CN114910147B - Maturity and yield estimation method and device based on Internet of things - Google Patents
Maturity and yield estimation method and device based on Internet of things Download PDFInfo
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Abstract
The application discloses a maturity and yield estimation method and device based on the Internet of things, wherein 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 fruits falling on the collector in the preset time and the preset fruit falling rate. According to the maturity and yield estimation method and device based on the Internet of things, the maturity of the fruits can be automatically identified under the condition of no manual participation, so that the accuracy and efficiency of the identification of the maturity of the fruits are improved, references are provided for selection of 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 and more mature, and ubiquitous connection of objects and people is realized through various possible network accesses, so that intelligent perception, identification and management of objects and processes are realized. The internet of things is an information carrier based on the internet, a traditional telecommunication network and the like, and enables all common physical objects which can be independently addressed to form an interconnection network. At present, the technology of the Internet of things is widely applied to the aspects of 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 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 represented. If the maturity is too high, the taste of the fruits becomes light, the fruits are easy to soften and damage, the logistics transportation and storability of the fruits are affected, and the fruit yield, flower bud development and the fruit quality of the next year are affected if the fruits are harvested too early or too late. In the traditional method, the maturity of the fruit in fruit harvest is judged by taking fruit peel color as a standard according to experience, and more important factors such as fruit diameter, single fruit weight, sweetness and the like are not considered, so that incorrect judgment is likely to be generated due to the early and late expression of pigment genes of different fruit peel.
Disclosure of Invention
In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides a maturity and yield estimation method based on the internet of things, the method comprising:
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 fruits falling on the collector in the preset time and the 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 the collector within a preset time;
the fruit maturity of the plant to be detected is determined according to the number of fruits falling on the collector in a preset time and a preset fruit falling rate, and the method 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 in the preset time and the preset fruit falling rate.
Preferably, the determining the number of ripe fruits from the actual number of fruits dropped on the collector within a preset time includes:
acquiring the impact times of single fruits on the collector;
and determining the number of mature fruits from the actual number of fruits falling on the collector within 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 includes:
and counting the number of fruits which fall on the collector within a preset time, wherein the impact frequency is 1, and the number of the fruits 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 collector within a preset time includes:
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 of fruits is used as the number of mature fruits.
The embodiment of the application also provides a yield estimation method based on the Internet of things, which comprises the steps of the maturity estimation method disclosed by the embodiment of the application, and further comprises the following steps:
after determining that the fruit maturity of the plant to be detected reaches the preset maturity, acquiring the average weight of the fruits falling on the collector within the preset time;
and determining the yield of the plant to be detected according to the average weight of the fruits.
Preferably, the relation between the average weight F of the fruit and the yield Q of the plant to be detected is such that:
wherein N is the number of fruits falling on the collector in the preset time, d is the average distance between the impact position of the falling fruits on the collector and the supporting rod of the collector, 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 dropped fruits;
the camera is used for collecting plant images to be detected;
the controller is used for determining the number of fruits falling on the collector in a preset time according to the plant image to be detected, and determining the fruit maturity of the plant to be detected according to the number of fruits falling on the collector in the preset time and the preset fruit dropping rate.
Preferably, the collector comprises a collecting plate, a supporting rod and a pressure sensor, wherein 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 fallen fruits;
the pressure sensor is electrically connected with the acquisition board and is used for acquiring the weight of fruits dropped by the plants to be detected;
the camera is arranged at the top of the supporting rod.
Preferably, the apparatus 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 maturity and yield estimation method and device based on the Internet of things, the maturity of the fruits can be automatically identified under the condition of no manual participation, so that the accuracy and efficiency of the identification of 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 application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic flow chart of a maturity estimation method based on the internet of things according to an embodiment of the present application;
fig. 2 is another 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 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 flow chart of a maturity estimation method based on the internet of things according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a maturity and yield estimation device based on the internet of things according to an embodiment of the present application.
Detailed Description
In order that the above objects, features and advantages of the application will be more clearly understood, a further description of the application will be made. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
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 otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the application.
Fig. 1 is a schematic flow chart of a maturity estimation method based on the internet of things, provided by the embodiment of the application, as shown in fig. 1, the maturity estimation method based on the internet of things includes:
step 101: and acquiring the number of fruits falling on the collector within a preset time.
In step 101, the number of fruits of the plant to be detected falling on the collector within a preset time is obtained.
Step 102: and determining the fruit maturity of the plant to be detected according to the number of fruits falling on the collector in the preset time and the preset fruit falling rate.
The fruit dropping rate refers to the number of fruits dropped from the plant to be detected within a preset time; when the fruits on the plant to be detected are ripe, the fruits on the plant to be detected naturally fall down; and when the plant to be detected is in the mature period, the fruits are more due to the fact that the fruits are more mature, and the corresponding fruits drop, so that compared with the plant to be detected in the mature period, the fruit drop rate of the plant to be detected is obviously higher. Therefore, the fruit maturity of the plant to be detected can be determined according to the number of fruits of the plant to be detected falling on the collector in the preset time and the preset fruit dropping rate.
According to the maturity estimation method based on the Internet of things, provided by the embodiment of the application, the fruit maturity can be automatically identified under the condition of no manual participation, so that the accuracy and the efficiency of fruit maturity identification are improved, and the labor cost can be effectively reduced.
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 of the plant to be detected falling on the collector within 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 of the plant to be detected falling on the collector within the preset time is greater than or equal to the preset fruit dropping rate, determining that the fruits of the plant to be detected are ripe; when the number of fruits of the plant to be detected falling on the collector within the preset time is smaller than the preset fruit dropping rate, determining that the fruits of the plant to be detected are immature.
In some embodiments, step 101: acquiring the number of fruits falling on the collector within a preset time, for example, further 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 the collector within a preset time; step 102: according to the fruit quantity that drops on the collector in the preset time and preset fruit drop rate, confirm the fruit maturity of waiting to detect the plant, for example still include: and determining the fruit maturity of the plant to be detected according to the number of mature fruits falling on the collector in the preset time and the preset fruit falling rate.
In some embodiments, the number of collectors placed on a plant farm is determined based on the area of the plant farm on which the plant to be detected is located. Preferably, the number of collector settings is one fifth of the total area of the plant park.
In some embodiments, the number of collectors provided in the southern area of the plant park is greater than the number of collectors provided in the northern area of the plant park. According to the method provided by the application, the position of the collector is placed, and the accuracy of fruit maturity detection can reach more than 86% through test, so that the effect is more obvious compared with other placing methods.
Since plants are grown in the south, it is preferable that the number of collectors provided in the southern area of the plant park is three-fourths of the total number of collectors and the number of collectors provided in the northern area of the plant park is one-fourth of the total number of collectors. Wherein the collectors are concentrated in the south area, i.e. the area towards the sun, which can reduce the yield by 7% loss.
The foregoing is merely one embodiment of the present application, and as shown in fig. 2, fig. 2 is another flow chart 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 fruits falling on the collector within a preset time.
The actual number of fruits is the number of all fruits falling on the collector within a preset time.
Step 202: the number of ripe fruits is determined from the actual number of fruits dropped on the collector for a preset time.
In step 202, it is determined which of the actual number of fruits falling on the collector from a preset time is the number of ripe fruits and which is the number of immature fruits.
Step 203: and determining the fruit maturity of the plant to be detected according to the number of mature fruits falling on the collector in the preset time and the preset fruit falling rate.
In step 203, the number of ripe 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 the preset time is greater than or equal to the preset fruit dropping rate, determining that the fruits of the plant to be detected are mature; when the number of mature fruits of the plant to be detected falling on the collector within the preset time is smaller than the preset fruit dropping rate, determining that the fruits of the plant to be detected are immature.
According to the maturity estimation method based on the Internet of things, the maturity of the fruits of the plants to be detected is determined by comparing the number of the ripe fruits falling on the collector within the preset time with the preset fruit dropping rate, and the fact that the actual number of the fruits falling on the collector contains non-ripe fruits is avoided, namely, the fruit dropping rate of the ripe fruits is compared with the preset fruit dropping rate, so that the accuracy of fruit maturity detection can be further improved, and the fruit maturity detection result can be more in accordance with the fruit maturity of the plants to be detected.
The foregoing is only one embodiment of the present application, and as shown in fig. 3, fig. 3 is another flow schematic diagram of a maturity estimation method based on the internet of things provided by the embodiment of the present application, and referring 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: the number of impacts of a single fruit on the collector is obtained.
The fruits are sprung when falling onto the collector, and some fruits strike the collector for 1 time and then spring onto the ground; some fruits are bounced after falling onto the collector and impacting 1 time, and fall onto the collector again and are bounced, so that the impact times of single fruits and the collector can be 1 time or multiple times.
Step 303: and determining the number of mature fruits from the actual number of fruits falling on the collector within preset time according to the impact times.
The fruits falling on the collector are bounced, because some fruits are still immature; compared with the mature fruits, the immature fruits are lighter in weight, so that after the immature fruits fall on the collector, the spring height is lower, the immature fruits fall on the collector again easily, and then whether the fruits are mature or not can influence the impact times of the mature fruits and the collector, and the number of the mature fruits can be determined from the actual number of the fruits falling on the collector in a preset time according to the impact times.
Step 304: and determining the fruit maturity of the plant to be detected according to the number of mature fruits falling on the collector in the preset time and the preset fruit falling rate.
According to the maturity estimation method based on the Internet of things, provided by the embodiment of the application, the number of the mature fruits is determined from the actual number of the fruits falling on the collector within the preset time according to the impact times of the single fruits on the collector, the method is simple to operate and easy to realize, the number of the mature fruits in the actual number of the fruits can be automatically detected, and a scientific basis is provided for detecting the maturity of the fruits.
In some embodiments, step 303: the determining the number of ripe fruits from the actual number of fruits falling on the collector in a preset time according to the number of impact times, for example, further includes: and counting the number of fruits which fall on the collector within a preset time, wherein the impact frequency is 1, and the number of the fruits is used as the number of mature fruits. When the number of times of the impact of the fruit falling on the collector and the collector is 1, the weight of the falling fruit is heavier, and only the mature fruit can reach the weight, and the falling fruit can be sprung up by the collector to be higher, and the collector can be sprung up to fall on the ground after 1 time of impact, so that the fruit with 1 time of impact can be judged to be the mature fruit.
The foregoing is only one embodiment of the present application, and as shown in fig. 4, fig. 4 is another flow schematic diagram of a maturity estimation method based on the internet of things provided by the embodiment of the present application, and referring 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 of fruits is used as the number of mature fruits.
The weight of the mature fruit is larger than that of the immature fruit, and when the weight of a single fruit reaches a certain weight, the fruit can be judged to be the mature fruit. For example, the preset weight may be set to the weight of ripe fruits, and the weight of a single fruit is equal to or greater than the preset weight, and the ripe fruits may be obtained as ripe fruits.
Step 403: and determining the fruit maturity of the plant to be detected according to the number of mature fruits falling on the collector in the preset time and the preset fruit falling rate.
According to the maturity estimation method based on the Internet of things, the number of the mature fruits is determined from the actual number of the fruits falling on the collector within the preset time according to the weight of a single fruit, the method is high in accuracy, the number of the mature fruits in the actual number of the fruits can be automatically detected without depending on the appearance conditions of the traditional fruits such as the color and luster, and scientific basis is provided for detecting the maturity of the fruits.
In some embodiments, the acquiring 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 the fruits falling on the collector are determined to be mature fruits, the fruits falling on the collector are recorded to be effective; and counting the number of effective fruits within a preset time, and determining the effective real number as the number of mature fruits. The fruit maturity of the plant to be detected is determined according to the number of fruits falling on the collector in a preset time and a preset fruit falling rate, and the method 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 in the preset time and the preset fruit falling rate.
According to the maturity estimation method based on the Internet of things, whether the dropped fruits are mature fruits or not is judged, after the dropped fruits are determined to be mature fruits, the dropped fruits are recorded as effective and real fruits, and the effective fruits are counted within a preset time, wherein the effective fruits are the mature fruits. Therefore, whether the dropped fruits are mature fruits can be judged, whether the dropped fruits are effective fruits can be recorded, and then the effective real quantity is counted, so that the number of the mature fruits can be obtained easily. The method is simple to operate and easy to implement, and provides a plurality of detection methods easy to implement for detecting the fruit maturity.
The embodiment of the application also provides a yield estimation method based on the Internet of things, which comprises the steps of the maturity estimation method disclosed by the embodiment of the application, and further comprises the following steps: after determining that the fruit maturity of the plant to be detected reaches the preset maturity, acquiring the average weight of the fruits falling on the collector within the preset time; and determining the yield of the plant to be detected according to the average weight of the fruits.
Specifically, when the fruit maturity of the plant to be detected reaches the preset maturity, it is indicated that the plant to be detected is already mature at this time, the yield of the plant to be detected can be estimated at this time, and the yield of the plant to be detected is estimated accurately at this time, so that the average weight of the fruits falling on the collector in the preset time can be obtained after the fruit maturity of the plant to be detected is determined to reach the preset maturity; from the average weight of the fruit, the yield of the plant to be detected can be determined.
Preferably, the relation between the average weight F of the fruit and the yield Q of the plant to be detected is such that:
wherein N is the number of fruits falling on the collector in the preset time, d is the average distance between the impact position of the falling fruits on the collector and the supporting 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 consideration, the falling of the fruits due to external factors such as wind power is 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 ripe fruits dropped onto the collector for a preset time and the average weight of ripe fruits. This can further improve the accuracy of the yield calculation of the plants to be detected.
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 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 on the collector in a preset time according to the plant image to be detected, and determining the fruit maturity of the plant to be detected according to the number of fruits falling on the collector in the preset time and the preset fruit dropping rate.
The maturity and yield estimation device based on the Internet of things provided by the embodiment of the application can realize automatic detection of the fruit maturity, is simple in structure and low in equipment cost, can realize wide application, has high accuracy in detecting the fruit maturity, does not need to rely on manpower to detect the maturity, and is convenient for realizing automatic production of agriculture.
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 fruit maturity of the plant to be detected reaches the 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, the estimation of the yield of the plant to be detected can be started at this time, and the estimation of the yield of the plant to be detected is accurate at this time, so that the controller can be used for acquiring the average weight of the fruits falling on 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 detected can be determined.
Preferably, the relation between the average weight F of the fruit and the yield Q of the plant to be detected is such that:
wherein N is the number of fruits falling on the collector in the preset time, d is the average distance between the impact position of the falling fruits on the collector and the supporting rod of the collector, a is 0.22-0.68, and W is the wind force value.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a device for estimating maturity and yield based on internet of things according to an embodiment of the present application, and referring to the structure of fig. 5, the device 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 the collecting plate 503 is fixed on the supporting rod 504 at one end; the collecting plate 503 is used for receiving the dropped fruits 507; the pressure sensor 505 is electrically connected with the collecting plate 503 and is used for collecting the weight of fruits 507 dropped by the plants to be detected; the camera 501 is arranged on 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 on the collector in a preset time according to the plant image to be detected, and determine the fruit maturity of the plant to be detected according to the number of fruits falling on the collector in the preset time and the preset fruit dropping rate.
The controller 503 may also be configured to determine that the average weight of the fruits falling on the collector within a preset time after the fruit maturity 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 support rod 504 in the collector, and the wireless communication module 508 can be in communication connection with the controller 502, so as 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, etc., which is not limited by the present application. The wireless communication module can be arranged at any position of the collector, and the position of the wireless communication module is adjusted according to the structure of the actual collector, so that the application is not limited to the position.
In some embodiments, as shown in fig. 5, the apparatus further includes a wind direction sensor 509 and a wind speed sensor 510, the wind direction sensor 509 and the wind speed sensor 510 being in communication with the controller 502, enabling 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 each 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 that enables the wind direction sensor 509 and the wind speed sensor 510 to communicate with the controller 502. The wireless communication module may be, for example, an antenna, a wireless connector, a signal interactor, etc., which is not limited by the present application.
In some embodiments, the controller may be, for example, a smart electronic device such as a cell phone, a computer, a tablet, or the like.
According to the maturity and yield estimation device based on the Internet of things, provided by the embodiment of the application, in order to eliminate the error of fruit maturity detection, the wind direction sensor and the wind speed sensor are arranged, for example, in a hilly area, one wind direction sensor and one wind speed sensor are arranged in each 10 mu area of plant park.
In some embodiments, the yield of the plant to be detected may be determined, for example, based on the number of ripe fruits dropped onto the collector for a preset time and the average weight of ripe fruits. This can further improve the accuracy of the yield calculation of the plants to be detected. As shown in fig. 5, d is an average distance between an impact position of a mature fruit on a collector and a supporting rod of the collector, and according to an embodiment of the present application, a yield estimation method based on the internet of things is provided, 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 dropped 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 a wind force value. Preferably, a is 0.22-0.68.
According to the maturity and yield estimation device based on the Internet of things, the wind force value can be calculated through the wind direction sensor and the wind speed sensor, according to the wind force value, the situation that the statistics of the number of mature fruits is inaccurate due to external factors such as wind force when the yield of a plant to be detected is calculated can be avoided, and adverse effects caused by the problem can be well avoided by combining the wind direction sensor and the wind speed sensor and the yield calculation method provided by the embodiment of the application, wherein some fruits fall naturally after maturation and possibly fall due to local wind influence, such as wind blowing, and the number of fruits falling due to wind blowing can influence statistics and calculation of the actual number of mature fruits.
The method and the device for estimating the maturity and the yield based on the Internet of things provided by the embodiment of the application are described in detail, and specific examples are applied to the description of the principle and the implementation mode of the application, and the description of the above embodiments is only used for helping to understand the method and the core idea of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
It should be noted that in this document, relational terms such as "first" and "second" and the like are 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. Moreover, 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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is only a specific embodiment of the application to enable those skilled in the art to understand or practice the 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 and described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The maturity estimation method based on the Internet of things is characterized by comprising the following steps of:
acquiring the number of fruits falling on a collector within a preset time, comprising: acquiring the actual number of fruits falling on a collector within a preset time; determining the number of ripe fruits from the actual number of fruits falling on the collector within a preset time comprises: acquiring the impact times of single fruits on the collector; according to the impact times, determining the number of mature fruits from the actual number of fruits falling on the collector within preset time;
according to the fruit quantity that drops on the collector in the time of predetermineeing and predetermine the fruit rate of falling, confirm the fruit maturity of waiting to detect the plant, include: according to the number of ripe fruits falling on the collector in the preset time and the preset fruit dropping rate, the fruit maturity of the plant to be detected is determined, wherein the fruit dropping rate refers to the number of fruits falling from the plant to be detected in the preset time.
2. The method of claim 1, wherein determining the number of ripe fruits from the actual number of fruits dropped on the collector for a preset time based on the number of impacts comprises:
and counting the number of fruits which fall on the collector within a preset time, wherein the impact frequency is 1, and the number of the fruits is used as the number of mature fruits.
3. The method of claim 1, wherein determining the number of ripe fruits from the actual number of fruits dropped onto the collector for a predetermined 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 of fruits is used as the number of mature fruits.
4. A yield estimation method based on the internet of things, comprising the steps of the method according to any one of claims 1-3, further comprising:
after determining that the fruit maturity of the plant to be detected reaches the preset maturity, acquiring the average weight of the fruits falling on the collector within the preset time;
and determining the yield of the plant to be detected according to the average weight of the fruits.
5. The method according to claim 4, characterized in that the relation between the average weight F of the fruit and the yield Q of the plant to be detected is such that:
wherein N is the number of fruits falling on the collector in a preset time, d is the average distance between the impact position of the falling fruits on the collector and the supporting rod of the collector, a is a constant, and W is a wind force value.
6. The 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 dropped fruits;
the camera is used for collecting plant images to be detected;
the controller is used for determining the quantity of fruits falling on the collector in a preset time according to the plant image to be detected, and determining the fruit maturity of the plant to be detected according to the quantity of fruits falling on the collector in the preset time and the preset fruit dropping rate, and comprises the following steps: acquiring the impact times of single fruits on the collector; according to the impact times, determining the number of mature fruits from the actual number of fruits falling on the collector within preset time; according to the number of ripe fruits falling on the collector in the preset time and the preset fruit dropping rate, determining the fruit ripeness of the plant to be detected, wherein the fruit dropping rate refers to the number of fruits falling from the plant to be detected in the preset time.
7. The device of claim 6, wherein the collector comprises a collection plate, a support rod and a pressure sensor, wherein the bottom of the support 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 fallen fruits;
the pressure sensor is electrically connected with the acquisition board and is used for acquiring the weight of fruits dropped by the plants to be detected;
the camera is arranged at the top of the supporting rod.
8. The apparatus of claim 7, further comprising a wind direction sensor and a wind speed sensor.
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