CN116681544B - Crop environment information processing method, electronic device, and computer-readable medium - Google Patents
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Abstract
Embodiments of the present disclosure disclose a crop environment information processing method, an electronic device, and a computer readable medium. One embodiment of the method comprises the following steps: acquiring a crop image of a target crop; determining crop category and crop maturity information of the target crop according to the crop image; acquiring a crop environment information set; inputting the crop maturation information, the crop category and the crop environment information set into a pre-trained environment information analysis model to obtain an environment information analysis result; and controlling the associated environment regulating equipment to regulate and control the environment of the target crop according to the environmental information analysis result. This embodiment increases crop yield.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technology, and in particular, to a crop environment information processing method, an electronic device, and a computer readable medium.
Background
With the continuous development of technology, how to more intelligently control the growth environment of crops becomes an important research topic. Currently, in controlling the crop growth environment, the following methods are generally adopted: and setting fixed environmental parameters to keep the crop growth environment constant.
However, when the crop growing environment is controlled in the above manner, there are often the following technical problems:
first, the growth environment required for the same crop in different growth stages may be different, and setting fixed environmental parameters may result in a crop growth stage that does not adapt to the set growth environment, resulting in lower crop yield.
Second, in determining crop categories, plant leaves are typically identified using the artificial immune system. The artificial immune system has lower local selection accuracy, and can be identified as a wrong crop category when identifying plant leaves, so that wrong growth environment parameters are used, and crop yield is reduced or even withered.
Third, when controlling insect pests, a fixed spraying amount of the pesticide is set, and the influence of other factors (such as weather factors) on the insect pests is not considered, so that too much or insufficient pesticide spraying is caused, and the environment is polluted excessively or the insect pest control is not thorough.
Fourth, when the mature crops are dried, a fixed drying time length is set, the influence of the weight and weather of the crops on the drying is not considered, the drying time length is insufficient or longer, and the crops are wasted due to rotten and deteriorated finish or the quality of the crops is lower due to lower moisture.
Disclosure of Invention
The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose crop environment information processing methods, electronic devices, and computer-readable media to address one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide a crop environment information processing method, the method including: acquiring a crop image of a target crop; determining crop category and crop maturity information of the target crop according to the crop image; acquiring a crop environment information set, wherein crop growth information in the crop environment information set is one of the following: temperature information, humidity information, illumination intensity information and carbon dioxide concentration information; inputting the crop maturity information, the crop category and the crop environment information set into a pre-trained environment information analysis model to obtain an environment information analysis result; and controlling the related environment regulating equipment to regulate the environment of the target crop according to the environmental information analysis result.
In a second aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors causes the one or more processors to implement the method described in any of the implementations of the first aspect above.
In a third aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.
The above embodiments of the present disclosure have the following advantages: crop yield may be improved by the crop environment information processing method of some embodiments of the present disclosure. In particular, the reason for the lower crop yield is that: the growth environment required for the same crop in different growth stages may be different, and setting fixed environmental parameters may result in a crop growth stage that does not adapt to the set growth environment, resulting in lower crop yields. Based on this, the crop environment information processing method of some embodiments of the present disclosure first acquires a crop image of a target crop. Thus, the type and maturity of the crop can be determined from the crop image. Secondly, determining crop type and crop maturity information of the target crops according to the crop images; a crop environmental information set is acquired. Thus, the information of the growth environment of the crop can be obtained. And then inputting the crop maturity information, the crop category and the crop environment information set into a pre-trained environment information analysis model to obtain crop maturity information and an environment information analysis result. Thus, it can be determined whether the current growing environment parameters meet the environment required for the above-mentioned crop category and maturity by a pre-trained environmental information analysis model. And finally, controlling the associated environment regulating equipment to regulate the environment of the target crop according to the environmental information analysis result. Therefore, the unsatisfied environmental parameters can be regulated and controlled through the environmental regulation and control equipment so as to meet the growth requirements of target crops. Therefore, the yield of crops is improved through the regulation and control of the environment.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
FIG. 1 is a flow chart of some embodiments of a crop environmental information processing method according to the present disclosure;
fig. 2 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Description of the embodiments
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates a flow 100 of some embodiments of a crop environment information processing method according to the present disclosure. The crop environment information processing method comprises the following steps: step 101, acquiring a crop image of a target crop.
In some embodiments, the execution subject (e.g., server) of the crop environment information processing method may acquire the crop image of the target crop through the associated monitoring apparatus by way of a wired connection or a wireless connection. The monitoring device can be a device which is installed in advance and used for shooting and video-recording the planted crops. For example, the monitoring device may be a camera. The target crop may be a crop planted in a region that can be photographed by the monitoring apparatus. The crop image may be an image captured by the monitoring device, or may be an image captured from a video recorded by the monitoring device.
Step 102, determining crop category and crop maturity information of the target crop according to the crop image.
In some embodiments, the executing entity may determine the crop category and the crop maturity information of the target crop from the crop image.
In practice, the execution subject may determine the crop category and crop maturity information of the target crop by:
first, the crop image is cropped to generate a cropped crop image. Wherein the cropped crop image may display leaves of the target crop. The cropping process may be a cropping process of crop leaves displayed in the crop image.
And secondly, inputting the cut crop images into a pre-trained crop identification model to obtain crop types. The crop identification model may be a neural network model with a crop image as input and a crop type as output. For example, the crop identification model may be a convolutional neural network model.
Here, the crop identification model may be a predefined model. The predefined model can be divided into three layers: the first layer, the input layer, is used for carrying on the preconditioning to the crop picture after cutting of input. The preprocessing may be binarizing the crop image after the cropping after the gray value normalization. A second layer, a process layer, comprising a first sub-model and a second sub-model. The first sub-model identifies the cropped crop image through global and local area selection, inputs the cropped crop image, and outputs crop category, crop maturity information and a first identification degree. The second sub-model is used for classifying the cut crop images, determining the types of crops in the cut crop images, inputting the cut crop images, and outputting the crop types, the crop maturity information and the second recognition degree. And the third layer is used for respectively receiving the output of the first sub-model and the second sub-model, and selecting the crop category with the highest recognition degree as the output of the whole predefined model.
The above-mentioned matters in the first step-the second step are taken as an invention point of the present disclosure, and the second problem mentioned in the background art is solved, and in determining the crop category, the artificial immune system is generally used for identifying the plant leaves. The artificial immune system has lower local selection accuracy, and can be identified as the wrong crop category when identifying plant leaves, so that wrong growth environment parameters are used, and crop yield is reduced or even withered. Factors that cause crop yield reduction and even wilting are often as follows: in determining crop categories, plant leaves are typically identified using the artificial immune system. The artificial immune system has lower local selection accuracy, and can be identified as a wrong crop category when identifying plant leaves, so that wrong growth environment parameters are used, and crop yield is reduced or even withered. If the above factors are solved, the effect of avoiding crop yield reduction and even withering can be achieved. To achieve this effect, first, the crop image is subjected to cropping processing to generate a cropped crop image. Thus, the crop category can be determined by identifying the crop image that is cropped. Secondly, inputting the cut crop images into a pre-trained crop identification model to obtain crop types. Therefore, the crop type can be determined by using the crop identification result with higher identification degree through the predefined model, and the probability of identifying the plant leaves by mistake can be avoided as far as possible, so that the crop yield is prevented from being reduced and even withered.
Optionally, after step 102, a set of historical growth data sets corresponding to the crop categories described above is obtained.
In some embodiments, the executing entity may obtain a historical growth dataset corresponding to the crop category. Wherein, the historical growth data group in the historical growth data group corresponds to a crop growth stage. The historical growth data set in the set of historical growth data sets may be a growth data set for a certain growth stage of the crop history.
Optionally, the pest growth stage is determined from the historical growth dataset.
In some embodiments, the executing entity may determine the pest growth stage based on the historical growth dataset.
And step 103, acquiring a crop environment information set.
In some embodiments, the executing entity may obtain the crop environment information set. Wherein, the crop growth information in the crop environment information set is one of the following: temperature information, humidity information, illumination intensity information, and carbon dioxide concentration information. In practice, the executing body can acquire the corresponding crop environment information through the associated sensing equipment. The sensing device may be a device for monitoring environmental data installed in an environment in which the target crop is located. For example, the sensing device may include: an illumination sensor, a temperature humidity sensor, etc. As an example, the above-mentioned illumination sensor may be used to detect illumination intensity information in an environment, and the above-mentioned temperature and humidity sensor is used to detect temperature information and humidity information in an environment.
And 104, inputting the crop maturity information, the crop category and the crop environment information set into a pre-trained environment information analysis model to obtain an environment information analysis result.
In some embodiments, the execution subject may input the crop maturity information, the crop category, and the crop environmental information set into a pre-trained environmental information analysis model to obtain an environmental information analysis result.
Alternatively, the environmental information analysis model may be trained by: first, a sample set is obtained.
In some embodiments, the execution body may obtain a sample set. The samples in the sample set comprise sample crop maturity information, sample crop category and sample crop environment information set, and sample environment information analysis results corresponding to the sample crop maturity information, the sample crop category and the sample crop environment information set.
And a second step of selecting samples from the sample set.
In some embodiments, the execution body may select a sample from the sample set. Here, the execution subject may randomly select a sample from the sample set.
And thirdly, inputting the sample into an initial network model to obtain an environmental information analysis result corresponding to the sample.
In some embodiments, the execution body may input the sample into an initial network model to obtain an environmental information analysis result corresponding to the sample. The initial neural network may be a classification model capable of obtaining an environmental information analysis result according to crop maturity information, crop category and crop environmental information set. The initial neural network may be a generative model.
And a fourth step of determining a loss value between the environmental information analysis result and a sample environmental information analysis result included in the sample.
In some embodiments, the execution body may determine a loss value between the environmental information analysis result and a sample environmental information analysis result included in the sample. In practice, a loss value between the environmental information analysis result and the sample environmental information analysis result included in the sample may be determined based on a preset loss function. For example, the predetermined loss function may be a cross entropy loss function.
And fifthly, adjusting network parameters of the initial network model in response to the loss value being greater than or equal to a preset threshold.
In some embodiments, the executing entity may adjust the network parameters of the initial network model in response to the loss value being greater than or equal to a preset threshold. Here, the setting of the preset threshold is not limited. For example, the loss value and the preset threshold may be differenced to obtain a loss difference. On this basis, the error value is transmitted forward from the last layer of the model by using back propagation, random gradient descent and the like to adjust the parameters of each layer. Of course, a network freezing (dropout) method may be used as needed, and network parameters of some layers therein may be kept unchanged and not adjusted, which is not limited in any way.
Optionally, in response to the loss value being less than the preset threshold, determining the initial network model as an environmental information analysis model.
In some embodiments, the executing entity may determine the initial network model as the environmental information analysis model in response to the loss value being less than the preset threshold.
In some alternative implementations of some embodiments, the environmental information analysis model may be a custom model. The custom model can be divided into three layers, namely a first layer and an input layer, and is used for preprocessing the input crop maturity information and crop categories and converting the crop maturity information and the crop categories into character strings. In practice, the input layer includes a mapping relation table of the crop maturity information and the character string, and a mapping relation table of the crop category and the character string, so that the crop maturity information and the crop category can be mapped into corresponding character strings respectively, and then splicing processing is performed to generate spliced character strings. The second layer, the processing layer, can include the corresponding relation table of the character string and each crop environmental information. The correspondence table may be a correspondence table established by a person skilled in the art based on correspondence between a large number of crop maturation information and crop categories and respective crop environment information. In this way, the character strings of the crop maturation information and the crop category mapping and splicing are sequentially compared with the character strings in the corresponding relation table, and if one character string in the corresponding relation table is identical to the character string, the crop environment information corresponding to the character string in the corresponding relation table is used as the crop maturation information and the crop environment information set required by the crops of the crop category. And the third layer and the output layer are used for comparing the crop environment information in the crop environment information set input by the input layer with the growing crop environment information corresponding to the crop environment information in the growing crop environment information set, and determining each obtained comparison result as an environment information analysis result to be used as the output of the whole self-defined model.
And 105, controlling the associated environment regulation and control equipment to regulate and control the environment of the target crop according to the environmental information analysis result.
In some embodiments, the execution body may control the associated environment control device to control the environment of the target crop according to the analysis result of the environment information. In practice, the executing body can respond to the abnormality of any crop environment information of the target crop, and control the associated environment equipment corresponding to the crop environment information to regulate and control. For example, in response to the carbon dioxide concentration being greater than the carbon dioxide concentration characterized by the growing crop environmental information, the executing entity may control the associated ventilation device to regulate such that the carbon dioxide concentration decreases to the carbon dioxide concentration characterized by the growing crop environmental information.
Optionally, after step 105, the growth phase of the target crop is determined.
In some embodiments, the executing entity may determine a growth stage of the target crop. In practice, the above-described execution subject may determine the growth stage of the target crop in various ways. For example, the executive may determine the stage of growth by the maturity of the target crop.
Optionally, after step 105, pest type information is determined in response to the growth phase of the target crop being a pest growth phase.
In some embodiments, the executing entity may determine the pest type information in response to the growth stage of the target crop being a pest growth stage. In practice, the above-described executing body may recognize the pest image to determine pest type information.
Optionally, after step 105, a historical medication data set is obtained based on the determined pest type information.
In some embodiments, the executing entity may obtain the historical medication data set corresponding to the pest type information from the database through a wired connection or a wireless connection. Wherein, the historical drug data in the historical drug data set can be data of a drug used for pest control in a historical way.
Optionally, the medication spray is determined from the historical medication data set described above.
In some embodiments, the subject may determine the medication spray amount based on the historical medication data set.
In practice, the above-described executing body can determine the medicine spray amount by:
first, determining a discrete value of medication spray based on the historical medication data set. In practice, the above-described executing body can determine the medicine spray discrete value by the following formula:
。
wherein Indicating the amount of drug sprayed. />Representing the number of historical medication data included in the historical medication data set.Representing the +.>Historical medication data. />Representing the mean of the individual historical medication data included in the historical medication data set.
Thus, each historical medication data is summed with the average of the historical medication data, and the result of the summation is summed with the degree of freedomAs a dispersion of the historical medication data set, so that the amount of medication sprayed to be used can be defined by the dispersion. Whereas the number of historical drug data affecting fluctuation is +.>Therefore use the degree of freedom +.>A better estimate can be obtained.
And step two, spraying discrete values according to the medicines to generate a discrete value offset range. In practice, the drug spray may be discrete plus or minus a pre-determined valueSetting an offset value and generating a discrete value offset range. The preset offset value may be a preset offset value of a discrete value of spraying the medicine. Here, the setting of the preset offset value is not limited, and may be a preset offset value obtained by experiment. And thirdly, determining the medicine spraying range according to the discrete value deviation range. Here, the maximum value and the minimum value of the discrete value offset range may be substituted into the following formulas, respectively, to determine the medicine spraying range; 。
wherein ,representing the maximum or minimum value of the discrete value offset range. />Representing the mean of the individual historical medication data included in the historical medication data set. />Representing the number of historical medication data included in the historical medication data set. />Indicating the maximum or minimum value of the spray range of the drug.
Therefore, the maximum value and the minimum value of the discrete value offset range are determined, so that the medicine spraying range is determined, the medicine spraying quantity can be selected from the medicine spraying range, and further, the situation that excessive medicines pollute the environment or insect pest control is incomplete can be avoided.
And fourthly, determining the spraying quantity of the medicine according to the insect pest degree. In practice, the executing subject can determine the insect pest degree by recognizing the crop image of the target crop. As an example, the medicament spray range may be divided into a plurality of sub-ranges. For example, the minimum value of the medicine spray range to the median value of the medicine spray range may be divided into ranges of the medicine spray amount where the degree of insect pest is a slight insect pest. And when the number of insect pests is set to be more than 0 and less than or equal to 3, determining the insect pest degree as slight insect pest, and selecting a random number from the minimum value of the pesticide spraying range to the median value of the pesticide spraying range as the pesticide spraying amount.
Optionally, controlling the associated spraying device to spray the target crop with the medicament according to the medicament spraying amount.
In some embodiments, the executing body may control the associated spraying device to spray the medicine to the target crop according to the medicine spraying amount. In practice, the executing body may control the associated spraying device to spray the determined medicine spraying amount of the medicine to the target crop at a preset spraying time. Wherein the associated spraying device may be an automatic spraying device.
The above-mentioned related matters are selected as an invention point of the present disclosure, and solve the third technical problem mentioned in the background art, namely, when pest control is performed, a fixed spraying amount of the pesticide is set, and the influence of other factors (such as weather factors) on the pest is not considered, so that too much or insufficient spraying of the pesticide is caused, and the environment is polluted excessively or pest control is not thorough. Factors causing excessive pollution to the environment or incomplete pest control of the medicines are often as follows: when the insect pest is treated, a fixed pesticide spraying amount is set, and the influence of other factors (such as weather factors) on the insect pest is not considered, so that too much or insufficient pesticide spraying is caused, and the environment is polluted excessively or the insect pest is not treated thoroughly. If the above factors are solved, the effects of avoiding excessive pollution of the medicaments to the environment or incomplete pest control can be achieved. To achieve this effect, first, the growth stage of the target crop is determined. Thus, it can be determined whether the target crop is in the pest stage. Next, pest type information is determined in response to the growth phase of the target crop being a pest growth phase. Therefore, the type of insect attack suffered by the target crops can be determined, and the corresponding medicaments and the medicament dosage can be selected for treatment. Then, a historical medication data set is obtained based on the determined pest type information. Thus, the medication spray amount can be determined from the historical medication data set. Finally, determining the medicine spraying quantity according to the historical medicine data set; and controlling the associated spraying equipment to spray the medicine to the target crops according to the medicine spraying quantity. Therefore, the medicine spraying quantity is determined according to the historical medicine spraying data, so that excessive medicine pollution to the environment or incomplete pest control can be avoided.
Optionally, after step 104, the associated picking device is controlled to perform a picking process on the target crop according to the crop maturity information of the target crop.
In some embodiments, the executing entity may control the associated picking device to perform picking treatment on the target crop according to crop maturity information of the target crop. In practice, first, the executing entity may determine whether the crop maturity information of the target crop satisfies a preset maturity condition. The preset ripening condition may be a preset condition for determining whether the target crop ripens. For example, the predetermined maturity condition may be that the maturity of the target crop is equal to or greater than the predetermined maturity. As an example, the preset maturity may be 98%. Second, the executing body may control the associated picking device to perform picking processing on the target crop and transport the target crop to a preset storage area in response to whether the crop maturity information of the target crop satisfies a preset maturity condition.
Optionally, weather information is acquired.
In some embodiments, the executing entity may obtain weather information. In practice, the executing body may acquire weather information in various ways. For example, the executing body may acquire weather information through the internet.
Optionally, determining the weight of the crop to be aired in response to the weather information meeting a preset weather condition.
In some embodiments, the executing body may determine the weight of the crop to be dried in response to the weather information satisfying a preset weather condition. The preset weather condition may be a preset weather condition. For example, the predetermined weather condition may be a sunny day and the temperature is between 25 ℃ and 30 ℃. The weight of the crops to be dried can be the weight of the crops to be dried.
Optionally, according to the weather information and the weight of the crops to be aired, the airing duration is determined.
In some embodiments, the executing body may determine the drying time according to the weather information and the weight of the crops to be dried. In practice, the execution subject may determine the airing duration through an airing duration determination model. The airing duration determining model may be a neural network model trained in advance and used for determining the airing duration. The sunning time length determination model takes the weather information and the weight of the crops to be sunned as input and takes the sunning time length as output.
In some optional implementations of some embodiments, the above-mentioned drying time period determination model may include a table of correspondence between temperature and weight of crops to be dried and drying time period. The corresponding relation table can be a corresponding relation table established by a person skilled in the art based on the corresponding relation between a large number of temperatures and the weight of crops to be aired and the airing time. In this way, the temperature and the weight of the crops to be dried are sequentially compared with a plurality of temperatures and the weights of the crops to be dried in the corresponding relation table, and if a certain temperature and the weight of the crops to be dried in the corresponding relation table are the same as or similar to the temperature and the weight of the crops to be dried, the drying time length corresponding to the temperature and the weight of the crops to be dried in the corresponding relation table is taken as the drying time length required by the crops with the temperature and the weight of the crops to be dried. Therefore, different airing time periods are configured according to different temperatures and weights of crops to be aired.
Optionally, according to the airing duration, airing the crops to be aired.
In some embodiments, the executing body may perform the drying process on the crop to be dried according to the drying time period. In practice, the execution body can control the associated transportation equipment to transport the crops to be dried to a preset drying area, and according to the drying time, the crops to be dried are dried.
The above-mentioned optional related matters are taken as an invention point of the present disclosure, and the fourth technical problem mentioned in the background art is solved, in the process of airing mature crops, a fixed airing time is set, the influence of the weight and weather of the crops on the airing is not considered, the airing time is insufficient or longer, and the crops are not aired, so that the waste caused by rotting and deterioration or the lower moisture is caused, and the quality of the crops is lower. Factors causing the crop to be wasted due to decay and deterioration after the completion of the non-sunning or lower quality of the crop due to lower moisture are often as follows: when the mature crops are dried, the fixed drying time is set, the influence of the weight and weather of the crops on the drying is not considered, the drying time is insufficient or longer, the crops are not dried and are finished, the waste is caused by rotting and deterioration, or the quality of the crops is lower due to lower moisture. If the above factors are solved, the effect of avoiding the waste of the crops caused by decay and deterioration or lower quality of the crops caused by lower moisture when the crops are not dried can be achieved. To achieve this effect, first, weather information is acquired. Thus, weather information such as temperature, humidity, illumination intensity and the like can be obtained. And secondly, determining the weight of the crops to be aired in response to the weather information meeting the preset weather conditions. Therefore, crops can be aired when the weather information meets the preset weather conditions. And then, determining the airing duration according to the weather information and the weight of the crops to be aired. Therefore, the time length required to be dried can be accurately determined through the corresponding relation table of the temperature, the weight of the crops to be dried and the drying time length. The condition of insufficient airing time or overlong airing time is avoided, so that the condition that crops are wasted due to rotting and deterioration after the completion of the non-airing of the crops or the quality of the crops is low due to low moisture is avoided. Finally, according to the airing duration, airing the crops to be aired. Therefore, the crop airing treatment is completed, and the condition that crops are wasted due to decay and deterioration or lower in quality due to lower moisture when the crops are not aired is avoided.
The above embodiments of the present disclosure have the following advantages: crop yield may be improved by the crop environment information processing method of some embodiments of the present disclosure. In particular, the reason for the lower crop yield is that: the growth environment required for the same crop in different growth stages may be different, and setting fixed environmental parameters may result in a crop growth stage that does not adapt to the set growth environment, resulting in lower crop yields. Based on this, the crop environment information processing method of some embodiments of the present disclosure first acquires a crop image of a target crop. Thus, the type and maturity of the crop can be determined from the crop image. Secondly, determining crop type and crop maturity information of the target crops according to the crop images; a crop environmental information set is acquired. Thus, the information of the growth environment of the crop can be obtained. And then inputting the crop maturity information, the crop category and the crop environment information set into a pre-trained environment information analysis model to obtain crop maturity information and an environment information analysis result. Thus, it can be determined whether the current growing environment parameters meet the environment required for the above-mentioned crop category and maturity by a pre-trained environmental information analysis model. And finally, controlling the associated environment regulating equipment to regulate the environment of the target crop according to the environmental information analysis result. Therefore, the unsatisfied environmental parameters can be regulated and controlled through the environmental regulation and control equipment so as to meet the growth requirements of target crops. Therefore, the yield of crops is improved through the regulation and control of the environment.
Referring now to fig. 2, a schematic diagram of an electronic device 200 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), car terminals (e.g., car navigation terminals), and the like, as well as stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 2 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.
As shown in fig. 2, the electronic device 200 may include a processing means 201 (e.g., a central processing unit, a graphics processor, etc.) that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 202 or a program loaded from a storage means 208 into a Random Access Memory (RAM) 203. In the RAM 203, various programs and data necessary for the operation of the electronic apparatus 200 are also stored. The processing device 201, ROM 202, and RAM 203 are connected to each other through a bus 204. An input/output (I/O) interface 205 is also connected to bus 204.
In general, the following devices may be connected to the I/O interface 205: input devices 206 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 207 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 208 including, for example, magnetic tape, hard disk, etc.; and a communication device 209. The communication means 209 may allow the electronic device 200 to communicate with other devices wirelessly or by wire to exchange data. While fig. 2 shows an electronic device 200 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 2 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via the communication device 209, or from the storage device 208, or from the ROM 202. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing device 201.
It should be noted that, the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: a crop image of a target crop is acquired. And determining the crop type and the crop maturity information of the target crop according to the crop image. Acquiring a crop environment information set, wherein crop growth information in the crop environment information set is one of the following: temperature information, humidity information, illumination intensity information, and carbon dioxide concentration information. Inputting the crop maturity information, the crop category and the crop environment information set into a pre-trained environment information analysis model to obtain an environment information analysis result. And controlling the related environment regulating equipment to regulate the environment of the target crop according to the environmental information analysis result.
Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
Claims (7)
1. A crop environment information processing method, characterized by comprising:
acquiring a crop image of a target crop;
determining crop category and crop maturity information of the target crop according to the crop image;
Acquiring a crop environment information set, wherein crop growth information in the crop environment information set is one of the following: temperature information, humidity information, illumination intensity information and carbon dioxide concentration information;
inputting the crop maturity information, the crop category and the crop environment information set into a pre-trained environment information analysis model to obtain an environment information analysis result;
according to the environmental information analysis result, controlling the related environmental regulation equipment to regulate and control the environment of the target crop;
determining a growth stage of the target crop;
determining pest type information in response to the growth stage of the target crop being a pest growth stage;
acquiring a historical drug data set according to the determined insect pest type information;
determining a medication spray amount from the historical medication data set;
wherein said determining a medication spray amount from said historical medication data set comprises:
determining a medication spray discrete value from the historical medication data set, wherein the medication spray discrete value is determined by the formula:
，
wherein ,for spraying the medicine, the drug is added>The number of historical medication data included for the historical medication data set,/- >For historical drug data set +.>Historical drug data,/->A mean value of each historical medication data included for the historical medication data set;
generating a discrete value offset range according to the medicine spraying discrete value;
determining a medicine spraying range according to the discrete value deviation range;
determining the spraying amount of the medicine according to the insect pest degree;
and controlling the associated spraying equipment to spray the medicine to the target crops according to the medicine spraying quantity.
2. The method of claim 1, wherein after the determining crop category and crop maturity information for the target crop from the crop image, the method further comprises:
acquiring a historical growth data set corresponding to the crop category, wherein the historical growth data set in the historical growth data set corresponds to a crop growth stage;
and determining the growth stage of the insect pest according to the historical growth data set.
3. The method of claim 1, wherein the environmental information analysis model is trained by:
obtaining a sample set, wherein samples in the sample set comprise sample crop maturation information, sample crop categories, a sample crop environment information set and sample environment information analysis results corresponding to the sample crop categories and the crop environment information set;
Selecting a sample from the set of samples;
inputting the sample into an initial network model to obtain an environmental information analysis result corresponding to the sample;
determining a loss value between an environmental information analysis result corresponding to the sample and a sample environmental information analysis result included in the sample;
and adjusting network parameters of the initial network model in response to the loss value being greater than or equal to a preset threshold.
4. A method according to claim 3, characterized in that the method further comprises:
and determining the initial network model as an environmental information analysis model in response to the loss value being less than the preset threshold.
5. The method according to claim 1, wherein the method further comprises:
and controlling the associated picking equipment to carry out picking treatment on the target crops according to the crop maturity information of the target crops.
6. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1 to 5.
7. A computer readable medium, characterized in that a computer program is stored thereon, wherein the program, when executed by a processor, implements the method according to any of claims 1 to 5.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111563625A (en) * | 2020-05-08 | 2020-08-21 | 山东贵合信息科技有限公司 | Method, device and medium for spraying pesticide to crops based on Internet of things |
| CN112772614A (en) * | 2020-12-31 | 2021-05-11 | 广州极飞科技股份有限公司 | Spraying method and device for plant protection equipment, plant protection equipment and processor |
| CN114903022A (en) * | 2022-05-13 | 2022-08-16 | 中国农业科学院烟草研究所(中国烟草总公司青州烟草研究所) | Intelligent pesticide spraying control method and system based on pesticide spraying machine |
| CN115777660A (en) * | 2022-11-25 | 2023-03-14 | 中联智慧农业股份有限公司 | Method, device, storage medium, processor and system for controlling drug amount |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10586105B2 (en) * | 2016-12-30 | 2020-03-10 | International Business Machines Corporation | Method and system for crop type identification using satellite observation and weather data |
| CN116630663A (en) * | 2023-05-11 | 2023-08-22 | 中国农业科学院烟草研究所(中国烟草总公司青州烟草研究所) | Intelligent pesticide application method and system based on Internet of things |
-
2023
- 2023-08-02 CN CN202310961655.4A patent/CN116681544B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111563625A (en) * | 2020-05-08 | 2020-08-21 | 山东贵合信息科技有限公司 | Method, device and medium for spraying pesticide to crops based on Internet of things |
| CN112772614A (en) * | 2020-12-31 | 2021-05-11 | 广州极飞科技股份有限公司 | Spraying method and device for plant protection equipment, plant protection equipment and processor |
| CN114903022A (en) * | 2022-05-13 | 2022-08-16 | 中国农业科学院烟草研究所(中国烟草总公司青州烟草研究所) | Intelligent pesticide spraying control method and system based on pesticide spraying machine |
| CN115777660A (en) * | 2022-11-25 | 2023-03-14 | 中联智慧农业股份有限公司 | Method, device, storage medium, processor and system for controlling drug amount |
Non-Patent Citations (3)
| Title |
|---|
| 基于CNN的农作物病虫害图像识别模型;史冰莹;李佳琦;张磊;李健;《计算机系统应用》(06);全文 * |
| 基于作物病害胁迫声发射的精准施药;王秀清;游国栋;杨世凤;《农业工程学报》(03);全文 * |
| 近56年西南地区四季参考作物蒸散量变化成因分析;刘悦 等;《节水灌溉》(第12期);54-59 * |
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