WO2021159990A1 - 植物花期播报方法、系统及计算机可读存储介质 - Google Patents

植物花期播报方法、系统及计算机可读存储介质 Download PDF

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Publication number
WO2021159990A1
WO2021159990A1 PCT/CN2021/074752 CN2021074752W WO2021159990A1 WO 2021159990 A1 WO2021159990 A1 WO 2021159990A1 CN 2021074752 W CN2021074752 W CN 2021074752W WO 2021159990 A1 WO2021159990 A1 WO 2021159990A1
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plant
flowering
flowering period
species
blooming
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PCT/CN2021/074752
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English (en)
French (fr)
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徐青松
李青
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杭州睿琪软件有限公司
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Priority to US17/629,413 priority Critical patent/US12026964B2/en
Publication of WO2021159990A1 publication Critical patent/WO2021159990A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/70—Labelling scene content, e.g. deriving syntactic or semantic representations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/188—Vegetation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Definitions

  • the present invention relates to the technical field of artificial intelligence, and in particular to a method, system and computer readable storage medium for broadcasting plant flowering period.
  • the purpose of the present invention is to provide a method, system and computer readable storage medium for broadcasting plant flowering dates, which can display information such as specific species, viewing positions, and flowering dates of plants currently available for viewing to users.
  • the specific technical solutions are as follows:
  • the present invention provides a method for broadcasting the flowering period of plants, which includes:
  • identifying the plant in the image to obtain the type of the plant includes:
  • the species recognition model established by pre-training is used to recognize plants in the image to obtain the species of the plants, and the species recognition model is a neural network model.
  • the step of training the species recognition model includes: obtaining a training sample set, each sample in the training sample set is labeled with the type of plant; obtaining a test sample set, each sample in the test sample set is also labeled There are types of plants, where the test sample set is different from the training sample set; the species recognition model is trained based on the training sample set; the species recognition model is tested based on the test sample set; When the test result indicates that the recognition accuracy rate of the species recognition model is less than the preset accuracy rate, increase the number of samples in the training sample set for re-training; and when the test result indicates that the species recognition model is recognized accurately When the rate is greater than or equal to the preset accuracy rate, the training is completed.
  • the plant variety recognition model is a neural network model.
  • the open state includes: unopened, initial blooming, blooming, and ending.
  • the method for broadcasting the flowering period of the plant further includes:
  • the viewing recommendation information of the plant is pushed to users in the area where the shooting position is located.
  • the viewing recommendation information of the plant includes one or more of the following information: the specific species of the plant, the flowering period, and the geographic location of the viewing location.
  • the method for broadcasting the flowering period of the plant further includes:
  • the flowering period broadcast map is shared with other users in a specific area, where the specific area is an area within a preset distance from the shooting position of the picture.
  • the method for broadcasting the flowering period of the plant further includes:
  • the flowering period broadcast map corresponding to the selected geographic area is displayed to the user, or the flowering period broadcast map corresponding to all geographic areas is displayed to the user.
  • the method for broadcasting the flowering period of the plant further includes:
  • the specific species and/or scenic spot information of the plants at the selected viewing location are displayed on the blooming broadcast map.
  • the method for broadcasting the flowering period of the plant further includes:
  • the flowering period broadcast map Compare the flowering period broadcast map with the historically recorded previous flowering period broadcast map. If there is a viewing location that is not marked on the flowering period broadcast map in the previous flowering period broadcast map, the viewing will be marked on the flowering period broadcast map.
  • Location, the current flowering period of the plant is estimated according to the time of the last flowering period of the plant at the viewing location, and the specific species and current flowering period of the plant at the viewing location are correspondingly displayed in the flowering period broadcast map.
  • the method for broadcasting the flowering period of the plant further includes:
  • a zoomed-out icon of a target picture is displayed at the viewing location on the blooming broadcast map, wherein the target picture is a picture taken at the viewing location with the best open state of the plant.
  • the method for broadcasting the flowering period of the plant further includes:
  • the collection of pictures is shown to the user.
  • the present invention also provides a plant blooming date broadcast system.
  • the system includes a processor and a memory.
  • the memory stores instructions.
  • the plant blooming date broadcast is implemented.
  • the steps of the method include: receiving an image, identifying plants in the image to obtain the type of the plant; calling a plant variety recognition model corresponding to the type of the plant to identify the specific variety and open state of the plant Obtain the shooting time and shooting location of the picture, and determine the flowering period of the plant according to the shooting time and the open state; mark the shooting location as the viewing location of the plant on the flowering period broadcast map, and display it accordingly The specific species and flowering period of the plant.
  • the present invention also provides a computer-readable storage medium with instructions stored on the computer-readable storage medium, and when the instructions are executed, the steps of the method for broadcasting plant flowering are realized, the method comprising : Receive an image, identify the plant in the image to obtain the type of the plant; call the plant variety recognition model corresponding to the type of the plant to identify the specific variety and open state of the plant; obtain the shooting time of the picture And the shooting location, determining the flowering period of the plant according to the shooting time and the open state; marking the shooting location as the viewing location of the plant on the flowering period broadcast map, and correspondingly displaying the specific species and the flowering period of the plant .
  • the method, system and computer-readable storage medium for plant blooming provided by the present invention have the following advantages:
  • the shooting time and shooting position of the picture, the flowering period of the plant is determined according to the shooting time and the open state, and finally the shooting position is marked as the viewing location of the plant on the flowering period broadcast map, and the corresponding display is displayed.
  • the specific species and flowering period of the plant are described.
  • the present invention can upload plant images by any user, and determine the specific species, flowering period, and shooting location of plants according to the plant images uploaded by the user, thereby updating the viewing locations, specific species and flowering dates of the displayed plants in the flowering period broadcast map, so that all
  • the flowering period broadcast map displays relevant information about plants available for viewing in the current time period in time, so as to provide users with accurate reference information for viewing plants and improve the viewing experience of users.
  • FIG. 1 is a schematic diagram of a network environment of a plant blooming date broadcast system provided by an embodiment of the present invention
  • Fig. 2 is a schematic flow chart of a method for broadcasting plant flowering dates according to an embodiment of the present invention
  • Fig. 3 is a schematic structural diagram of a plant blooming date broadcast system provided by an embodiment of the present invention.
  • Fig. 1 shows a schematic diagram of a network environment 100 of a plant flowering period broadcast system according to an embodiment of the present invention.
  • the network environment 100 of the plant blooming broadcast system may include a mobile device 102, a remote server 103, a training device 104, and a database 105, which are wired or wirelessly coupled to each other through the network 106.
  • the network 106 may be embodied as a wide area network (such as a mobile phone network, a public switched telephone network, a satellite network, the Internet, etc.), a local area network (such as Wi-Fi, Wi-Max, ZigBeeTM, BluetoothTM, etc.) and/or other forms of networking functions.
  • the mobile device 102 may include a mobile phone, a tablet computer, a laptop computer, a personal digital assistant, and/or other computing devices configured to capture, store, and/or transmit images such as digital photos. Therefore, the mobile device 102 may include an image capture device such as a digital camera and/or may be configured to receive images from other devices.
  • the mobile device 102 may include a display.
  • the display may be configured to provide one or more user interfaces to the user 101, the user interface may include multiple interface elements, the user 101 may interact with the interface elements, and so on. For example, the user 101 can use the mobile device 102 to photograph a certain plant and upload or store the image.
  • the mobile device 102 can output detailed introductions about the plant's category, specific species, and flowering period to the user, or can display the flowering period broadcast map to the user, push the viewing recommendation information of the plant, and prompt the user to share the plant with other users or friends.
  • the remote server 103 may be configured to analyze images and the like received from the mobile device 102 via the network 106 to determine the type of plant, and to identify the specific variety and open status of the plant and other detailed information.
  • the remote server 103 may also be configured to create and train the plant variety recognition model and species recognition model of this embodiment. The specific training process of the plant variety recognition model and the species recognition model will be described below in conjunction with specific embodiments.
  • the training device 104 may be coupled to the network 106 to facilitate the training of the plant variety recognition model and the species recognition model.
  • the training device 104 may have multiple CPUs and/or GPUs to assist in training the plant species recognition model and the species recognition model.
  • the database 105 may be coupled to the network 106 and provide data required by the remote server 103 for relevant calculations.
  • the database 105 may include a sample library storing a large number of images of different types of plants, and a sample library of images of multiple varieties of plants under the same category.
  • the sample library may include a large number of image samples of different varieties of cherry blossoms in different locations, different seasons, weather at different times, and different shooting angles.
  • the selected plant photos taken by the user can also be stored in a sample library corresponding to the plant species.
  • the location information, seasonal information, and time information of the plant can also be recorded in the database.
  • the database can be implemented by various database technologies known in the art.
  • the remote server 103 can access the database 105 to perform related operations as needed.
  • the network environment 100 herein is only an example. Those skilled in the art can add more devices or delete some devices as needed, and can modify the functions and configurations of some devices. In the following, a description will be given taking the broadcast of the flowering period of the cherry blossoms as an example.
  • the method for broadcasting the flowering period of a plant includes the following steps:
  • Step S101 receiving an image, and identifying plants in the image to obtain the type of the plant.
  • the received image may be previously stored by the user or captured by the user in real time.
  • the image may be previously stored in the mobile device 102 by the user or captured in real time by the user using an external camera connected to the mobile device 102 or a built-in camera of the mobile device 102.
  • the user can also obtain the image in real time via the network.
  • a species recognition model established by pre-training may be used to recognize plants in the image to obtain the species of the plants.
  • the training step of the species recognition model may include: step a, obtaining a training sample set, each sample in the training sample set is labeled with the type of plant; step b, obtaining a test sample set, each of the test sample sets This book is also marked with the types of plants, where the test sample set is different from the training sample set; step c, training the species recognition model based on the training sample set; step d, based on the test sample set Test the species recognition model; step e, when the test result indicates that the recognition accuracy of the species recognition model is less than a preset accuracy rate, increase the number of samples in the training sample set for retraining; and step f When the test result indicates that the recognition accuracy rate of the species recognition model is greater than or equal to the preset accuracy rate, the training is completed.
  • a certain number of image samples labeled with corresponding information are acquired for each plant type, and the number of image samples prepared for each plant type may be equal or different.
  • the corresponding information labeled for each image sample may include the plant species in the image sample (including scientific name, nickname, category name of botanical classification, etc.).
  • the image samples obtained for each plant species can include as much as possible the different shooting angles, different lighting conditions, different weather (for example, the same plant may have different forms in sunny days and rainy days), different months or seasons (for example, the same The shape of plants may be different in different months or seasons), different times (for example, the shape of the same plant may be different in the morning and night of each day), different growth environments (for example, the shape of the same plant may be different indoors and outdoors), and different geographies An image of a location (for example, the same plant may grow differently in different geographic locations).
  • the corresponding information labeled for each image sample may also include information such as the shooting angle, illumination, weather, season, time, growth environment, or geographic location of the image sample.
  • the image samples that have undergone the above-mentioned annotation processing can be divided into a training sample set for training the species recognition model and a test sample set for testing the training result.
  • the number of samples in the training sample set is significantly greater than the number of samples in the test sample set.
  • the number of samples in the test sample set can account for 5% to 20% of the total number of image samples, and the corresponding training sample set The number of samples can account for 80% to 95% of the total number of image samples.
  • the number of samples in the training sample set and the test sample set can be adjusted as needed.
  • the training sample set can be used to train the species recognition model, and the test sample set can be used to test the recognition accuracy of the trained species recognition model. If the recognition accuracy does not meet the requirements, increase the number of image samples in the training sample set, and use the updated training sample set to retrain the species recognition model until the recognition accuracy of the trained species recognition model meets the requirements. If the recognition accuracy meets the requirements, the training ends. In one embodiment, it can be judged whether the training can end based on whether the recognition accuracy rate is less than the preset accuracy rate. In this way, a trained species recognition model whose output accuracy meets the requirements can be used to recognize plant species.
  • the species recognition model is a neural network model, such as a deep convolutional neural network (CNN) or a deep residual network (Resnet).
  • the deep convolutional neural network is a deep feedforward neural network, which uses the convolution kernel to scan the plant image, extracts the features to be recognized in the plant image, and then recognizes the features to be recognized in the plant.
  • the original plant image can be directly input to the deep convolutional neural network model without preprocessing the plant image.
  • the deep convolutional neural network model has higher recognition accuracy and recognition efficiency.
  • the deep residual network model Compared with the deep convolutional neural network model, the deep residual network model adds an identity mapping layer, which can avoid the accuracy saturation or even saturation caused by the convolutional neural network as the network depth (the number of layers in the network) increases.
  • the identity mapping function of the identity mapping layer in the residual network model needs to satisfy: the sum of the input of the identity mapping function and the residual network model is equal to the output of the residual network model.
  • the residual network model changes the output more obviously, so it can greatly improve the recognition accuracy and recognition efficiency of plant physiological period recognition, and then improve the recognition accuracy and recognition efficiency of plants.
  • Step S102 Invoking the plant variety recognition model corresponding to the type of plant to identify the specific variety and open state of the plant.
  • the aforementioned species recognition model can only recognize the types of plants and cannot recognize the specific species of plants, in an embodiment of the present invention, corresponding plant species recognition models are pre-trained for different plant types. After the plant species is recognized in step S101, the plant species recognition model corresponding to the plant species is called to perform recognition again, so as to recognize the specific species of plants in the image, and at the same time, the open state of the plants can be recognized.
  • step S101 recognizes that the plant is a cherry blossom
  • a pre-trained cherry blossom variety recognition model is called to re-recognize the image, so as to identify the specific variety and open state of the cherry blossom.
  • the open state of a plant can be divided according to the state of the flower bud and the degree of the whole plant blooming. For example, it can be divided into four states: unopened, early blooming, blooming and ending. For example, if the plant has no buds or only a few buds, then The open state is unopened. When the plant has more flower buds or a few flower buds open, the open state is the initial opening. If the proportion of the whole plant flowering exceeds a certain threshold (for example, 80%), the open state is in full bloom. If the proportion of withering exceeds a certain threshold (for example, 70%), the open state is closed.
  • a certain threshold for example, 80%
  • the open state is in full bloom.
  • a certain threshold for example, 70%
  • the training method of the plant variety recognition model corresponding to each plant species is basically the same. Taking the cherry blossom as an example, the training method of the cherry blossom variety recognition model is briefly introduced. There are many varieties of sakura, such as Hanhi Sakura, Kawazu Sakura, Rainy Weeping Sakura, Somei Yoshino Sakura, Oshima Sakura, Daisy Sakura, etc. Different varieties of cherry blossoms have their unique morphological characteristics. When training the cherry blossom species recognition model, obtain a certain number of image samples labeled with corresponding information for each cherry blossom species. The number of image samples prepared for each cherry blossom can be equal or unequal, and the labeled information should include image samples The varieties of cherry blossoms and their blooming state.
  • the image samples obtained for each cherry blossom variety can include as much as possible the different shooting angles, different lighting conditions, different weather (for example, the same plant may have different forms on sunny days and rainy days), different months or seasons (for example, the same The shape of plants may be different in different months or seasons), different times (for example, the shape of the same plant may be different in the morning and night of each day), different growth environments (for example, the shape of the same plant may be different indoors and outdoors), and different geographies An image of a location (for example, the same plant may grow differently in different geographic locations).
  • the corresponding information labeled for each image sample may also include information such as the shooting angle, illumination, weather, season, time, growth environment, or geographic location of the image sample.
  • the cherry blossom species recognition model is also a neural network-based model, such as a deep convolutional neural network (CNN) or a deep residual network (Resnet). After the cherry blossom image samples are obtained, the cherry blossom species recognition model can be trained according to the above-mentioned training process of the species recognition model, which will not be repeated here.
  • CNN deep convolutional neural network
  • Resnet deep residual network
  • the above-mentioned plant species identification model can simultaneously identify the specific species and the open state of plants.
  • a separate plant variety recognition model and an open state recognition model can also be set at the same time.
  • a separate plant variety recognition model is only used to identify specific species of plants, and the open state recognition model is used for Recognizing the open state of plants, it can be understood that by setting up separate plant variety recognition models and open state recognition models, the accuracy of variety recognition and open state recognition can be improved.
  • the open state recognition model is used to recognize the open state of the plants in the image, it is also possible to combine the shooting position and shooting time information of the image to perform a second confirmation of the recognition result.
  • the open state recognition model recognizes a certain When the open state of a plant in an image is in full bloom, combined with the shooting location and shooting time of the image, if it can be predicted that the open state of the same species of plants at this shooting location during the shooting time should be in full bloom, then it can be confirmed that The recognition result of the open state recognition model is accurate. Otherwise, it can be considered that the recognition result of the open state recognition model may be wrong. In this way, the open state of the plants in the image can be re-identified or directly manually recognized, or further The open state recognition model is retrained until the recognition accuracy of the open state recognition model meets the requirements. It can be seen that the accuracy of the recognition result of the open state recognition model can be guaranteed through the second confirmation of the shooting position and the shooting time.
  • Step S103 Acquire the shooting time and shooting position of the image, and determine the flowering period of the plant according to the shooting time and the open state.
  • the shooting time and shooting location of the image belong to the attribute information of the image. Therefore, the shooting time and shooting location of the image can be obtained from the attribute information of the image.
  • the flowering period of the plant can be determined according to the shooting time and the open state.
  • the flowering period of a plant may include the first blooming period and the blooming period.
  • the image was taken on February 20, 2019, and the shooting location was location A.
  • the cherry blossoms at location A can be predicted to bloom and bloom at location A based on the historically recorded flowering information For example, in the last flowering period, the cherry blossoms at this location A had their first blooming date on March 1, 2018 and their full blooming date was March 15, 2018, then it is predicted that the cherry blossoms at this location A will be in the current blooming period
  • the first blooming date is March 1, 2019, and the blooming date is March 15, 2019;
  • the shooting time can be used as the first blooming period of the cherry blossoms at the location A, and then The blooming period of the cherry blossoms at the location A is predicted based on the initial blooming period; if the open state is in full bloom, the shooting time is taken as the blooming period of the cherry blossoms at the location A.
  • Step S104 Mark the shooting location as the viewing location of the plant on the flowering period broadcast map, and correspondingly display the specific species and flowering period of the plant.
  • the display of the mobile device 102 provides the user with a user interface, and the flowering period broadcast map can be displayed on the user interface.
  • the flowering period broadcast map can be used to display a specific plant species such as various viewing locations of cherry blossoms. It is also possible to display various viewing locations of multiple plant species such as cherry blossoms and peach blossoms at the same time, which is not limited in this embodiment.
  • step S103 the shooting location of the image has been obtained, and the flowering period and specific species of the cherry blossoms in the image are also obtained, and then the flowering period broadcast map can be marked
  • the shooting position is a viewing spot of cherry blossoms, and the specific varieties and flowering dates of cherry blossoms are correspondingly displayed at the same time.
  • the shooting location is park A, and park A is marked with a specific sign in the flowering period broadcast map, and the specific species and flowering period of the cherry blossoms are displayed at the specific sign.
  • the user can see from the flowering period broadcast map that the location corresponding to the shooting position has cherry blossoms available for viewing, and learn about the specific varieties and flowering dates of the cherry blossoms here.
  • the method for broadcasting plant flowering period provided by the embodiment of the present invention can upload plant images by any user, and determine the specific species, flowering period, and shooting location of plants according to the plant images uploaded by the user, the viewing locations of the plants are updated in the flowering period broadcasting map.
  • the flowering period broadcast map can timely display relevant information of plants available for viewing in the current time period, thereby providing users with accurate reference information for viewing plants and improving the viewing experience of users.
  • the method for reporting the flowering period of the plant in this embodiment may further include: pushing the plant to users in the area where the shooting position is when the open state of the plant is early blooming or in full bloom.
  • Recommended viewing information That is, if it is recognized that the open state of the plant is early blooming or in full bloom, it means that the plant is currently in the best viewing time period.
  • the remote server 103 can report to other users in the area where the shooting location is located. Push the viewing recommendation information of the plant, prompting other users in the area where the shooting location is located to watch the plant at the shooting location. A user near the park A pushes the viewing recommendation information of the plant.
  • the viewing recommendation information of the plant includes one or more of the following information: the specific species of the plant, the flowering period, the geographic location of the viewing location, etc. Of course, it may also include other information, which is not limited in this embodiment.
  • the way of pushing the viewing recommendation information may be in the form of short message, in-app message, or other forms, which is not limited in this embodiment.
  • the flowering period broadcast map can also be shared with other users in a specific area, where the specific area is within a preset distance from the shooting position of the picture Area. That is, the blooming broadcast map can be shared with other users or friends in a specific area (for example, within 5 kilometers from the shooting location).
  • the user uploads a photographed plant picture, and the plant picture is marked on the blooming broadcast map.
  • the user may choose to actively share the flowering period broadcast map with other users or friends, so as to invite other users or friends to this viewing location to view the plant.
  • the blooming broadcast map can be shared with other users in the application, or can be shared with other users through other social platforms.
  • the flowering period broadcast map may be processed and shared in the form of pictures, or the flowering period broadcast map may be processed into an H5 page for sharing, which is not limited in this embodiment.
  • the user can click to view the recommended plant viewing locations in the current time period.
  • the plant viewing locations are generally in parks, botanical gardens and other scenic spots.
  • the mobile device 102 can In response to the user's operation of selecting the viewing location, the specific species and/or scenic spot information of the plants at the selected viewing location are displayed on the blooming broadcast map, such as scenic spot introduction, traffic information, traffic information, tickets, and others Charge information in order to provide users with more information about where to see plants.
  • the user wishes to view the flowering status of the plant in a certain area or the entire area
  • the mobile device 102 may respond to the user's operation to display the flowering period broadcast map corresponding to the selected geographic area to the user
  • the flowering period broadcast map corresponding to all geographic regions is displayed to the user. For example, if the user chooses to view the flowering status of the plant in the Beijing area, the mobile device 102 will display the flowering period broadcast map of the Beijing area to the user.
  • the user chooses to view the flowering status of the plant in all areas of China, it will include The flowering period broadcast map of all regions in China is shown to users.
  • the method for broadcasting the flowering period of the plant in order to mark all the places available for viewing of the plant, and to provide users with comprehensive viewing information, the flowering period broadcasting map and the historical record of the previous flowering period can also be reported.
  • the map is compared, and if there is a viewing location that is not marked on the flowering period reporting map in the previous flowering period broadcasting map, the viewing location is marked on the flowering period broadcasting map, and then the viewing location is based on the previous viewing location of the plant.
  • the time of the flowering period predicts the current flowering period of the plant, and correspondingly displays the specific species and the current flowering period of the plant at the viewing location in the flowering period broadcast map.
  • the current flowering period broadcast map has marked three viewing locations of the plants, namely locations A, B, and C, and the flowering period broadcast map of the previous flowering period has marked 4 locations for the plants.
  • the viewing locations are locations A, B, C, and D.
  • the plant (because no user has taken a plant picture at location D and uploaded it in the current flowering period, the location D cannot be marked on the flowering period broadcast map in the current flowering period), therefore, the location D is also marked in the flowering period broadcast map of the current flowering period
  • the location D is also marked in the flowering period broadcast map of the current flowering period
  • To view the location at the same time obtain the specific species of the plant at the location D of the last flowering period and the specific time of the flowering period, and estimate the time of the current flowering period based on the time of the last flowering period, so that the location D is correspondingly displayed on the broadcast map of the current flowering period.
  • the specific species of the plant and the current flowering period can be used to update the viewing locations in the flowering period broadcast map of the current flowering period, so as to make up for the insufficiency of marking viewing locations by uploading plant images by the user.
  • a zoomed-out icon of the target picture may be displayed at the viewing location on the blooming broadcast map, where the target picture is a picture taken at the viewing location with the best open state of the plant.
  • the most recently taken picture (for example, within 24 hours) of the plant in the best open state may be used as the zoom-out icon at the corresponding viewing location.
  • the picture with the best open state for example, you can choose the picture with more than 80% of the whole plant blooming, you can also choose the picture with the flowers occupying the most area of the picture, or the picture with the best open state identified by the above-mentioned open state recognition model picture.
  • the received images of the plants taken at the same viewing location may also be combined into a picture set.
  • the pictures in the picture set shown may be taken by the same user or may include those taken by other users.
  • Each picture of can be sorted comprehensively according to the shooting time and open state.
  • the user chooses to view the picture collection (for example, clicking the zoom-out icon described above)
  • the picture collection is displayed to the user so that the user can view the flowering situation of the plant at the viewing location.
  • the present invention also provides a plant blooming date broadcast system.
  • the plant flowering date broadcast system 200 may include a processor 210 and a memory 220.
  • the memory 220 stores instructions. When the instructions are executed by the processor 210, the steps in the plant flowering date broadcast method described above can be implemented. .
  • the processor 210 may perform various actions and processing according to instructions stored in the memory 220.
  • the processor 210 may be an integrated circuit chip with signal processing capability.
  • the above-mentioned processor may be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, or discrete hardware components.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA off-the-shelf programmable gate array
  • Various methods, steps, and logical block diagrams disclosed in the embodiments of the present invention can be implemented or executed.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc., and may be an X86 architecture or an ARM architecture.
  • the memory 220 stores executable instructions, and the instructions are executed by the processor 210 for the above-mentioned method for broadcasting the plant flowering period.
  • the memory 220 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory.
  • the non-volatile memory may be read only memory (ROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), or flash memory.
  • Volatile memory may be random access memory (RAM), which acts as an external cache.
  • RAM synchronous dynamic random access memory
  • SDRAM synchronous dynamic random access memory
  • DDRSDRAM double data rate synchronous dynamic random access memory
  • ESDRAM enhanced synchronous dynamic random access memory
  • SLDRAM synchronous connection dynamic random access memory
  • DR RAM direct memory bus random access memory
  • the present invention also provides a computer-readable storage medium with instructions stored on the computer-readable storage medium.
  • the instructions When executed, the steps in the above-described method for broadcasting plant flowering can be realized.
  • the computer-readable storage medium in the embodiment of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory. It should be noted that the computer-readable storage media described herein are intended to include, but are not limited to, these and any other suitable types of memory.
  • each block in the flowchart or block diagram may represent a module, program segment, or part of code, and the module, program segment, or part of code contains one or more logic for implementing the specified Executable instructions for the function.
  • the functions marked in the block may also occur in a different order from the order marked in the drawings. For example, two blocks shown one after another can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved.
  • each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be implemented by a dedicated hardware-based system that performs the specified functions or operations Or it can be realized by a combination of dedicated hardware and computer instructions.
  • the various exemplary embodiments of the present invention may be implemented in hardware or special purpose circuits, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device.
  • firmware or software that may be executed by a controller, microprocessor, or other computing device.

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Abstract

本发明提供了一种植物花期播报方法、系统及计算机可读存储介质，方法包括：接收图像，识别所述图像中的植物以得到所述植物的种类；调用所述植物的种类对应的植物品种识别模型，识别所述植物的具体品种以及开放状态；获取所述图片的拍摄时间和拍摄位置，根据所述拍摄时间和所述开放状态确定所述植物的花期；在花期播报地图上标记所述拍摄位置为所述植物的观赏地点，并对应显示所述植物的具体品种以及花期。本发明提供的方案可以通过花期播报地图向用户展示当前可供观赏的植物的具体品种、位置以及花期等信息，为用户观赏植物提供准确的参考信息，提高用户的观赏体验。

Description

植物花期播报方法、系统及计算机可读存储介质 技术领域
本发明涉及人工智能技术领域，特别涉及一种植物花期播报方法、系统及计算机可读存储介质。
背景技术
目前，越来越多的人喜欢到户外如公园、植物园等地观赏植物，例如观赏樱花。然而，人们往往不甚了解自己周围都有哪些植物可供观赏，而且也难以了解到各个观赏地点的植物的最佳观赏花期。因此，存在对观赏植物进行花期播报的需求。
发明内容
本发明的目的在于提供一种植物花期播报方法、系统及计算机可读存储介质，可以向用户展示当前可供观赏的植物的具体品种、观赏位置以及花期等信息。具体技术方案如下：
为达到上述目的，本发明提供了一种植物花期播报方法，包括：
接收一植物的图像，识别所述图像中的植物以得到所述植物的种类；
调用所述植物的种类对应的植物品种识别模型，识别所述植物的具体品种以及开放状态；
获取所述图片的拍摄时间和拍摄位置，根据所述图片的拍摄时间和所述植物的开放状态确定所述植物的花期；
在一花期播报地图上标记所述图片的拍摄位置为所述植物的观赏地点，并对应显示所述植物的具体品种以及花期。
可选的，识别所述图像中的植物以得到所述植物的种类，包括：
利用预先训练建立的物种识别模型识别所述图像中的植物以得到所述植物的种类，所述物种识别模型为神经网络模型。
可选的，所述物种识别模型的训练步骤包括：获取训练样本集，所述训练样本集中的每一样本标注有植物的种类；获取测试样本集，所述测试样本 集中的每一样本也标注有植物的种类，其中，所述测试样本集不同于所述训练样本集；基于所述训练样本集对所述物种识别模型进行训练；基于所述测试样本集对所述物种识别模型进行测试；在所述测试结果指示所述物种识别模型的识别准确率小于预设准确率时，增加所述训练样本集中的样本数量进行再次训练；以及在所述测试结果指示所述物种识别模型的识别准确率大于或等于所述预设准确率时，完成训练。
可选的，所述植物品种识别模型为神经网络模型。
可选的，所述开放状态包括：未开、初开、盛开和完结。
可选的，所述植物花期播报方法还包括：
当所述植物的开放状态为初开或盛开时，向所述拍摄位置所在区域内的用户推送所述植物的观赏推荐信息。
可选的，所述植物的观赏推荐信息包括以下信息中的一个或多个：所述植物的具体品种、花期、观赏地点的地理位置。
可选的，所述植物花期播报方法还包括：
当所述植物的开放状态为盛开时，将所述花期播报地图分享给特定区域内的其它用户，其中所述特定区域为与所述图片的拍摄位置在预设距离内的区域。
可选的，所述植物花期播报方法还包括：
响应于用户操作，将选定地理区域对应的所述花期播报地图展示给用户，或者，将所有地理区域对应的所述花期播报地图展示给用户。
可选的，所述植物花期播报方法还包括：
响应于用户选择一观赏地点的操作，在所述花期播报地图中显示选定的观赏地点处的所述植物的具体品种和/或景点信息。
可选的，所述植物花期播报方法还包括：
将所述花期播报地图与历史记录的上一花期播报地图进行比对，若上一花期播报地图中存在所述花期播报地图未标注的观赏地点，则在所述花期播报地图中标注出该观赏地点，再根据该观赏地点处所述植物的上一花期的时间预估所述植物的当前花期，并在所述花期播报地图中对应显示该观赏地点处所述植物的具体品种以及当前花期。
可选的，所述植物花期播报方法还包括：
在所述花期播报地图上的观赏地点处显示目标图片的缩小图标，其中所述目标图片为在所述观赏地点处拍摄的所述植物的开放状态最好的图片。
可选的，所述植物花期播报方法还包括：
将接收到的在所述观赏地点处拍摄的所述植物的图像组成图片集，其中各个图片按照拍摄时间和开放状态进行排序；
响应于用户操作，将所述图片集展示给用户。
基于同一发明构思，本发明还提供了一种植物花期播报系统，所述系统包括处理器和存储器，所述存储器上存储有指令，当所述指令被所述处理器执行时，实现植物花期播报方法的步骤，所述方法包括：接收图像，识别所述图像中的植物以得到所述植物的种类；调用所述植物的种类对应的植物品种识别模型，识别所述植物的具体品种以及开放状态；获取所述图片的拍摄时间和拍摄位置，根据所述拍摄时间和所述开放状态确定所述植物的花期；在花期播报地图上标记所述拍摄位置为所述植物的观赏地点，并对应显示所述植物的具体品种以及花期。
基于同一发明构思，本发明还提供了一种计算机可读存储介质，所述计算机可读存储介质上存储有指令，当所述指令被执行时，实现植物花期播报方法的步骤，所述方法包括：接收图像，识别所述图像中的植物以得到所述植物的种类；调用所述植物的种类对应的植物品种识别模型，识别所述植物的具体品种以及开放状态；获取所述图片的拍摄时间和拍摄位置，根据所述拍摄时间和所述开放状态确定所述植物的花期；在花期播报地图上标记所述拍摄位置为所述植物的观赏地点，并对应显示所述植物的具体品种以及花期。
与现有技术相比，本发明提供的植物花期播报方法、系统及计算机可读存储介质具有以下优点：
首先接收用户上传的植物图像，识别所述图像中的植物以得到所述植物的种类，然后调用所述植物的种类对应的植物品种识别模型，识别所述植物的具体品种以及开放状态，同时获取所述图片的拍摄时间和拍摄位置，根据所述拍摄时间和所述开放状态确定所述植物的花期，最后在花期播报地图上标记所述拍摄位置为所述植物的观赏地点，并对应显示所述植物的具体品种 以及花期。本发明可通过任意用户上传植物图像，并根据用户上传的植物图像确定植物的具体品种、花期以及拍摄地点，从而在花期播报地图中更新展示植物的观赏地点以及具体品种和花期，如此可使所述花期播报地图中及时显示当前时间段内可供观赏的植物的相关信息，从而为用户观赏植物提供准确的参考信息，提高用户的观赏体验。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明一实施例提供的植物花期播报系统的网络环境示意图；
图2是本发明一实施例提供的植物花期播报方法的流程示意图；
图3是本发明一实施例提供的植物花期播报系统的结构示意图。
具体实施方式
以下结合附图和具体实施例对本发明提出的一种植物花期播报方法、系统及计算机可读存储介质作进一步详细说明。根据下面说明，本发明的优点和特征将更清楚。需说明的是，附图均采用非常简化的形式且均使用非精准的比例，仅用以方便、明晰地辅助说明本发明实施例的目的。须知，本说明书所附图式所绘示的结构、比例、大小等，均仅用以配合说明书所揭示的内容，以供熟悉此技术的人士了解与阅读，并非用以限定本发明实施的限定条件，故不具技术上的实质意义，任何结构的修饰、比例关系的改变或大小的调整，在不影响本发明所能产生的功效及所能达成的目的下，均应仍落在本发明所揭示的技术内容能涵盖的范围内。
本申请的发明人深入研究了用于植物花期播报的方法及系统。图1示出了根据本发明实施例的植物花期播报系统的网络环境100的示意图。
植物花期播报系统的网络环境100可以包括移动设备102、远程服务器103、训练设备104和数据库105，它们通过网络106彼此有线或无线地耦接。 网络106可以体现为广域网(诸如移动电话网络、公共交换电话网络、卫星网络、互联网等)、局域网(诸如Wi-Fi、Wi-Max、ZigBeeTM、BluetoothTM等)和/或其它形式的联网功能。
移动设备102可以包括移动电话、平板计算机、膝上型计算机、个人数字助理和/或被配置用于捕获、存储和/或传输诸如数字照片之类的图像的其它计算装置。因此，移动设备102可以包括诸如数字相机之类的图像捕获装置和/或可以被配置为从其它装置接收图像。移动设备102可以包括显示器。显示器可以被配置用于向用户101提供一个或多个用户界面，所述用户界面可以包括多个界面元素，用户101可以与界面元素进行交互等。例如，用户101可以使用移动设备102对某一植物进行拍摄并上传或存储图像。移动设备102可以向用户输出有关该植物的类别信息、具体品种和花期等详细介绍等，或者可以向用户展示花期播报地图，以及推送该植物的观赏推荐信息、提示用户向其它用户或好友分享该植物的花期等。
远程服务器103可以被配置为对经由网络106从移动设备102接收的图像等进行分析以确定植物的种类，并识别该植物的具体品种以及开放状态等详细信息。远程服务器103还可以被配置为创建并训练本实施例的植物品种识别模型和物种识别模型。植物品种识别模型和物种识别模型的具体训练过程将在下文结合具体实施例进行描述。
训练设备104可以耦合到网络106以促进植物品种识别模型和物种识别模型的训练。训练设备104可以具有多个CPU和/或GPU以辅助训练植物品种识别模型和物种识别模型。
数据库105可以耦合到网络106并提供远程服务器103进行相关计算所需的数据。例如，数据库105可以包括存储有大量的不同种类的植物的图像的样本库，以及同一种类下的多个品种的植物的图像的样本库。在一个实施例中，以樱花为例，样本库可以包括大量不同位置、不同季节、不同时间天气和不同拍摄角度下的不同品种的樱花的图像样本。在一个实施例中，还可以将用户所拍摄的选定植物照片存储到与该植物种类相对应的样本库中，同时，还可以在数据库中记录与该植物的位置信息、季节信息、时间信息、天气信息和拍摄角度信息中的一个或多个相对应的生理周期信息和形态信息。 数据库可以采取本领域中已知的各种数据库技术来实现。远程服务器103可以根据需要访问数据库105以进行相关操作。
应该理解的是，本文的网络环境100仅仅是一个示例。本领域技术人员可以根据需要，增加更多的装置或删减一些装置，并且可以对一些装置的功能和配置进行修改。下面，将以樱花的花期播报为例进行描述。
下面结合图2来介绍本发明一实施例提供的一种植物花期播报方法。如图2所示，本发明一实施例提供的植物花期播报方法包括如下步骤：
步骤S101，接收图像，识别所述图像中的植物以得到所述植物的种类。
如前所述，接收的图像可以是用户先前存储的或者是用户实时拍摄的。例如，所述图像可以是用户先前存储在移动设备102中或者是用户使用连接到移动设备102的外置摄像头或移动设备102内置的摄像头进行实时拍摄的。在一个实施例中，用户还可以通过网络实时获取所述图像。
在一个实施例中，可以利用预先训练建立的物种识别模型识别所述图像中的植物以得到所述植物的种类。所述物种识别模型的训练步骤可以包括：步骤a，获取训练样本集，所述训练样本集中的每一样本标注有植物的种类；步骤b，获取测试样本集，所述测试样本集中的每一样本也标注有植物的种类，其中，所述测试样本集不同于所述训练样本集；步骤c，基于所述训练样本集对所述物种识别模型进行训练；步骤d，基于所述测试样本集对所述物种识别模型进行测试；步骤e，在所述测试结果指示所述物种识别模型的识别准确率小于预设准确率时，增加所述训练样本集中的样本数量进行再次训练；以及步骤f，在所述测试结果指示所述物种识别模型的识别准确率大于或等于所述预设准确率时，完成训练。
例如，为每个植物种类获取一定数量的标注有对应信息的图像样本，为每个植物种类准备的图像样本的数量可以相等也可以不等。为每个图像样本标注的对应信息可以包括图像样本中的植物种类(包括学名、别称、植物学分类的类别名称等)。为每个植物种类获取的图像样本可以尽可能包括该种类的植物的不同拍摄角度、不同光照条件、不同天气(例如同一植物在艳阳天和雨天的形态可能不同)、不同月份或季节(例如同一植物在不同月份或季节的形态可能不同)、不同时间(例如同一植物在每天的早晨和夜晚的形态可能 不同)、不同生长环境(例如同一植物在室内和室外生长的形态可能不同)、不同地理位置(例如同一植物在不同的地理位置生长的形态可能不同)的图像。在这些情况下，为每个图像样本标注的对应信息还可以包括该图像样本的拍摄角度、光照、天气、季节、时间、生长环境或地理位置等信息。
可以将经过上述标注处理的图像样本划分为用于训练物种识别模型的训练样本集和用于对训练结果进行测试的测试样本集。通常训练样本集内的样本的数量明显大于测试样本集内的样本的数量，例如，测试样本集内的样本的数量可以占总图像样本数量的5％到20％，而相应的训练样本集内的样本的数量可以占总图像样本数量的80％到95％。本领域技术人员应该理解的是，训练样本集和测试样本集内的样本数量可以根据需要来调整。
可以利用训练样本集对物种识别模型进行训练，并利用测试样本集对经过训练的物种识别模型的识别准确率进行测试。若识别准确率不满足要求，则增加训练样本集中的图像样本的数量，并利用更新的训练样本集重新对物种识别模型进行训练，直到经过训练的物种识别模型的识别准确率满足要求为止。若识别准确率满足要求，则训练结束。在一个实施例中，可以基于识别准确率是否小于预设准确率来判断训练是否可以结束。如此，输出准确率满足要求的经过训练的物种识别模型可以用于进行植物种类的识别。
所述物种识别模型为神经网络模型，例如可以是深度卷积神经网络(CNN)或深度残差网络(Resnet)。其中，深度卷积神经网络为深度前馈神经网络，其利用卷积核扫描植物图像，提取出植物图像中待识别的特征，进而对植物待识别的特征进行识别。另外，在对植物图像进行识别的过程中，可以直接将原始植物图像输入深度卷积神经网络模型，而无需对植物图像进行预处理。深度卷积神经网络模型相比于其他的识别模型，具备更高的识别准确率以及识别效率。而深度残差网络模型相比于深度卷积神经网络模型增加了恒等映射层，可以避免随着网络深度(网络中叠层的数量)的增加，卷积神经网络造成的准确率饱和、甚至下降的现象。残差网络模型中恒等映射层的恒等映射函数需要满足：恒等映射函数与残差网络模型的输入之和等于残差网络模型的输出。引入恒等映射以后，残差网络模型对输出的变化更加明显，因此可以大大提高植物生理期识别的识别准确率和识别效率，进而提 高植物的识别准确率和识别效率。
步骤S102，调用所述植物的种类对应的植物品种识别模型，识别所述植物的具体品种以及开放状态。
由于观赏植物例如樱花、梅花等存在多种品种，在植物观赏时，用户通常希望了解所观赏植物的具体品种以及花期，从而可选择性的前往喜欢的品种所在的观赏地点进行观赏，因此在进行花期播报时需要向用户展示植物的具体品种。由于上述的物种识别模型仅能识别出植物的种类，无法识别出植物的具体品种，因此，在本发明的一个实施例中，针对不同的植物种类，预先训练了相对应的植物品种识别模型，在步骤S101识别出植物种类之后，再调用该植物种类对应的植物品种识别模型再次进行识别，以识别出所述图像中植物的具体品种，同时还可以识别出植物的开放状态。例如，步骤S101识别出所述植物为樱花，则调用预先训练好的樱花品种识别模型对所述图像进行再次识别，以识别出樱花的具体品种以及开放状态。植物的开放状态可根据花苞状态和整株开花的程度来划分，例如可分为未开、初开、盛开和完结四种状态，举例而言，若植物还没有花苞或仅有少量花苞，则开放状态为未开，当植物有较多花苞或有少数花苞开放，则开放状态为初开，若植物整株开花的比例超过一定阈值(例如80％)，则开放状态为盛开，若植物花朵凋谢的比例超过一定阈值(例如70％)，则开放状态为完结。
各个植物种类对应的植物品种识别模型的训练方式基本相同，在此以樱花为例，对樱花品种识别模型的训练方法进行简单介绍。樱花的品种繁多，例如寒绯樱、河津樱、雨情枝垂、染井吉野樱、大岛樱、雏菊樱等，不同品种的樱花具有其特有的形态特点。在训练樱花品种识别模型时，为每个樱花品种获取一定数量的标注有对应信息的图像样本，为每个樱花准备的图像样本的数量可以相等也可以不等，所标注的信息应包括图像样本中的樱花品种以及开放状态。为每个樱花品种获取的图像样本可以尽可能包括该品种的樱花的不同拍摄角度、不同光照条件、不同天气(例如同一植物在艳阳天和雨天的形态可能不同)、不同月份或季节(例如同一植物在不同月份或季节的形态可能不同)、不同时间(例如同一植物在每天的早晨和夜晚的形态可能不同)、不同生长环境(例如同一植物在室内和室外生长的形态可能不同)、不 同地理位置(例如同一植物在不同的地理位置生长的形态可能不同)的图像。在这些情况下，为每个图像样本标注的对应信息还可以包括该图像样本的拍摄角度、光照、天气、季节、时间、生长环境或地理位置等信息。
所述樱花品种识别模型也是基于神经网络的模型，例如是深度卷积神经网络(CNN)或深度残差网络(Resnet)。在获取到樱花图像样本后，所述樱花品种识别模型可按照如上所述的物种识别模型的训练过程进行训练，在此不做赘述。
上述的植物品种识别模型可同时识别出植物的具体品种以及开放状态。在本发明的其它实施例中，也可以同时设置单独的植物品种识别模型和开放状态识别模型，其中，单独的植物品种识别模型仅用于识别出植物的具体品种，开放状态识别模型则用于识别出植物的开放状态，可以理解的是，通过设置单独的植物品种识别模型和开放状态识别模型可以提高品种识别以及开放状态识别的准确性。在利用所述开放状态识别模型对图像中植物的开放状态进行识别时，还可以结合图像的拍摄位置以及拍摄时间信息对识别结果进行二次确认，例如，当所述开放状态识别模型识别出某一图像中的植物的开放状态为盛开时，同时结合该图像的拍摄位置和拍摄时间若能够预测此拍摄位置处的相同品种植物在此拍摄时间内的开放状态应当为盛开，则可确认所述开放状态识别模型的识别结果是准确的，否则可认为所述开放状态识别模型的识别结果可能有误，如此可重新对该图像中植物的开放状态进行再次识别或直接进行人工识别，也可以进一步对所述开放状态识别模型进行再次训练直至所述开放状态识别模型的识别准确率满足要求。可见，通过拍摄位置和拍摄时间的二次确认可以保证所述开放状态识别模型的识别结果的准确性。
步骤S103，获取所述图像的拍摄时间和拍摄位置，根据所述拍摄时间、所述开放状态确定所述植物的花期。
所述图像的拍摄时间和拍摄位置属于图像的属性信息，因此，可从所述图像的属性信息中获取所述图像的拍摄时间和拍摄位置。在识别出植物的开放状态后，可根据所述拍摄时间和开放状态确定所述植物的花期。植物的花期可以包括初开的花期和盛开的花期。
以樱花为例，所述图像的拍摄时间为2019年2月20日、拍摄位置为地点A，若开放状态为未开，可根据历史记录的花期信息，预测地点A处的樱花初开和盛开的花期，例如在上一花期，该地点A处的樱花的初开的日期为2018年3月1日、盛开的日期为2018年3月15日，则预测该地点A处的樱花在本花期的初开日期是2019年3月1日、盛开的日期为2019年3月15日；若开放状态为初开，则可将所述拍摄时间作为该地点A处的樱花的初开花期，然后根据初开花期预测该地点A处的樱花的盛开花期；若开放状态为盛开，则将所述拍摄时间作为该地点A处的樱花的盛开花期。
步骤S104，在花期播报地图上标记所述拍摄位置为所述植物的观赏地点，并对应显示所述植物的具体品种以及花期。
本实施例中，所述移动设备102的显示器向用户提供用户界面，在所述用户界面上可显示花期播报地图，所述花期播报地图可用于展示某一特定植物种类例如樱花的各个观赏地点，也可以同时展示多个植物种类例如樱花和桃花等多种植物的各个观赏地点，本实施例对此不做限定。
以单独展示樱花的花期播报地图为例，在步骤S103中已获取了所述图像的拍摄位置，也获取了所述图像中的樱花的花期和具体品种，则可以在所述花期播报地图中标记所述拍摄位置为樱花的观赏地点，同时对应显示樱花的具体品种和花期。例如所述拍摄位置为公园A，则在所述花期播报地图中将公园A以一特定标志标记出来，同时在所述特定标志处显示该樱花的具体品种和花期。如此，用户可从所述花期播报地图中看到所述拍摄位置对应的地点具有可供观赏的樱花，以及了解到此处的樱花的具体品种和花期。
由于本发明实施例提供的植物花期播报方法可通过任意用户上传植物图像，并根据用户上传的植物图像确定出植物的具体品种、花期以及拍摄地点，从而在花期播报地图中更新展示植物的观赏地点以及具体品种和花期，如此可使所述花期播报地图能够及时显示当前时间段内可供观赏的植物的相关信息，从而为用户观赏植物提供准确的参考信息，提高用户的观赏体验。
可选的，为进一步提高用户体验，本实施例的植物花期播报方法还可以包括：当所述植物的开放状态为初开或盛开时，向所述拍摄位置所在区域内的用户推送所述植物的观赏推荐信息。即，若识别出所述植物的开放状态为 初开或盛开，表示当前所述植物正处于最佳观赏时间段，此时，所述远程服务器103可向所述拍摄位置所在区域内的其它用户推送所述植物的观赏推荐信息，提示所述拍摄位置所在区域内的其它用户可前往所述拍摄位置处观赏所述植物，例如所述拍摄位置为公园A，则向当前正位于公园A内或者在公园A附近的用户推送所述植物的观赏推荐信息。所述植物的观赏推荐信息包括以下信息中的一个或多个：所述植物的具体品种、花期、观赏地点的地理位置等，当然也可以包含其它信息，本实施例对此不做限定。推送所述观赏推荐信息的方式可通过短信、应用内消息的形式进行推送，也可以是其它形式，本实施例对此亦不做限定。
可选的，当所述植物的开放状态为盛开时，还可以将所述花期播报地图分享给特定区域内的其它用户，其中所述特定区域为与所述图片的拍摄位置在预设距离内的区域。即，所述花期播报地图可以分享给特定区域内(例如距离所述拍摄位置5公里内)的其它用户或好友，用户上传了拍摄的植物图片，根据该植物图片在花期播报地图上标记出所述植物的观赏地点后，所述用户可以选择将该花期播报地图主动分享给其它用户或好友，以便邀请其它用户或好友来此观赏地点观赏所述植物。具体的，可以将所述花期播报地图在应用内分享给其它用户，也可以通过其它社交平台分享给其它用户。在分享时，可以将所述花期播报地图经过处理后以图片的形式进行分享，也可以将所述花期播报地图处理成H5页面的形式进行分享，本实施例对此不做限定。
可选的，用户可点击查看当前时间段内推荐的植物观赏地点，通常来说，植物的观赏地点一般在公园、植物园等景点内，当用户选择某一观赏地点时，所述移动设备102可以响应于用户选择该观赏地点的操作，在所述花期播报地图中显示选定的观赏地点处的所述植物的具体品种和/或景点信息，例如景点介绍、交通信息、人流量、门票以及其它收费信息，以便给用户提供更多关于植物观赏地点的信息。
在一些实施例中，用户希望查看某一区域或全部区域内所述植物的开花情况，所述移动设备102可响应于用户操作，将选定地理区域对应的所述花期播报地图展示给用户，或者，将所有地理区域对应的所述花期播报地图展 示给用户。例如，用户选择查看北京地区所述植物的开花情况，则所述移动设备102将北京地区的花期播报地图展示给用户，当用户选择查看在中国全部范围内所述植物的开花情况，则将包含中国所有地区的花期播报地图展示给用户。
另外，本实施例提供的植物花期播报方法，为了标记出所述植物的所有可供观赏的地点，给用户提供全面的观赏信息，还可以将所述花期播报地图与历史记录的上一花期播报地图进行比对，若上一花期播报地图中存在所述花期播报地图未标注的观赏地点，则在所述花期播报地图中标注出该观赏地点，再根据该观赏地点处所述植物的上一花期的时间预估所述植物的当前花期，并在所述花期播报地图中对应显示该观赏地点处所述植物的具体品种以及当前花期。举例而言，当前的所述花期播报地图已标记出三处所述植物的观赏地点，分别为地点A、B、C，而在上一花期的花期播报地图中标注有4处所述植物的观赏地点，分别为地点A、B、C、D，通过将这两个花期播报地图进行比对，发现地点D未被标注出来，而地点D在上一花期被标注出来，表示地点D存在所述植物(因为当前花期还没有用户在地点D拍摄植物图片并上传，导致当前花期无法在所述花期播报地图中标注出地点D)，因此，在当前花期的花期播报地图中将地点D也标注为观赏地点，同时获取上一花期地点D处所述植物的具体品种以及花期的具体时间，根据上一花期的时间来预估当前花期的时间，从而在当前花期的播报地图中对应显示地点D处植物的具体品种以及当前花期。如此，可利用历史记录的花期信息对当前花期的所述花期播报地图中的观赏地点进行更新，以弥补通过用户上传植物图像来标记观赏地点的不足。
可选的，还可以在所述花期播报地图上的观赏地点处显示目标图片的缩小图标，其中所述目标图片为在所述观赏地点处拍摄的所述植物的开放状态最好的图片。具体的，可以将最近拍摄的(例如24小时内)所述植物的开放状态最好的图片作为对应观赏地点处的缩小图标。开放状态最好的图片，例如可以选择整株开花的比例超过80％的图片，也可以选择花朵占据图片面积最多的图片，还可以是通过上述的开放状态识别模型识别出的开放状态最好的图片。
在其它实施例中，也可以将接收到的在同一观赏地点处拍摄的所述植物的图像组成图片集，所示图片集中的图片可以为同一用户拍摄的、也可以包含其它用户拍摄的，其中的各个图片可以按照拍摄时间和开放状态进行综合排序。当用户选择查看所述图片集(例如点击上述的缩小图标)时，将所述图片集展示给用户，以便用户查看所述观赏地点处所述植物的开花情况。
基于同一发明构思，本发明还提供了一种植物花期播报系统。如图3所示，植物花期播报系统200可以包括处理器210和存储器220，存储器220上存储有指令，当指令被处理器210执行时，可以实现如上文所描述的植物花期播报方法中的步骤。
其中，处理器210可以根据存储在存储器220中的指令执行各种动作和处理。具体地，处理器210可以是一种集成电路芯片，具有信号的处理能力。上述处理器可以是通用处理器、数字信号处理器(DSP)、专用集成电路(ASIC)、现成可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。可以实现或者执行本发明实施例中公开的各种方法、步骤及逻辑框图。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等，可以是X86架构或者是ARM架构等。
存储器220存储有可执行指令，该指令在被处理器210执行上文所述的植物花期播报方法。存储器220可以是易失性存储器或非易失性存储器，或可包括易失性和非易失性存储器两者。非易失性存储器可以是只读存储器(ROM)、可编程只读存储器(PROM)、可擦除可编程只读存储器(EPROM)、电可擦除可编程只读存储器(EEPROM)或闪存。易失性存储器可以是随机存取存储器(RAM)，其用作外部高速缓存。通过示例性但不是限制性说明，许多形式的RAM可用，例如静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、同步动态随机存取存储器(SDRAM)、双倍数据速率同步动态随机存取存储器(DDRSDRAM)、增强型同步动态随机存取存储器(ESDRAM)、同步连接动态随机存取存储器(SLDRAM)和直接内存总线随机存取存储器(DR RAM)。应注意，本文描述的方法的存储器旨在包括但不限于这些和任意其它适合类型的存储器。
基于同一发明构思，本发明还提供了一种计算机可读存储介质，计算机 可读存储介质上存储有指令，当指令被执行时，可以实现上文所描述的植物花期播报方法中的步骤。
类似地，本发明实施例中的计算机可读存储介质可以是易失性存储器或非易失性存储器，或可包括易失性和非易失性存储器两者。应注意，本文描述的计算机可读存储介质旨在包括但不限于这些和任意其它适合类型的存储器。
需要说明的是，附图中的流程图和框图，图示了按照本发明各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，所述模块、程序段、或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
一般而言，本发明的各种示例实施例可以在硬件或专用电路、软件、固件、逻辑，或其任何组合中实施。某些方面可以在硬件中实施，而其他方面可以在可以由控制器、微处理器或其他计算设备执行的固件或软件中实施。当本发明的实施例的各方面被图示或描述为框图、流程图或使用某些其他图形表示时，将理解此处描述的方框、装置、系统、技术或方法可以作为非限制性的示例在硬件、软件、固件、专用电路或逻辑、通用硬件或控制器或其他计算设备，或其某些组合中实施。
需要说明的是，本说明书中的各个实施例均采用相关的方式描述，各个实施例之间相同相似的部分互相参见即可，每个实施例重点说明的都是与其他实施例的不同之处。尤其，对于系统、计算机可读存储介质而言，由于其基本相似于方法实施例，所以描述的比较简单，相关之处参见方法实施例的部分说明即可。
在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者 操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
上述描述仅是对本发明较佳实施例的描述，并非对本发明范围的任何限定，本发明领域的普通技术人员根据上述揭示内容做的任何变更、修饰，均属于权利要求书的保护范围。

Claims (15)

  1. 一种植物花期播报方法，其特征在于，包括：
    接收一植物的图像，识别所述图像中的植物以得到所述植物的种类；
    调用所述植物的种类对应的植物品种识别模型，识别所述植物的具体品种以及开放状态；
    获取所述图片的拍摄时间和拍摄位置，根据所述图片的拍摄时间和所述植物的开放状态确定所述植物的花期；
    在一花期播报地图上标记所述图片的拍摄位置为所述植物的观赏地点，并对应显示所述植物的具体品种以及花期。
  2. 如权利要求1所述的植物花期播报方法，其特征在于，识别所述图像中的植物以得到所述植物的种类，包括：
    利用预先训练建立的物种识别模型识别所述图像中的植物以得到所述植物的种类，所述物种识别模型为神经网络模型。
  3. 如权利要求2所述的植物花期播报方法，其特征在于，所述物种识别模型的训练步骤包括：
    获取训练样本集，所述训练样本集中的每一样本标注有植物的种类；
    获取测试样本集，所述测试样本集中的每一样本也标注有植物的种类，其中，所述测试样本集不同于所述训练样本集；
    基于所述训练样本集对所述物种识别模型进行训练；
    基于所述测试样本集对所述物种识别模型进行测试；
    在所述测试结果指示所述物种识别模型的识别准确率小于预设准确率时，增加所述训练样本集中的样本数量进行再次训练；以及
    在所述测试结果指示所述物种识别模型的识别准确率大于或等于所述预设准确率时，完成训练。
  4. 如权利要求1所述的植物花期播报方法，其特征在于，所述植物品种识别模型为神经网络模型。
  5. 如权利要求1所述的植物花期播报方法，其特征在于，所述植物的开放状态包括：未开、初开、盛开和完结。
  6. 如权利要求5所述的植物花期播报方法，其特征在于，还包括：
    当所述植物的开放状态为初开或盛开时，向所述拍摄位置所在区域内的用户推送所述植物的观赏推荐信息。
  7. 如权利要求6所述的植物花期播报方法，其特征在于，所述植物的观赏推荐信息包括以下信息中的一个或多个：所述植物的具体品种、花期、观赏地点的地理位置。
  8. 如权利要求5所述的植物花期播报方法，其特征在于，还包括：
    当所述植物的开放状态为盛开时，将所述花期播报地图分享给特定区域内的其它用户，其中所述特定区域为与所述图片的拍摄位置在预设距离内的区域。
  9. 如权利要求1所述的植物花期播报方法，其特征在于，还包括：
    响应于用户操作，将选定地理区域对应的所述花期播报地图展示给用户，或者，将所有地理区域对应的所述花期播报地图展示给用户。
  10. 如权利要求1所述的植物花期播报方法，其特征在于，还包括：
    响应于用户选择一观赏地点的操作，在所述花期播报地图中显示选定的观赏地点处的所述植物的具体品种和/或景点信息。
  11. 如权利要求1所述的植物花期播报方法，其特征在于，还包括：
    将所述花期播报地图与历史记录的上一花期播报地图进行比对，若上一花期播报地图中存在所述花期播报地图未标注的观赏地点，则在所述花期播报地图中标注出该观赏地点，再根据该观赏地点处所述植物的上一花期的时间预估所述植物的当前花期，并在所述花期播报地图中对应显示该观赏地点处所述植物的具体品种以及当前花期。
  12. 如权利要求1所述的植物花期播报方法，其特征在于，还包括：
    在所述花期播报地图上的观赏地点处显示目标图片的缩小图标，其中所述目标图片为在所述观赏地点处拍摄的所述植物的开放状态最好的图片。
  13. 如权利要求1所述的植物花期播报方法，其特征在于，还包括：
    将接收到的在所述观赏地点处拍摄的所述植物的图像组成图片集，其中各个图片按照拍摄时间和开放状态进行排序；
    响应于用户操作，将所述图片集展示给用户。
  14. 一种植物花期播报系统，其特征在于，所述系统包括处理器和存储器，所述存储器上存储有指令，当所述指令被所述处理器执行时，实现如权利要求1至13中任一项所述的方法的步骤。
  15. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有指令，当所述指令被执行时，实现如权利要求1至13中任一项所述的方法的步骤。
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