequency of 1 is used as the number of mature fruits.
Preferably, the determining the number of ripe fruits from the actual number of fruits dropped on the harvester within the preset time comprises:
counting the number of fruits falling on the collector within a preset time, wherein the weight of each fruit is greater than or equal to the preset weight, and the number is used as the number of mature fruits.
The embodiment of the present application further provides a yield estimation method based on the internet of things, where the yield estimation method includes the steps of the maturity estimation method described in the embodiment of the present application, and further includes:
after the fruit maturity of the plant to be detected is determined to reach the preset maturity, the average weight of the fruits falling on the collector within the preset time is obtained;
and determining the yield of the plant to be detected according to the average weight of the fruits.
Preferably, the relationship between the average weight of fruit F and the yield Q of the plants to be detected satisfies:
the number of the fruits falling on the collector within the preset time is N, the average distance between the impact position of the falling fruits on the collector and the support rod of the collector is d, a is a constant, and W is a wind force value.
The embodiment of the application also provides a maturity and yield estimation device based on the Internet of things, which comprises a collector, a camera and a controller;
the collector is used for receiving the fallen fruits;
the camera is used for collecting an image of a plant to be detected;
the controller is used for determining the number of fruits falling onto the collector within a preset time according to the image of the plant to be detected, and determining the fruit maturity of the plant to be detected according to the number of the fruits falling onto the collector within the preset time and a preset fruit falling rate.
Preferably, the collector comprises a collecting plate, a supporting rod and a pressure sensor, and the bottom of the supporting rod is arranged on the ground;
the collecting plate is a plane collecting plate, and one end of the collecting plate is fixed on the supporting rod; the collecting plate is used for receiving the fallen fruits;
the pressure sensor is electrically connected with the collecting plate and is used for collecting the weight of fruits falling from the plant to be detected;
the camera is arranged at the top of the supporting rod.
Preferably, the device further comprises a wind direction sensor and a wind speed sensor.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages:
according to the method and the device for estimating the maturity and the yield based on the Internet of things, the maturity and the yield of the fruits can be automatically identified without manual participation, so that the accuracy and the efficiency of identifying the maturity of the fruits are improved, and the labor cost can be effectively reduced.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a schematic flowchart of a maturity estimation method based on the internet of things according to an embodiment of the present application;
fig. 2 is another schematic flow chart of a maturity estimation method based on the internet of things according to an embodiment of the present application;
fig. 3 is another schematic flow chart of a maturity estimation method based on the internet of things according to an embodiment of the present application;
fig. 4 is another schematic flow chart of a maturity estimation method based on the internet of things according to the embodiment of the present application;
fig. 5 is a schematic structural diagram of an internet-of-things-based maturity and yield estimation device according to an embodiment of the present disclosure.
Detailed Description
In order that the above-mentioned objects, features and advantages of the present application may be more clearly understood, the solution of the present application will be further described below. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways than those described herein; it is to be understood that the embodiments described in this specification are only some embodiments of the present application and not all embodiments.
Fig. 1 is a schematic flow chart of a method for estimating maturity based on the internet of things according to an embodiment of the present application, and as shown in fig. 1, the method for estimating maturity based on the internet of things includes:
step 101: and acquiring the quantity of the fruits falling on the collector within a preset time.
In step 101, the number of fruits falling on a collector within a preset time of a plant to be detected is obtained.
Step 102: and determining the fruit maturity of the plant to be detected according to the number of the fruits falling on the collector within a preset time and a preset fruit falling rate.
The fruit drop rate refers to the number of fruits dropped by the plant to be detected in a preset time; when the fruits on the plant to be detected are mature, the fruits on the plant to be detected can naturally fall off; and when the plant to be detected is in the mature period, the fruit drop rate of the plant to be detected in the mature period is obviously higher than that of the plant to be detected in the mature period because more mature fruits and more corresponding dropped fruits exist. Therefore, the fruit maturity of the plant to be detected can be determined according to the number of fruits falling on the collector within the preset time and the preset fruit falling rate of the plant to be detected.
The maturity estimation method based on the Internet of things can realize automatic identification of the maturity of fruits under the condition of no manual participation, not only improves the accuracy and efficiency of identification of the maturity of fruits, but also can effectively reduce labor cost.
In some embodiments, for example, the preset fruit drop rate may be a fruit drop rate of the plant to be detected in a mature period, so that the number of fruits dropped on the harvester by the plant to be detected in a preset time can be compared with the preset fruit drop rate, and the fruit maturity of the plant to be detected can be determined. When the number of fruits falling onto the collector of the plant to be detected within a preset time is larger than or equal to a preset fruit falling rate, determining that the fruits of the plant to be detected are mature; and when the number of the fruits of the plant to be detected falling on the collector within the preset time is less than the preset fruit falling rate, determining that the fruits of the plant to be detected are not mature.
In some embodiments, step 101: obtain the fruit quantity that drops on the collector in the preset time, for example still include: acquiring the actual number of fruits falling on a collector within a preset time; determining the number of mature fruits from the actual number of fruits falling on a collector within a preset time; step 102: determining the fruit maturity of the plant to be detected according to the number of fruits falling on the collector within a preset time and a preset fruit falling rate, for example, further comprising: and determining the fruit maturity of the plant to be detected according to the number of mature fruits falling on the collector within a preset time and a preset fruit falling rate.
In some embodiments, the number of collectors set in a plant park is determined according to the area of the plant park in which the plants to be detected are located. Preferably, the number of collectors is one fifth of the total area of the plant garden.
In some embodiments, the number of collectors located in a southern area of the plant garden is greater than the number of collectors located in a northern area of the plant garden. According to the method, the position of the collector is placed, through test, the precision of fruit maturity detection can reach more than 86%, and compared with other placing methods, the effect is more obvious.
Since the plants are grown south-facing, it is preferred that the number of collectors provided in the southern area of the plant garden is three quarters of the total number of collectors and the number of collectors provided in the northern area of the plant garden is one quarter of the total number of collectors. Wherein, the collector is concentrated in the southern area, namely the area towards the sun, and the yield can be reduced by 7 percent.
The above is only an implementation manner of the present application, and an implementation manner of the present application may also be as shown in fig. 2, where fig. 2 is another schematic flow diagram of a maturity estimation method based on the internet of things provided by the embodiment of the present application, and referring to fig. 2, the maturity estimation method based on the internet of things includes:
step 201: and acquiring the actual number of the fruits falling onto the collector within a preset time.
The actual fruit number is the number of all fruits that fall on the harvester within a preset time.
Step 202: the number of ripe fruits is determined from the actual number of fruits falling on the harvester within a preset time.
In step 202, it is determined which of the actual number of fruits dropped onto the harvester within a preset time are ripe and which are unripe.
Step 203: and determining the fruit maturity of the plant to be detected according to the number of mature fruits falling on the collector within a preset time and a preset fruit falling rate.
In step 203, the number of mature fruits falling on the collector within a preset time is compared with a preset fruit falling rate to determine the fruit maturity of the plant to be detected. When the number of mature fruits of the plant to be detected falling on the collector within a preset time is larger than or equal to a preset fruit falling rate, determining that the fruits of the plant to be detected are mature; and when the number of mature fruits of the plant to be detected falling on the collector within the preset time is less than the preset fruit falling rate, determining that the fruits of the plant to be detected are immature.
The maturity estimation method based on the internet of things determines the fruit maturity of a plant to be detected by comparing the number of mature fruits falling on a collector with a preset fruit falling rate within a preset time, avoids the actual fruit falling on the collector from containing non-mature fruits, compares the fruit falling rate of the mature fruits with the preset fruit falling rate, can further improve the accuracy of fruit maturity detection, and enables the fruit maturity detection result to better accord with the fruit maturity of the actual plant to be detected.
The above is only an implementation manner of the present application, and an implementation manner of the present application may also be as shown in fig. 3, where fig. 3 is another schematic flow diagram of a maturity estimation method based on the internet of things provided by the embodiment of the present application, and with reference to fig. 3, the maturity estimation method based on the internet of things includes:
step 301: and acquiring the actual number of fruits falling on the collector within a preset time.
Step 302: obtaining the impact times of the single fruit on the collector.
The fruits can be bounced when falling onto the collector, and some fruits can be bounced to the ground after falling onto the collector for 1 time; some fruits are bounced after falling onto the collector and impacting 1 time, and then fall onto the collector again and are bounced, so that the number of times of impacting a single fruit on the collector can be 1 or more.
Step 303: and determining the number of the mature fruits from the actual number of the fruits falling on the collector within the preset time according to the impact times.
The fruit falling on the collector can be bounced because some fruits are not mature; compare with ripe fruit, immature fruit weight is lighter, and consequently immature fruit drops on the collector after, by the height of bounceing lower, falls on the collector again easily, and then whether the fruit is ripe can influence its striking number of times with the collector, so can be according to striking number of times, confirm ripe fruit quantity in the actual fruit quantity that drops on the collector in the time of predetermineeing.
Step 304: and determining the fruit maturity of the plant to be detected according to the number of mature fruits falling on the collector within a preset time and a preset fruit falling rate.
According to the maturity estimation method based on the Internet of things, the number of ripe fruits is determined from the actual number of fruits falling onto a collector within the preset time according to the impact frequency of a single fruit on the collector.
In some embodiments, step 303: the determining the number of ripe fruits from the actual number of fruits falling onto the collector within a preset time according to the number of impacts, for example, further includes: counting the number of fruits falling on the collector within a preset time, wherein the number of the fruits with the impact frequency of 1 is used as the number of mature fruits. When the impact frequency of the fruit falling on the collector and the collector is 1, the weight of the falling fruit is heavier, only the mature fruit can reach the weight, the fruit can be bounced by the collector to be higher, the fruit can be bounced for 1 time, and the fruit can be bounced to fall on the ground, so that the fruit which is impacted for 1 time can be judged to be the mature fruit.
The above is only an embodiment of the present application, and the embodiment of the present application may also be as shown in fig. 4, where fig. 4 is another schematic flow diagram of a maturity estimation method based on the internet of things provided in the embodiment of the present application, and with reference to fig. 4, the maturity estimation method based on the internet of things includes:
step 401: and acquiring the actual number of fruits falling on the collector within a preset time.
Step 402: counting the number of fruits falling on the collector within a preset time, wherein the weight of each fruit is greater than or equal to the preset weight, and the number is used as the number of mature fruits.
The weight of the mature fruit is larger than that of the immature fruit, and the fruit can be judged to be mature when the weight of a single fruit reaches a certain weight. For example, the preset weight may be set to the weight of the ripe fruit, and the fruit having a single fruit weight equal to or greater than the preset weight may be regarded as a ripe fruit and the number of ripe fruits may be obtained.
Step 403: and determining the fruit maturity of the plant to be detected according to the number of mature fruits falling on the collector within a preset time and a preset fruit falling rate.
According to the maturity estimation method based on the Internet of things, the quantity of ripe fruits is determined from the actual quantity of fruits falling onto a collector within the preset time according to the weight of a single fruit, the method is high in accuracy, the quantity of ripe fruits in the actual quantity of fruits can be automatically detected without depending on the traditional appearance conditions such as the color and the size of the fruits, and scientific basis is provided for detecting the maturity of the fruits.
In some embodiments, the obtaining the number of fruits falling on the collector within the preset time further includes: judging whether the fruits falling on the collector are mature fruits or not; after determining that the fruits falling on the collector are mature fruits, recording the fruits falling on the collector as effective fruits; and counting the number of effective fruits in a preset time, and determining the number of the effective fruits as the number of mature fruits. The method for determining the fruit maturity of the plant to be detected according to the number of fruits falling on the collector within the preset time and the preset fruit falling rate comprises the following steps: and determining the fruit maturity of the plant to be detected according to the number of mature fruits falling on the collector within a preset time and a preset fruit falling rate.
According to the maturity estimation method based on the Internet of things, whether dropped fruits are mature fruits is judged firstly, after the dropped fruits are determined to be mature fruits, the dropped fruits are recorded as effective fruits, the number of the effective fruits in the preset time is counted, and the number of the effective fruits is the number of the mature fruits. Therefore, whether the dropped fruits are mature fruits or not can be judged, whether the dropped fruits are effective fruits or not can be recorded, and then the number of the effective fruits is counted, so that the number of the mature fruits can be obtained easily. The method is simple to operate and easy to realize, and various detection methods which are easy to realize are provided for detecting the maturity of the fruits.
The embodiment of the present application further provides a yield estimation method based on the internet of things, where the yield estimation method includes the steps of the maturity estimation method described in the embodiment of the present application, and further includes: after the fruit maturity of the plant to be detected is determined to reach the preset maturity, the average weight of the fruits falling on the collector within the preset time is obtained; and determining the yield of the plant to be detected according to the average weight of the fruits.
Specifically, after the fruit maturity of the plant to be detected reaches the preset maturity, the plant to be detected is mature at the moment, the yield of the plant to be detected can be estimated, and the estimated yield of the plant to be detected is accurate at the moment, so that the average weight of the fruits falling on the collector within the preset time can be obtained after the fruit maturity of the plant to be detected reaches the preset maturity; from the average weight of the fruits, the yield of the plant to be detected can be determined.
Preferably, the relation between the average weight of fruit F and the yield Q of the plant to be detected satisfies:
wherein N is the number of the fruits falling on the collector within the preset time, d is the average distance between the impact position of the falling fruits on the collector and the support rod of the collector, a is 0.22-0.68, and W is the wind force value.
According to the yield estimation method based on the Internet of things, the yield of the plant to be detected can be determined according to the average weight of the fruits falling on the collector within the preset time, the calculation method is simple and accurate, the wind power value is taken into account, the fruits falling caused by external factors such as wind power are eliminated, the yield of the plant to be detected can be accurately calculated, and a scientific basis is provided for the yield calculation of the plant to be detected.
In some embodiments, the yield of the plant to be detected may be determined, for example, based on the number of mature fruits falling on the harvester within a preset time and the average weight of the mature fruits. This can further improve the yield calculation accuracy of the plants to be tested.
The embodiment of the application also provides a maturity and yield estimation device based on the Internet of things, which comprises a collector, a camera and a controller; the collector is used for receiving the fallen fruits; the camera is used for collecting the plant image to be detected; the controller is used for determining the number of fruits falling onto the collector within a preset time according to the image of the plant to be detected, and determining the fruit maturity of the plant to be detected according to the number of the fruits falling onto the collector within the preset time and a preset fruit falling rate.
The maturity and yield estimation device based on the Internet of things can realize the automatic detection of fruit maturity, has a simple structure and low equipment cost, can realize wide application, has high accuracy rate for detecting the fruit maturity, does not need to depend on manpower to detect the maturity, and is convenient for realize agricultural automatic production.
The controller can also be used for acquiring the average weight of fruits falling on the collector within a preset time after determining that the maturity of the fruits of the plant to be detected reaches a preset maturity; and determining the yield of the plant to be detected according to the average weight of the fruits.
Specifically, when the controller determines that the fruit maturity of the plant to be detected reaches the preset maturity, it indicates that the plant to be detected is mature at this time, and at this time, the yield of the plant to be detected can be estimated, and the estimated yield of the plant to be detected is accurate, so that the controller can be used for acquiring the average weight of the fruits falling onto the collector within the preset time after determining that the fruit maturity of the plant to be detected reaches the preset maturity; from the average weight of the fruit, the yield of the plant to be tested can be determined.
Preferably, the relation between the average weight of fruit F and the yield Q of the plant to be detected satisfies:
wherein N is the number of the fruits falling on the collector within the preset time, d is the average distance between the impact position of the falling fruits on the collector and the support rod of the collector, a is 0.22-0.68, and W is a wind force value.
As shown in fig. 5, fig. 5 is a schematic structural diagram of an internet-of-things-based maturity and yield estimation apparatus provided in an embodiment of the present application, and referring to the structure of fig. 5, the apparatus includes a collector, a camera 501, and a controller 502; the collector comprises a collecting plate 503, a supporting rod 504 and a pressure sensor 505, wherein the bottom of the supporting rod 504 is arranged on the ground 506; the collecting plate 503 is a planar collecting plate, and one end of the collecting plate 503 is fixed on the supporting rod 504; the collecting plate 503 is used for receiving the fallen fruits 507; the pressure sensor 505 is electrically connected with the collecting plate 503 and is used for collecting the weight of the fruits 507 dropped by the plants to be detected; the camera 501 is arranged at the top of the supporting rod 504, and the camera 501 is used for collecting images of plants to be detected. The controller 503 is configured to determine the number of fruits falling onto the collector within a preset time according to the image of the plant to be detected, and determine the fruit maturity of the plant to be detected according to the number of fruits falling onto the collector within the preset time and a preset fruit falling rate.
The controller 503 may also be configured to obtain an average weight of fruits falling onto the collector within a preset time after determining that the maturity of the fruits of the plant to be detected reaches a preset maturity; and determining the yield of the plant to be detected according to the average weight of the fruits.
In some embodiments, a wireless communication module 508 is disposed on the supporting rod 504 of the collector, and the wireless communication module 508 may be in communication connection with the controller 502 to implement wireless signal interaction between the collector and the controller. The wireless communication module may be, for example, an antenna, a wireless connector, a signal interactor, and the like, which is not limited in this application. This wireless communication module can set up the optional position at the collector, and the position that wireless communication module set up adjusts according to the structure of actual collector, and this application is not limited to this.
In some embodiments, as shown in fig. 5, the apparatus further comprises a wind direction sensor 509 and a wind speed sensor 510, wherein the wind direction sensor 509 and the wind speed sensor 510 are in communication with the controller 502 to enable wireless signal interaction between the wind direction sensor, the wind speed sensor and the controller. For example, the wind direction sensor 509 and the wind speed sensor 510 are both disposed on a support column 511, and the support column 511 is disposed on the ground 506. For example, the wind direction sensor 509 and the wind speed sensor 510 are provided with a wireless communication module, which can realize the communication between the wind direction sensor 509 and the wind speed sensor 510 and the controller 502. The wireless communication module may be, for example, an antenna, a wireless connector, a signal interactor, and the like, which is not limited in this application.
In some embodiments, the controller may be, for example, a smart electronic device such as a cell phone, a computer, a tablet, etc.
The maturity and the output estimation device based on thing networking that this application embodiment provided, in order to eliminate the error that fruit maturity detected, set up wind direction sensor and wind speed sensor, for example in hilly areas, every 10 mu area plant garden set up a wind direction sensor and wind speed sensor can.
In some embodiments, the yield of the plant to be detected may be determined, for example, based on the number of mature fruits falling on the harvester within a preset time and the average weight of the mature fruits. This further improves the accuracy of the yield calculation of the plants to be tested. As shown in fig. 5, d is an average distance between an impact position of a mature fruit on a collector and a support rod of the collector, and a yield estimation method based on the internet of things is provided according to an embodiment of the present application, wherein a relationship between an average weight F of the mature fruit and a yield Q of a plant to be detected satisfies:
wherein N is the number of the fallen mature fruits, d is the average distance between the impact position of the mature fruits on the collector and the supporting rod of the collector, a is a constant, and W is the wind force value. Preferably, a is 0.22-0.68.
The maturity and yield estimation device based on thing networking that this application embodiment provided, can calculate the air force value through this wind direction sensor and wind velocity transducer, according to this wind force value, can avoid when calculating to detect plant yield because the inaccurate condition of ripe fruit quantity statistics that external factors such as wind-force caused, for example some fruits are not because of the nature after ripe drops, probably because local wind-force influence, for example blown and dropped, and this kind of fruit quantity that drops because the wind blows can influence the statistics and the calculation of actual ripe fruit quantity, but be provided with wind direction sensor and wind velocity transducer in this application embodiment, the yield calculation method that combines this application embodiment to provide, can be fine avoid the harmful effects that this problem caused.
The maturity and yield estimation method and device based on the internet of things provided by the embodiment of the application are introduced in detail, a specific example is applied to explain the principle and the implementation mode of the application, and the description of the embodiment is only used for helping to understand the method and the core idea of the application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description is only an example of the present application, and is provided to enable any person skilled in the art to understand or implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A maturity estimation method based on the Internet of things is characterized by comprising the following steps:
acquiring the number of fruits falling on a collector within a preset time;
and determining the fruit maturity of the plant to be detected according to the number of the fruits falling on the collector within a preset time and a preset fruit falling rate.
2. The method of claim 1, wherein the obtaining the number of fruits falling on the collector within a preset time comprises:
acquiring the actual number of fruits falling on a collector within a preset time;
determining the number of mature fruits from the actual number of fruits falling on a collector within a preset time;
the method for determining the fruit maturity of the plant to be detected according to the number of fruits falling on the collector within the preset time and the preset fruit falling rate comprises the following steps:
and determining the fruit maturity of the plant to be detected according to the number of mature fruits falling on the collector within a preset time and a preset fruit falling rate.
3. The method of claim 2, wherein determining the number of ripe fruits from the actual number of fruits dropped on the harvester within the preset time comprises:
obtaining the impact times of a single fruit on the collector;
and determining the number of the mature fruits from the actual number of the fruits falling on the collector within the preset time according to the impact times.
4. The method of claim 3, wherein determining the number of ripe fruits from the actual number of fruits dropped on the harvester within a preset time according to the number of impacts comprises:
counting the number of fruits falling on the collector within a preset time, wherein the number of the fruits with the impact frequency of 1 is used as the number of mature fruits.
5. The method of claim 2, wherein determining the number of ripe fruits from the actual number of fruits dropped on the harvester within the preset time comprises:
counting the number of fruits falling on the collector within a preset time, wherein the weight of each fruit is greater than or equal to the preset weight, and the number is used as the number of mature fruits.
6. An internet of things based yield estimation method, comprising the steps of the method of any one of claims 1-5, further comprising:
after determining that the fruit maturity of the plant to be detected reaches a preset maturity, acquiring the average weight of fruits falling onto a collector within a preset time;
and determining the yield of the plant to be detected according to the average weight of the fruits.
7. Method according to claim 6, wherein the relation between the average weight of fruit F and the yield Q of the plant to be detected satisfies:
the device comprises a collector, a support rod, a wind power value and a controller, wherein N is the number of fruits falling on the collector within a preset time, d is the average distance between the impact position of the falling fruits on the collector and the support rod of the collector, a is a constant, and W is the wind power value.
8. A maturity and yield estimation device based on the Internet of things is characterized by comprising a collector, a camera and a controller;
the collector is used for receiving the fallen fruits;
the camera is used for collecting an image of a plant to be detected;
the controller is used for determining the number of fruits falling onto the collector within a preset time according to the image of the plant to be detected, and determining the fruit maturity of the plant to be detected according to the number of the fruits falling onto the collector within the preset time and a preset fruit falling rate.
9. The device of claim 8, wherein the collector comprises a collecting plate, a supporting rod and a pressure sensor, and the bottom of the supporting rod is arranged on the ground;
the collecting plate is a plane collecting plate, and one end of the collecting plate is fixed on the supporting rod; the collecting plate is used for receiving the fallen fruits;
the pressure sensor is electrically connected with the collecting plate and is used for collecting the weight of fruits falling from the plant to be detected;
the camera is arranged at the top of the supporting rod.
10. The apparatus of claim 8, further comprising a wind direction sensor and a wind speed sensor.
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