CN110458032B - Whole-process litchi growth condition monitoring method and system, cloud server and storage medium - Google Patents

Abstract

The invention discloses a litchi growth condition whole-process monitoring method and system, a cloud server and a storage medium, wherein the system is based on the Internet of things and comprises an environmental data acquisition device, a networking camera, a router and the cloud server, wherein the environmental data acquisition device and the networking camera are respectively connected with the router, and the router is connected with the cloud server; the cloud server can acquire litchi images and environmental data information, detect information such as litchi growth period, litchi fruit number and maturity by using a target detection algorithm and an image processing technology based on deep learning, and provide a management strategy meeting agricultural requirements for a litchi plantation manager by combining environmental data acquired by various sensors; the Internet of things management platform and the e-commerce platform are deployed in the cloud server, litchi related information can be checked, the e-commerce platform integrates the virtual roaming module, and the litchi planting base is virtualized through a virtual reality technology, so that the e-commerce platform has litchi plantation virtual display and virtual roaming functions.

Description

Full-process monitoring method, system, cloud server and storage medium for lychee growth status

technical field

The invention relates to a whole-process monitoring method, system, cloud server and storage medium for litchi growth conditions, belonging to the technical fields of Internet of Things, image processing, virtual reality and the like.

Background technique

Agricultural fruit remote monitoring is to detect the growth environment information of fruit tree crops in agricultural orchards in real time, and dynamically detect the growth of fruit tree crops. According to research, the pollination and fertilization of lychees during the flowering period directly affects the fruit setting rate of lychees, and the pollination and fertilization of lychees is extremely sensitive to environmental conditions. Higher levels affect fertilization. Therefore, when the temperature in the flowering period is low, artificial regulation is required, and lychee pollen germination is promoted by spraying growth regulators; artificial pollination is required when the temperature is low and rainy for a long time. However, in the current actual orchard production management, there are situations such as untimely acquisition of environmental information, improper management measures, and inability to harvest timely. Applying technologies such as the Internet of Things, sensors, and image processing to agriculture, collecting and remotely processing and analyzing crop growth information in agricultural environments, and prompting managers to perform correct management in combination with agronomic requirements will be beneficial to the healthy growth of crops.

With the continuous enrichment of agricultural products and the improvement of people's living standards, consumers have higher and higher requirements for ordered products. They not only have higher quality requirements for the final products ordered, but also hope to have certain planting management processes for the ordered products. understanding. The current e-commerce platform provides consumers with more functions to browse products, order products and pay, and it is difficult to satisfy consumers' understanding of orchards and fruit planting process.

SUMMARY OF THE INVENTION

In view of this, the present invention provides a whole-process monitoring method, device, system, cloud server and storage medium for lychee growth conditions, which can realize the whole-process monitoring of lychee growth conditions, integrate multi-source information processing and combine with agronomic requirements, It provides management strategies and picking strategies, and at the same time enables users to view lychee-related information and virtual sightseeing functions.

The first object of the present invention is to provide a whole-process monitoring method for the growth condition of litchi.

The second object of the present invention is to provide a whole-process monitoring device for the growth condition of litchi.

The third object of the present invention is to provide a whole-process monitoring system for the growth condition of litchi.

The fourth object of the present invention is to provide a cloud server.

A fifth object of the present invention is to provide a storage medium.

The first purpose of the present invention can be achieved by adopting the following technical solutions:

A method for monitoring the growth condition of litchi throughout the process, which is applied to a cloud server, and the method includes:

Get real-time lychee images;

According to the real-time lychee image, detect the growth period of the lychee;

When it is detected that the lychee flowering period is coming, prompt the lychee plantation manager to manually release bees to promote pollination;

During the flowering period, if the time when the air temperature is lower than the first preset value exceeds the preset time, and the average daily air humidity after the preset time exceeds the second preset value, the lychee plantation manager will be prompted to spray growth regulators and carry out artificial pollination;

When the lychee results are detected, the ripeness of the lychee is detected.

Further, described detecting the maturity degree of litchi, specifically includes:

Use the trained lychee recognition model to identify lychees in real-time lychee images;

Taking each identified target center as a clustering object, clustering by density, and treating the targets clustered into one class as a string of lychees;

Convert each lychee region in a string of lychees to the HSV color space, and calculate the H channel component;

According to the H channel component, calculate the red ratio of the entire string of target areas;

According to the proportion of red in the target area of the whole bunch, the maturity of the whole bunch of lychees is estimated.

Further, the calculation formula of the red ratio of the target area is as follows:

Among them, _PR is the red ratio of the target area, i is the H component value, H _i is the number of pixels in i, n is the number of lychees, and s is the total number of pixels in the area.

Further, before the acquisition of the real-time litchi image, it also includes:

Obtain images of lychees in various growth periods and under various lighting conditions;

According to the images of lychees in various growth periods and under various illuminations, the training set and the validation set are constructed;

The training set is input into the target detection network for training, and the lychee recognition model is obtained;

Input the validation set into the lychee recognition model for verification to adjust the parameters of the lychee recognition model.

The second object of the present invention can be achieved by adopting the following technical solutions:

A whole-process monitoring device for lychee growth, applied to a cloud server, the device comprising:

The acquisition module is used to acquire real-time lychee images;

The first detection module is used to detect the growth period of the lychee according to the real-time lychee image;

The first prompt module is used to prompt the litchi plantation manager to manually release bees to promote pollination when the lychee flowering period is detected;

The second prompting module is used to prompt the management of the lychee plantation if the time when the air temperature is lower than the first preset value exceeds the preset time during the flowering period, and the average daily air humidity after the preset time exceeds the second preset value spraying of growth regulators and artificial pollination;

The second detection module is used to detect the maturity of the lychee after the lychee result is detected.

The third object of the present invention can be achieved by adopting the following technical solutions:

A whole-process monitoring system for lychee growth conditions, the system comprises an environmental data acquisition device, a networked camera, a router and a cloud server, the environmental data acquisition device and the networked camera are respectively connected to a router, and the router is connected to the cloud server;

The cloud server is used for executing the above-mentioned whole-process monitoring method for the growth condition of litchi.

Further, the environmental data collection device includes a processor chip, a LoRa communication module, an air temperature and humidity sensor, a soil moisture sensor, a soil pH sensor, a carbon dioxide concentration sensor, and a light intensity sensor, and the air temperature and humidity sensor, soil moisture sensor, The soil pH sensor, the carbon dioxide concentration sensor and the light intensity sensor are respectively connected with the processor chip through the control circuit, and the processor chip is connected with the router through the LoRa communication module.

Further, an Internet of Things management platform is deployed in the cloud server, and the Internet of Things management platform provides users with management advice, lychee images, days after flowering, environmental data, number of lychee fruits, number of lychee strings, and ripeness in the form of a website. degree information.

Further, an e-commerce platform is deployed in the cloud server, the e-commerce platform provides users with commodity information in the form of a website, and the e-commerce platform is integrated with a virtual roaming module;

The establishment process of the virtual roaming module includes: modeling the physical objects in the lychee plantation through modeling software, and importing the built model into the virtual reality development software; according to the environment of the actual lychee plantation, using the virtual reality development software Build a three-dimensional scene of a virtual lychee plantation; add a character model in the established virtual lychee plantation, and nest cameras in the character model; add a control script to enable users to manipulate the character model to achieve virtual sightseeing in the virtual lychee plantation.

The fourth object of the present invention can be achieved by adopting the following technical solutions:

A cloud server includes a processor and a memory for storing a program executable by the processor, and when the processor executes the program stored in the memory, the above-mentioned method for monitoring the growth condition of litchi throughout the process is realized.

The fifth object of the present invention can be achieved by adopting the following technical solutions:

A storage medium storing a program, characterized in that, when the program is executed by a processor, the above-mentioned method for monitoring the growth condition of litchi throughout the process is realized.

The present invention has the following beneficial effects with respect to the prior art:

1. Based on the Internet of Things, the present invention can realize the whole process monitoring of the growth status of lychees, combine the detection results with agronomic requirements, and provide management strategies for the managers of lychee plantations; The number of fruits, the number of litchi bunches, the degree of maturity and other information are stored, a relational database based on time scale is constructed, and a regression method is used to establish a relationship model between lychee growth environment factors and ripening rate, so as to analyze the lychee growth environment factors and maturity rate. The regularity between them provides picking strategies for lychee plantation managers.

2. The present invention deploys an e-commerce platform in a cloud server, so that lychees can be used as commodities to trade with users, and users can view lychee prices, lychee images, days after flowering, environmental data, and lychee fruits by visiting the web page of the e-commerce platform. The e-commerce platform integrates a virtual roaming module, and virtualizes the lychee planting base through virtual reality technology, so that the e-commerce platform has the functions of virtual display and virtual roaming of lychee plantations, so that users can Go out, browse the planting environment of lychee plantation through the Internet, get a virtual agricultural sightseeing experience, and provide users with more comprehensive information.

3. The present invention deploys an Internet of Things management platform in the cloud server, and users can view management suggestions, lychee images, days after flowering, environmental data, number of lychee fruits, number of lychee strings, maturity, etc. by accessing the web page of the Internet of Things platform. information.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can also be obtained according to the structures shown in these drawings without creative efforts.

1 is a structural block diagram of a whole-process monitoring system for litchi growth conditions according to Embodiment 1 of the present invention.

FIG. 2 is a schematic diagram of the relationship between an environmental data collection device, a networked camera, a router, and a cloud server according to Embodiment 1 of the present invention.

FIG. 3 is a structural block diagram of the apparatus for collecting environmental data according to Embodiment 1 of the present invention.

FIG. 4 is a structural block diagram of a cloud server according to Embodiment 1 of the present invention.

Fig. 5 is the flow chart of the whole process monitoring method of the litchi growth condition of the embodiment of the present invention 1.

FIG. 6 is a flow chart of detecting the maturity of litchi according to Embodiment 1 of the present invention.

FIG. 7 is a schematic diagram of detecting the maturity degree of litchi in Example 1 of the present invention.

FIG. 8 is a flowchart of establishing a litchi identification model according to Embodiment 1 of the present invention.

FIG. 9 is a flowchart of establishing a virtual roaming module according to Embodiment 1 of the present invention.

10 is a schematic diagram of implementing virtual sightseeing in a virtual lychee plantation by using a virtual roaming module to manipulate a character model according to Embodiment 1 of the present invention.

FIG. 11 is a structural block diagram of a whole-process monitoring device for litchi growth according to Embodiment 2 of the present invention.

FIG. 12 is a structural block diagram of a second detection module according to Embodiment 2 of the present invention.

FIG. 13 is a relationship diagram of a second acquisition module, a building module, a training module, and an adjustment module in Embodiment 2 of the present invention.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work are protected by the present invention. scope.

Example 1:

As shown in FIG. 1 and FIG. 2 , this embodiment provides a whole-process monitoring system for the growth of litchi, which is based on the Internet of Things and includes an environmental data collection device 101, a networked camera 102, a router 103 and a cloud server 104. The device 101 and the networked camera 102 are respectively connected to the router 103 , and the router 103 is connected to the cloud server 104 .

In order to wirelessly transmit the data collected by the environmental data collection device 101 to the router 103 and ensure the communication effect, LoRa wireless technology with strong penetrating power and long communication distance is selected; In the plantation, and the environmental data acquisition device 101 and the router 103 are equipped with a LoRa communication module, the environmental data acquisition device 101 transmits data to the router 103 through the LoRa communication module according to the set time.

As shown in FIG. 3 , the environmental data acquisition device 101 includes a processor chip 1011, a LoRa communication module 1012, an air temperature and humidity sensor 1013, a soil moisture sensor 1014, a soil pH sensor 1015, a carbon dioxide (CO ₂ ) concentration sensor 1016 and The light intensity sensor 1017, the air temperature and humidity sensor 1013, the soil moisture sensor 1014, the soil pH sensor 1015, the carbon dioxide concentration sensor 1016 and the light intensity sensor 1017 are respectively connected with the processor chip 1012 through the control circuit, and the processor chip 1012 is connected through the LoRa communication module 1012 Connect with router 103.

Specifically, the air temperature and humidity sensor 1012 is used to collect the temperature and humidity data of the air, the soil moisture sensor 1013 is used to collect the moisture content in the soil, and the soil pH sensor 1014 is used to collect the pH value of the soil. The soil fertility can be obtained according to the pH value. The carbon dioxide concentration sensor 1015 is used to collect carbon dioxide concentration data in the air, the light intensity sensor 1016 is used to collect light intensity values, the processor chip 1012 collects these data, and sends these data through the LoRa communication module 1012 at a set time to the router.

In order to wirelessly transmit the lychee image data collected by the networked camera 102 to the router 103 , the networked camera of this embodiment is arranged in a lychee plantation with a resolution of 2560×1440 and can be connected to the WiFi network transmission provided by the router 103 Lychee image data.

The router 103 in this embodiment adopts a 4G router, and FIG. 2 shows the process of data collection and transmission, in which the environmental data collection device 101 and the networked camera 102 are respectively connected to the router 103 through the LoRa communication module and WiFi, and the router 103 then passes through the 4G network The data is transmitted to the cloud server 104 .

As shown in FIG. 4 , the cloud server 104 includes a processor 1042, a first memory, and a network interface 1043 connected through a system bus 1041; wherein the processor 1042 is used to provide computing and control capabilities, and the memory includes non-volatile storage A medium 1044 and an internal memory 1045, the non-volatile storage medium 1044 stores an operating system, a computer program and a database, the internal memory 1045 provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium, will collect The data such as air temperature, air humidity, soil moisture content, soil pH value, carbon dioxide concentration value, light intensity value, lychee image and other data are stored in the database, and the computer program is executed by the processor 1042.

As shown in Figure 5, the whole process monitoring method of litchi growth condition of the present embodiment comprises the following steps:

S501. Acquire a real-time lychee image.

Specifically, the real-time lychee image collected by the networked camera 102 is stored in the database of the cloud server 104, and the real-time lychee image can be obtained by searching from the database.

S502, according to the real-time lychee image, detect the growth period of the lychee.

S503, when it is detected that the lychee flowering period is approaching, prompt the lychee plantation manager to release bees manually to promote pollination.

S504. During the flowering period, if the following two conditions are met, the lychee plantation manager is prompted to spray growth regulators and conduct artificial pollination:

1) The time when the air temperature is lower than the first preset value exceeds the set time.

2) After the set time is exceeded, the daily average air humidity is greater than the second preset value.

The air temperature data and air humidity data are stored in the database of the cloud server 104, and the air temperature data and air humidity data can be obtained by searching from the database. The set time in this embodiment is three days, and the first preset value is 17° C. , the second preset value is 85%. If the air temperature is lower than 17°C for more than three days, and the average daily air humidity is greater than 85% after three days, the lychee plantation manager will be prompted to spray growth regulators and conduct artificial pollination. .

In steps S503 and S504, it can be sent to mobile terminals such as mobile phones and tablet computers of lychee plantation managers (usually referred to as fruit farmers) by means of text messages, WeChat, etc., to prompt the lychee plantation managers to perform corresponding operations and detect The results were combined with agronomic requirements to provide management strategies for litchi plantation managers.

After step S504 , the method may further include: when the withered flowers are detected, recording the number of days after the flowers so that the user can check them in the future.

S505, after detecting the lychee result, detect the maturity degree of the lychee.

As shown in Figure 6 and Figure 7, detecting the maturity of lychees includes:

S5051 , using the trained lychee recognition model to recognize lychees in real-time lychee images.

S5052 , using each identified target center as a clustering object, perform clustering according to density, and regard the targets clustered into one category as a string of lychees; the clustering according to density in this embodiment uses the DBSCAN method to perform clustering.

S5053. Convert each lychee region in a string of lychees to the HSV color space, and calculate the H channel component.

S5054. Calculate the red ratio of the entire string of target areas according to the H channel component.

Assuming that i is the value of the H component, H _i is the number of pixels in i, n is the number of lychees, and s is the total number of pixels in the area, the formula for the proportion of red in the target area P _R is as follows:

S5055. Estimate the maturity of the whole bunch of lychees according to the proportion of red in the whole bunch of target areas.

Taking the color change characteristics of glutinous rice cakes and lychees during the ripening process as an example, the calculated proportion of red in the target area P _R is less than 25% and is estimated to be seven mature, 25-50% is estimated to be eight-ripe, and 50%-80% is estimated to be judged. As nine mature, more than 80% of the estimates are judged to be fully mature.

In order to obtain the trained litchi identification model, as shown in Figure 8, the method for monitoring the whole process of the litchi growth condition of the present embodiment, before step S501, further includes:

S801 , acquiring images of lychees in various growth periods and under various lighting conditions.

Images of lychees in various growth periods and under various illuminations can be collected in advance through networked cameras, and these image data are stored in the database of the cloud server 104, and these image data can be obtained by searching from the database.

S802 , constructing a training set and a validation set according to images of lychees in various growth periods and under various lighting conditions.

S803, input the training set into the target detection network for training to obtain a lychee recognition model; the target detection network in this embodiment is the YoloV3 target detection network.

S804, input the verification set into the lychee recognition model for verification, so as to adjust the parameters of the lychee recognition model.

It should be noted that although the above-described method operations are depicted in a particular order in the figures, this does not require or imply that the operations must be performed in the particular order, or that all illustrated operations must be performed to achieve desirable results. Conversely, the depicted steps may change the order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined to be performed as one step, and/or one step may be decomposed into multiple steps to be performed.

In this embodiment, the above-mentioned information such as the number of days after flowering, environmental data, the number of litchi fruits, the number of litchi bunches, and the degree of maturity is stored, a relational database based on time scale is constructed, and a regression method is used to establish the relationship between litchi growth environmental factors and ripening rates. The relationship model between lychees was analyzed to analyze the law between lychee growth environment factors and ripening rate, and to provide picking strategies for lychee plantation managers.

In order to facilitate user access, the whole-process monitoring system for lychee growth in this embodiment is further deployed with an IoT management platform 105 in the cloud server 104. The IoT management platform 105 provides users with management suggestions, images of lychees, and the number of days after flowering in the form of a website. , environmental data, the number of lychee fruits, the number of lychee strings, the degree of maturity and other information, that is, users can view management suggestions, lychee images, days after flowering, environmental data, number of lychee fruits, and number of lychee strings by visiting the web page of the IoT platform 105 , maturity, etc.

In order to make lychees trade with users as commodities, the whole-process monitoring system for the growth of lychees in this embodiment is further deployed with an e-commerce platform 106 in the cloud server, and the e-commerce platform 106 provides users with commodity information in the form of a website, and the commodity information includes Information such as lychee price, lychee image, days after flowering, environmental data, number of lychee fruits, number of lychee strings, maturity, etc., users can view these commodity information by visiting the webpage of the e-commerce platform 106, which integrates virtual Roaming module, the user can log in the e-commerce platform 106 to load and use the virtual roaming module.

The establishment of the virtual roaming module 1061 is shown in Figure 9 and includes the following steps:

S901. Model the physical objects in the lychee plantation, such as lychee trees, fruits, fences, etc., through modeling software, and import the built model into the virtual reality development software; The software uses unity3D.

S902, according to the actual environment of the lychee plantation, use virtual reality development software to establish a three-dimensional scene of the virtual lychee plantation.

S903, adding a character model to the established virtual lychee plantation, and nesting a camera in the character model.

S904 , adding a control script to enable the user to manipulate the character model to realize virtual sightseeing in the virtual lychee plantation, as shown in FIG. 10 .

Example 2:

As shown in FIG. 11 , this embodiment provides a whole-process monitoring device for litchi growth. The device is applied to a cloud server and includes a first acquisition module 1101 , a first detection module 1102 , a first prompt module 1103 , and a second prompt module 1104 and the second detection module 1105, the specific functions of each module are as follows:

The first acquisition module 1101 is used to acquire real-time lychee images.

The first detection module 1102 is configured to detect the growth period of the lychee according to the real-time lychee image.

The first prompting module 1103 is used to prompt the litchi plantation manager to manually release bees to promote pollination when it is detected that the lychee flowering period is approaching.

The second prompting module 1104 is used to prompt the lychee if the time when the air temperature is less than the first preset value exceeds the preset time during the flowering period, and the average daily air humidity after the preset time exceeds the second preset value. Plantation managers spray growth regulators and conduct hand pollination.

The second detection module 1105 is used to detect the maturity of the lychee after the lychee result is detected.

Further, as shown in FIG. 12 , the second detection module 1105 specifically includes:

The identification unit 11051 is used to identify the lychees in the real-time lychee images by using the trained lychee recognition model.

The clustering unit 11052 is configured to use each identified target center as a clustering object, perform clustering according to density, and regard the targets clustered into one class as a string of lychees.

The first calculation unit 11053 is used to convert each lychee region in a string of lychees to the HSV color space, and calculate the H channel component.

The second calculation unit 11054 is configured to calculate the red ratio of the entire string of target areas according to the H channel component.

The estimation unit 11055 is used for estimating the maturity degree of the whole bunch of lychees according to the proportion of red in the whole bunch of target areas.

Further, as shown in FIG. 13 , before the first acquisition module 1101, it further includes:

The second acquisition module 1301 is used to acquire images of lychees in various growth periods and under various lighting conditions.

The construction module 1302 is used for constructing a training set and a verification set according to images of lychees in various growth periods and under various illuminations.

The training module 1303 is used to input the training set into the target detection network for training to obtain a lychee recognition model.

The adjustment module 1304 is used to input the verification set into the lychee recognition model for verification, so as to adjust the parameters of the lychee recognition model.

For the specific implementation of each module in this embodiment, reference may be made to the foregoing Embodiment 1, and details are not repeated here. It should be noted that the device provided in this embodiment is only illustrated by the division of the above-mentioned functional modules. In practical applications, the above-mentioned function allocation may be completed by different functional modules as required, that is, the internal structure is divided into different functional modules. Function modules to complete all or part of the functions described above.

It can be understood that the terms "first", "second", etc. used in the apparatus of this embodiment can be used to describe various units, but these units are not limited by these terms. These terms are only used to distinguish the first module from another. For example, without departing from the scope of the present invention, a first prompting module may be referred to as a second prompting module, and similarly, a second prompting module may be referred to as a first prompting module, the first prompting module and The second prompt module is both a prompt module, but it is not the same prompt module.

Example 3:

This embodiment provides a storage medium, which is a computer-readable storage medium, and stores a computer program. When the program is executed by a processor, when the processor executes the computer program stored in the memory, the above-mentioned Embodiment 1 is implemented. The whole process monitoring method of lychee growth status is as follows:

Get real-time lychee images;

According to the real-time lychee image, detect the growth period of the lychee;

When it is detected that the lychee flowering period is coming, prompt the lychee plantation manager to manually release bees to promote pollination;

During the flowering period, if the time when the air temperature is lower than the first preset value exceeds the preset time, and the average daily air humidity after the preset time exceeds the second preset value, the lychee plantation manager will be prompted to spray growth regulators and carry out artificial pollination;

When the lychee results are detected, the ripeness of the lychee is detected.

The storage medium in this embodiment may be a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a U disk, a removable hard disk, and other media.

To sum up, based on the Internet of Things, the present invention can realize the whole-process monitoring of the growth status of lychees, and combine the detection results with agronomic requirements to provide management strategies for the managers of lychee plantations; The data, the number of lychee fruits, the number of lychee bunches, the degree of maturity and other information are stored, a relational database based on time scale is constructed, and a regression method is used to establish a relationship model between lychee growth environment factors and ripening rate, so as to analyze the lychee growth environment factors The regularity between the rate of ripening and ripening provides picking strategies for lychee plantation managers.

The above is only a preferred embodiment of the patent of the present invention, but the protection scope of the patent of the present invention is not limited to this. The technical solution and the inventive concept of the invention are equivalently replaced or changed, all belong to the protection scope of the patent of the present invention.

Claims (8)

1. a litchi growth condition whole process monitoring method, is applied to cloud server, it is characterized in that, described method comprises: Get real-time lychee images; According to the real-time lychee image, detect the growth period of the lychee; When it is detected that the lychee flowering period is coming, prompt the lychee plantation manager to manually release bees to promote pollination; During the flowering period, if the time when the air temperature is lower than the first preset value exceeds the preset time, and the average daily air humidity after the preset time exceeds the second preset value, the lychee plantation manager will be prompted to spray growth regulators and carry out artificial pollination; When the lychee results are detected, the maturity level of the lychee is detected; Described detecting the maturity degree of lychee, specifically includes: Use the trained lychee recognition model to identify lychees in real-time lychee images; Taking each identified target center as a clustering object, clustering by density, and treating the targets clustered into one class as a string of lychees; Convert each lychee region in a string of lychees to the HSV color space, and calculate the H channel component; According to the H channel component, calculate the red ratio of the entire string of target areas; Estimate the maturity of the whole bunch of lychees according to the proportion of red in the whole bunch of target areas; The calculation formula of the red ratio of the target area is as follows:

in,

is the red ratio of the target area, i is the H component value, H _i is the number of pixels in i, n is the number of lychees, and s is the total number of pixels in the area. 2. lychee growth condition whole-process monitoring method according to claim 1, is characterized in that, before described obtaining real-time lychee image, also comprises: Obtain images of lychees in various growth periods and under various lighting conditions; According to the images of lychees in various growth periods and under various illuminations, the training set and the validation set are constructed; The training set is input into the target detection network for training, and the lychee recognition model is obtained; Input the validation set into the lychee recognition model for verification to adjust the parameters of the lychee recognition model. 3. a litchi growth condition whole process monitoring system, it is characterised in that the system comprises an environmental data acquisition device, a networked camera, a router and a cloud server, the environmental data acquisition device and the networked camera are respectively connected with the router, and the router is connected to the router. cloud server connection; The cloud server is used to execute the whole-process monitoring method for the growth condition of litchi according to any one of claims 1-2. 4. litchi growth condition whole process monitoring system according to claim 3, is characterized in that, described environment data acquisition device comprises processor chip, LoRa communication module, air temperature and humidity sensor, soil moisture sensor, soil pH sensor, carbon dioxide concentration Sensor and light intensity sensor, the air temperature and humidity sensor, soil moisture sensor, soil pH sensor, carbon dioxide concentration sensor and light intensity sensor are respectively connected to the processor chip through the control circuit, and the processor chip is connected to the router through the LoRa communication module . 5. The whole process monitoring system for litchi growth conditions according to any one of claims 3-4, characterized in that, an Internet of Things management platform is deployed in the cloud server, and the Internet of Things management platform provides users with a website in the form of a website. Management advice, lychee images, days after flowering, environmental data, number of lychee fruits, number of lychee bunches, ripeness information. 6. The whole-process monitoring system for litchi growth conditions according to any one of claims 3-4, wherein an e-commerce platform is deployed in the cloud server, and the e-commerce platform provides users with commodity information in the form of a website , and the e-commerce platform integrates a virtual roaming module; The establishment process of the virtual roaming module includes: modeling the physical objects in the lychee plantation through modeling software, and importing the built model into the virtual reality development software; according to the environment of the actual lychee plantation, using the virtual reality development software Build a three-dimensional scene of a virtual lychee plantation; add a character model in the established virtual lychee plantation, and nest cameras in the character model; add a control script to enable users to manipulate the character model to achieve virtual sightseeing in the virtual lychee plantation. 7. A cloud server, comprising a processor and a memory for storing an executable program of the processor, is characterized in that, when the processor executes the program stored in the memory, realizes the lychee described in any one of claims 1-2 Growing status monitoring method throughout the process. 8. A storage medium storing a program, characterized in that, when the program is executed by a processor, the method for monitoring the whole process of the growth condition of litchi according to any one of claims 1-2 is realized.

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