CN115376006A - Method, storage medium, and processor for predicting crop harvest date - Google Patents
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Abstract
Embodiments of the present application provide a method, a storage medium, and a processor for predicting a crop harvest date. The method comprises the following steps: obtaining a remote sensing image of a planting area where crops to be predicted are located; determining the initial date of the growth period of the crop to be predicted according to the remote sensing image; determining a base incubation value required for a crop to be predicted to grow from a first inception date of a first growth period to a second inception date of a second growth period; determining a predicted temperature value of each growth day of the crops to be predicted within a preset time period after the second initial day; determining a temperature accumulation difference value between the basic temperature accumulation value and the target temperature accumulation value; and determining the growth day corresponding to the predicted accumulated temperature value reaching the accumulated temperature difference value as the predicted harvest date of the crops to be predicted. Through the technical scheme, the predicted harvest date corresponding to the loss rate and the yield can be more accurately determined, the harvest yield of the crops is greatly improved, and the high loss rate of the crops during harvest on the improper harvest date can be avoided.
Description
Technical Field
The present application relates to the field of agriculture, and in particular to a method, storage medium and processor for predicting crop harvest date.
Background
In the actual agricultural production process, the accurate prediction of the harvest date of crops has important significance for the yield increase of the crops and the management of agricultural production. At present, the maturity of crops is generally judged through traditional experiences of farmers, and the harvest date of the crops is determined according to weather conditions and scheduling conditions of agricultural machinery and agricultural operators. Taking crops as rice as an example, the harvest date determined based on the traditional experience of farmers generally differs from the optimal harvest date by 10 days or even more than half a month. And the harvest yield of crops can be averagely improved by 10 percent when the crops are harvested at proper time. Moreover, the yield can be increased by 22.8% when the crops are harvested timely compared with the crops harvested too early, the processing quality of the rice can be effectively improved when the crops are harvested timely compared with the crops harvested too late, and the economic benefit of the rice is improved. The yield of the rice in China is about 2.12 hundred million tons in 2020, and if the loss is calculated according to 10% of actual loss, the loss of the rice in the process of harvesting production exceeds 2000 million tons.
Thus, the harvest date of a crop predicted by traditional experience of farmers will generally differ significantly from the optimal harvest period, and the yield of the crop will also be relatively reduced. Moreover, no matter the crops are harvested by agricultural machinery or the crops are harvested by manual mode, the direct loss of crop seeds can be caused during harvesting, the loss rate of the crops is greatly improved, and the harvest of the crops is difficult to guarantee.
Disclosure of Invention
It is an object of embodiments of the present application to provide a method, storage medium, and processor for predicting a crop harvest date.
To achieve the above object, a first aspect of the present application provides a method for predicting a harvest date of a crop, comprising:
obtaining a remote sensing image of a planting area where crops to be predicted are located;
determining an initial date of a growth period of a crop to be predicted according to the remote sensing image, wherein the growth period of the crop at least comprises a first growth period and a second growth period;
determining a base temperature value required for a crop to be predicted to grow from a first inception date of a first growth period to a second inception date of a second growth period;
determining a predicted temperature value of each growth day of the crops to be predicted in a preset time period after the second initial day;
determining a temperature accumulation difference value between the basic temperature accumulation value and a target temperature accumulation value, wherein the target temperature accumulation value is the temperature accumulation value required by the planted historical crops to grow from the first initial date of the first growth period to the optimal harvest date, and the optimal harvest date is determined according to the loss rate and yield of the historical crops in the actual harvest date;
and determining the growth day corresponding to the predicted accumulated temperature value reaching the accumulated temperature difference value as the predicted harvest date of the crops to be predicted.
In an embodiment of the application, the growth period of the crop further comprises a third growth period, the method further comprising: acquiring a historical remote sensing image of the planted historical crops before determining a temperature accumulation difference value between the basic temperature accumulation value and the target temperature accumulation value; determining a first initial date of a first growing period and a third initial date of a third growing period of the historical crops according to the historical remote sensing images; optionally selecting N dates within a preset time period after the third initial date as actual harvesting dates of the historical crops, and determining the loss rate and yield of the crops harvested within each actual harvesting date; and determining the actual harvest date corresponding to the minimum loss rate and the maximum yield as the optimal harvest date of the historical crops.
In an embodiment of the application, the method further comprises: determining a historical daily average temperature for each date within a period of time from a first inception date to the optimal harvest date of the historical crop from a first birth period after determining the harvest date corresponding to the minimum loss rate and the maximum yield as the optimal harvest date of the historical crop; determining a historical temperature value of each date according to a temperature interval where the historical daily average temperature is located; and determining the accumulated temperature value required by the historical crops to grow from the first initial date of the first growing period to the optimal harvesting date according to all the historical accumulated temperature values.
In an embodiment of the application, determining the historical accumulated temperature value of each date according to the temperature interval in which the historical average daily temperature is located comprises determining the historical accumulated temperature value of each date according to formula (1):
wherein GDD is the historical accumulated temperature value, T mean Is the historical daily average temperature.
In the embodiment of the application, the number of the remote sensing images is multiple, each remote sensing image corresponds to each growth day of the crop to be predicted, and the growth period further comprises a third growth period; determining the inception date of the growth period of the crop to be predicted according to the remote sensing image comprises: determining any two consecutive growth days as a date group; preprocessing the remote sensing image corresponding to each date group, and determining a first vegetation index of each preprocessed remote sensing image; determining a second vegetation index corresponding to each date group according to the first vegetation index of each preprocessed remote sensing image; arranging each date group in the order of growth days; for any selected date group, determining that the crop to be predicted enters a first growth period when a second vegetation index corresponding to the selected date group is a preset value and the second vegetation index of the date group arranged after the selected date group is higher than that of the selected date group, and determining the earliest date in the first date group after the selected date group as the first initial date of the first growth period of the crop to be predicted; for any selected date group, under the condition that a second vegetation index corresponding to the selected date group is a preset value, the second vegetation index of the date group arranged after the selected date group is lower than that of the selected date group, and the second vegetation index of the date group arranged after the selected date group is lower than a preset value, determining that the crop to be predicted enters a second growth period, and determining the date with the earliest time in the first date group after the selected date group with the second vegetation index lower than the preset value as a second initial date of the second growth period; and for any one selected date group, determining that the crop to be predicted enters a third growth period when the second vegetation index corresponding to the selected date group is smaller than that of the date group arranged before the selected date group and is smaller than that of the date group arranged after the selected date group, and determining the date with the earliest time in the selected date group as the third initial date of the third growth period.
In an embodiment of the present application, determining the second vegetation index corresponding to each date group according to the first vegetation index of each preprocessed remote sensing image includes: determining a second vegetation index for each date group according to equation (2):
NDVI FD ＝(NDVI i+1 -NDVI i )/Δ DOY (2)
wherein NDVI FD Is the second vegetation index, NDVI i+1 Is the first vegetation index, NDVI, of the preprocessed remote sensing image corresponding to the i +1 th growth day i A first vegetation index, delta, of the preprocessed remote sensing image corresponding to the ith growth day DOY The date interval between the i +1 th growth day and the i-th growth day.
In an embodiment of the application, the method further comprises: after determining a predicted harvest date for the crop to be predicted, acquiring meteorological data for a period of time before or after the predicted harvest date; under the condition that the meteorological data meet the preset meteorological conditions, the predicted harvest date of the crops to be predicted is adjusted according to the meteorological data; and under the condition that the meteorological data do not meet the preset meteorological conditions, harvesting the crops to be predicted according to the predicted harvesting date.
In the examples of the present application, the crop to be predicted is rice.
A second aspect of the present application provides a machine-readable storage medium having stored thereon instructions that, when executed by a processor, cause the processor to be configured to perform the method for predicting a crop harvest date as described above.
A third aspect of the present application provides a processor configured to perform the above-described method for predicting a crop harvest date.
Through above-mentioned technical scheme, can more accurately determine the prediction harvest date that corresponds with loss rate and output, can make crops carry out the in good time results at prediction harvest date, when promoting the output of crops by a wide margin, also can avoid crops to produce higher loss rate when the results, reduce human cost and time cost.
Additional features and advantages of embodiments of the present application will be described in detail in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the embodiments of the disclosure, but are not intended to limit the embodiments of the disclosure. In the drawings:
fig. 1 schematically shows a flow diagram of a method for predicting a crop harvest date according to an embodiment of the application;
FIG. 2 schematically illustrates a flow diagram of a method for predicting a crop harvest date according to yet another embodiment of the present application;
fig. 3 schematically shows an internal structure diagram of a computer device according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the specific embodiments described herein are only used for illustrating and explaining the embodiments of the present application and are not used for limiting the embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 schematically shows a flow diagram of a method for predicting a crop harvest date according to an embodiment of the present application. In one embodiment of the present application, as shown in fig. 1, there is provided a method for predicting a crop harvest date, comprising the steps of:
And step 102, determining the initial date of the growth period of the crop to be predicted according to the remote sensing image, wherein the growth period of the crop at least comprises a first growth period and a second growth period.
Step 103, determining a basal temperature value required for the crop to be predicted to grow from a first inception date of a first growth period to a second inception date of a second growth period.
And 104, determining the predicted temperature value of the crop to be predicted in each growth day in a preset time period after the second initial day.
And 105, determining a temperature accumulation difference value between the basic temperature accumulation value and a target temperature accumulation value, wherein the target temperature accumulation value is the temperature accumulation value required by the planted historical crops to grow from the first initial date of the first growing period to the optimal harvesting date, and the optimal harvesting date is determined according to the loss rate and the yield of the historical crops in the actual harvesting date.
And 106, determining the growth day corresponding to the predicted accumulated temperature value reaching the accumulated temperature difference value as the predicted harvest date of the crops to be predicted.
The crop to be predicted may refer to various plants cultivated in agriculture. In one embodiment, the crop to be predicted may be rice. Crops to be predicted can be planted in a preset planting area. The planting area may refer to a field. When the harvesting date of the crops is predicted, the processor can firstly acquire the remote sensing image of the planting area where the crops to be predicted are located. The remote sensing image can refer to a satellite remote sensing image and also can be an unmanned aerial vehicle multispectral image.
In one embodiment, if rainy weather before and after the satellite remote sensing image is acquired is more, it is difficult to acquire a clear satellite remote sensing image. At the moment, the multispectral image of the planting area where the crops to be predicted are located can be obtained through equipment with an image acquisition function, and image splicing and atmospheric correction can be carried out through mapping and photogrammetry software, so that a clear image of the planting area where the crops to be predicted are located can be obtained. Wherein, the equipment that possesses the image acquisition function can be unmanned aerial vehicle. Surveying and photogrammetry software may refer to drone surveying and photogrammetry software. In particular, pix4D software may be referred to.
After the remote sensing image of the planting area where the crop to be predicted is located is obtained, the processor can determine the initial date of the growth period of the crop to be predicted according to the remote sensing image. Wherein the growth period of the crop to be predicted may include at least a first growth period and a second growth period. The first growth period may refer to a transplanting period of the crop to be predicted. The second growth period may refer to the maturity stage of the crop to be predicted. The processor may further determine a basal temperature value required for the crop to be predicted to grow from a first inception date of the first growth period to a second inception date of the second growth period.
After determining the base temperature value, the processor determines a predicted temperature value for each growth day of the crop to be predicted within a preset time period after a second initial day of the second growth period. The preset time period can be customized according to actual conditions. For example, it may be 10 to 15 days after the second initial date of the second growth period of the crop to be predicted. The predicted accumulated temperature value is the accumulated temperature value of the crop to be predicted in the period of time. For example, if the inception date of the second growth period is 15, the preset time period is 15 to 25, i.e., all growth days after the second inception date are 16 to 25. Then, if the predicted temperature value of 16 # is X, the predicted temperature value of 17 # is X + Y, and the predicted temperature value of 18 # is X + Y + Z. That is, the predicted temperature value corresponding to each growth day is an accumulated temperature value.
After determining the predicted backlog value for each growing day, the processor may first determine the inception date of the growth period of the planted historical crop. Wherein the growth period of the historic crop can include at least a first growth period. The first growth period of the historic crop may be referred to as the transplant period. Specifically, the processor may determine a first inception date and an actual harvest date for a first growth period of the historic crop. The processor may then determine an optimal harvest date for the historic crop based further on the rate of loss and yield of the historic crop over the actual harvest date. After determining the optimal harvest date for the historic crop, the processor may determine a required temperature value, i.e., a target temperature value, for the historic crop to grow from the first inception date to the optimal harvest date for the first growth period.
After determining the target integrated temperature value, the processor may determine an integrated temperature difference value between the base integrated temperature value and the target integrated temperature value. In particular, the processor may pass through the AGDD 0 ＝AGDD-AGDD Second growth period And determining an integrated temperature value difference value between the basic integrated temperature value and the target integrated temperature value. Wherein AGDD refers to target temperature value, AGDD Second growth period Refers to the basal temperature value. AGDD Second growth period Can pass through
And (4) determining. Wherein GDD is the historical hypothermia value for each date from the first inception date of the first growth period to the second inception date of the second growth period. The processor may determine a growth day corresponding to the predicted temperature accumulation value that reaches the temperature accumulation difference value as a predicted harvest date for the crop to be predicted. The predicted harvest date may refer to a harvest date predicted to be more suitable for harvesting. That is, if harvesting is performed on the predicted harvest date, the accumulated temperature value of the crop may have reached the accumulated temperature value required for the historical crop to grow from the first initial date of the first growth period to the optimal harvest date, and the harvest can guarantee the yield of the crop and reduce the loss rate to some extent.
Through above-mentioned technical scheme, can more accurately determine the prediction harvest date that corresponds with loss rate and output, can make crops carry out the in good time results at prediction harvest date, when promoting the harvest output of crops by a wide margin, also can avoid crops to produce higher loss rate when the results, reduce human cost and time cost.
In one embodiment, the number of the remote sensing images is multiple, each remote sensing image corresponds to each growth day of the crop to be predicted, and the growth period further comprises a third growth period; determining the inception date of the growth period of the crop to be predicted according to the remote sensing image comprises the following steps: determining any two consecutive growth days as a date group; preprocessing the remote sensing image corresponding to each date group, and determining a first vegetation index of each preprocessed remote sensing image; determining a second vegetation index corresponding to each date group according to the first vegetation index of each preprocessed remote sensing image; arranging each date group in the order of growth days; for any selected date group, determining that the crop to be predicted enters a first growth period when a second vegetation index corresponding to the selected date group is a preset value and the second vegetation index of the date group arranged after the selected date group is higher than that of the selected date group, and determining the earliest date in the first date group after the selected date group as the first initial date of the first growth period of the crop to be predicted; for any selected date group, determining that the crop to be predicted enters a second growth period under the condition that a second vegetation index corresponding to the selected date group is a preset value, the second vegetation index of the date group arranged after the selected date group is lower than the second vegetation index of the selected date group, and the second vegetation index of the date group arranged after the selected date group is smaller than a preset value, and determining the earliest date in the first date group after the selected date group with the second vegetation index smaller than the preset value as a second initial date of the second growth period; and for any one selected date group, determining that the crop to be predicted enters a third growth period when the second vegetation index corresponding to the selected date group is smaller than that of the date group arranged before the selected date group and is smaller than that of the date group arranged after the selected date group, and determining the date with the earliest time in the selected date group as the third initial date of the third growth period.
The number of remote sensing images can be multiple. Each remote sensing image corresponds to each growth day of the crops to be predicted. The growth period of the crop to be predicted may also include a third growth period. The third growth period of the crop to be predicted may refer to the yellow-ripe period of the crop to be predicted. When determining the initial date of the growth period of the crop to be predicted according to the remote sensing image, the processor may first determine any two consecutive growth days as a date group. The processor may then determine a vegetation index for each remote sensing image prior to preprocessing each remote sensing image. In particular, the processor may determine the vegetation index of the remotely sensed image from NDVI = (NIR-RED)/(NIR + RED). And the NDVI is the vegetation index of the remote sensing image. NIR is near infrared band reflectance. RED is the reflectance in the RED band.
After determining the vegetation index for each remote sensing image, the processor may pre-process the remote sensing image for each date group. For example, an S-G filter calling algorithm can be used to smooth the remotely sensed image. The processor may further determine a first vegetation index for each of the preprocessed remote sensing images. Wherein, the first vegetation index can refer to the vegetation index of the remote sensing image after being smoothed. Specifically, the processor may determine the first vegetation index by NDVI _ smooth = scipy. The NDVI _ smooth refers to a first vegetation index, and the NDVI refers to the vegetation index of the remote sensing image before preprocessing. The windows _ length refers to the length of the window. The smaller the window _ length value, the closer the curve is to the true curve. The larger the window _ length value, the stronger the smoothing effect. Specifically, the window _ length value may range from 9 to 11.k is a fitting value obtained by fitting a polynomial of k order to the data points in the window. The larger the k value, the closer the curve is to the true curve. The smaller the k value, the stronger the curve smoothing effect. Specifically, the value of k may range from 3 to 5.
The processor may determine a second vegetation index corresponding to each date group from the first vegetation index of each preprocessed remote sensing image. Specifically, in one embodiment, determining the second vegetation index corresponding to each date group according to the first vegetation index of each preprocessed remote sensing image includes: determining a second vegetation index for each date group according to equation (2):
NDVI FD ＝(NDVI i+1 -NDVI i )/Δ DOY (2)
wherein NDVI FD Is the second vegetation index, NDVI i+1 Is the first vegetation index, NDVI, of the preprocessed remote sensing image corresponding to the i +1 th growth day i A first vegetation index, delta, of the preprocessed remote sensing image corresponding to the ith growth day DOY The date interval between the i +1 th growth day and the i growth day. Wherein the date interval Δ DOY May be 1.
The processor may rank each group of dates in order of growth days. For any selected date group, the processor may determine that the crop to be predicted enters the first growth period when the second vegetation index corresponding to the selected date group is a preset value and the second vegetation index of the date group arranged after the selected date group is higher than the second vegetation index of the selected date group, and may determine the earliest date in the first date group after the selected date group as the first initial date of the first growth period of the crop to be predicted. Wherein the first growth period of the crop to be predicted may refer to a transplanting period. For example, if the order of date groups A, B and C is: a is more than B and less than C, the second vegetation index corresponding to the date group A is A1, the second vegetation index corresponding to the date group B is B1, and the second vegetation index corresponding to the date group C is C1. Also, each of date groups a, B, and C may include two consecutive growth days, i.e., date group a may include two consecutive growth days, date group B may include two consecutive growth days, and date group C may include two consecutive growth days. Then, for the selected date group a, in the case where A1 is 0 and both B1 and C1 are greater than A1, the processor may determine that the crop enters the transplanting period, and may determine the earliest date in the date group B as the first inception date of the transplanting period.
For any selected date group, in the case that the second vegetation index corresponding to the selected date group is a preset value, the second vegetation index of the date group arranged after the selected date group is lower than the second vegetation index of the selected date group, and the second vegetation index of the date group arranged after the selected date group is smaller than the preset value, the processor may determine that the crop to be predicted enters the second growth period, and may determine the earliest date in the first date group after the selected date group, in which the second vegetation index is smaller than the preset value, as the second initial date of the second growth period of the crop to be predicted. Wherein the second growth period of the crop to be predicted may be referred to as the milk stage. For example, if the order of date groups A, B, and C is: a is more than B and less than C, the second vegetation index corresponding to the date group A is A1, the second vegetation index corresponding to the date group B is B1, and the second vegetation index corresponding to the date group C is C1. Also, each of date groups a, B, and C may include two consecutive growth days, i.e., date group a may include two consecutive growth days, date group B may include two consecutive growth days, and date group C may include two consecutive growth days. Then, for the selected day group a, where A1 is 0, and both B1 and C1 are less than A1, and both B1 and C1 are less than preset value Z, the processor may determine that the crop enters the maturity stage and may determine the earliest date in day group B as the first inception date of maturity stage. Wherein the predetermined difference Z may be-0.0065.
For any one selected date group, the processor may determine that the crop to be predicted enters a third growth period and may determine the earliest date in the selected date group as a third inception date of the third growth period for the crop to be predicted, if the second vegetation index corresponding to the selected date group is less than the second vegetation index of the date group that is ranked before the selected date group and the second vegetation index corresponding to the selected date group is less than the second vegetation index of the date group that is ranked after the selected date group. Wherein the third growth period of the crop to be predicted may refer to the yellow-ripe-full-ripe period. For example, if the order of date groups A, B, and C is: a is more than B and less than C, the second vegetation index corresponding to the date group A is A1, the second vegetation index corresponding to the date group B is B1, and the second vegetation index corresponding to the date group C is C1. Also, each of date groups a, B, and C may include two consecutive growth days, i.e., date group a may include two consecutive growth days, date group B may include two consecutive growth days, and date group C may include two consecutive growth days. Then, for the selected date group B, with B1 < A1 and B1 < C1, the processor may determine that the second vegetation index for date group B is a minimum for B1. The processor may determine that the crop enters the yellow-ripe-finish stage and may determine the earliest date in date group B as the first inception date of the yellow-ripe-finish stage.
In one embodiment, the growth period of the crop further comprises a third growth period, the method further comprising: acquiring a historical remote sensing image of the planted historical crops before determining a temperature accumulation difference value between the basic temperature accumulation value and the target temperature accumulation value; determining a first initial date of a first growing period and a third initial date of a third growing period of the historical crops according to the historical remote sensing images; optionally selecting N dates within a preset time period after the third initial date as actual harvesting dates of the historical crops, and determining the loss rate and yield of the crops harvested within each actual harvesting date; and determining the actual harvest date corresponding to the minimum loss rate and the maximum yield as the optimal harvest date of the historical crops.
The crop growth period may also include a third growth period. The third growth period of the historic crop may refer to the yellow-ripe period of the historic crop. Before determining the accumulated temperature difference value between the basic accumulated temperature value and the target accumulated temperature value, the processor can acquire historical remote sensing images of the planted historical crops, and determine a first initial date of a first breeding period and a third initial date of a third breeding period of the historical crops according to the historical remote sensing images. Wherein the historic crop can be rice. The first growth period of the historic crop may refer to the transplanting period of the historic crop.
The processor may optionally select N dates within a preset time period after the third inception date of the historic crop as the actual harvest date of the historic crop. Specifically, for a preset time period after the third inception date of the historic crop, the processor may select N dates every preset number of days within the preset time period as the actual harvest dates of the historic crop. For example, the third initial date of the historic crop can be used as the starting date, and N dates can be selected as the actual harvest dates of the historic crop every 3/4/5 days.
After determining the actual harvest dates of the historic crop, the processor may determine the loss rate and yield of the crop harvested within each actual harvest date. The processor may then determine the actual harvest date corresponding to the minimum loss rate and the maximum yield as the optimal harvest date for the historic crop. Wherein the crops can be harvested by an agricultural harvester. When crops are harvested by the agricultural harvester, the yield of the crops can be determined through the detection result of the sensor for measuring the yield of the crops, and the loss rate of the crops can be determined through the detection result of the sensor for measuring the yield loss rate of the crops on the agricultural harvester. Wherein the yield of the crop may refer to the harvest yield of the crop. The harvest loss rate measuring sensor may include a cleaning loss measuring sensor and an entrainment loss measuring sensor. The cleaning loss measuring sensor may be used to measure the number of seeds lost by cleaning in the thresher body of the harvester. The entrainment loss measurement sensor may be used to measure the number of seeds lost to entrainment within the thresher body of the harvester.
For example, the agricultural harvester may be a rice harvester. Before crops are harvested through the rice harvester, a cleaning loss measuring sensor can be installed below a straw chopper cutting knife baffle of the rice harvester, and an entrainment loss measuring sensor can be installed on the inner side wall of a threshing cylinder. Therefore, when the rice harvester is used for harvesting crops, the number of the seeds subjected to cleaning loss can be obtained through the cleaning loss measuring sensor, and the number of the seeds subjected to entrainment loss can be obtained through the entrainment loss measuring sensor. The processor may then determine a rate of loss of the crop based on the area of the harvesting area of the crop, the number of seeds lost by cleaning, and the number of seeds lost by entrainment. Specifically, the processor may determine a loss rate of the crop from S = N/a. Wherein S is the loss rate of the threshing body of the agricultural harvester, namely the loss rate of the crops. N is the number of seeds lost by cleaning or the number of seeds lost by entrainment. A is the area of the harvest area of the crop.
In one embodiment, the method further comprises: determining a historical daily average temperature of the historical crop for each date in a period from a first inception date of a first growing period to the optimal harvesting date after determining the harvesting date corresponding to the minimum loss rate and the maximum yield as the optimal harvesting date of the historical crop; determining a historical accumulated temperature value of each date according to a temperature interval in which the historical average daily temperature is located; and determining the accumulated temperature value required by the historical crops to grow from the first initial date of the first growing period to the optimal harvesting date according to all the historical accumulated temperature values.
After determining the harvest date corresponding to the minimum loss rate and the maximum yield as the optimal harvest date of the historic crop, the processor may first obtain the historic maximum temperature and the historic minimum temperature corresponding to each date in the period from the first initial date to the optimal harvest date of the first growing period of the historic crop. Wherein the first inception date of the historic crop can refer to a transplanting period of the historic crop. The processor may then determine a historical average daily temperature for each date based on the historical maximum and minimum temperatures for each date. In particular, the processor may be based on T mean ＝T max +T min Determining historical daily average temperature of the historical crop on each date in the time period from the first initial date of the first growing period to the optimal harvesting date. Wherein, T mean Refers to the historical daily average temperature, T max Refers to the historical maximum temperature, T min Refers to the historical lowest temperature.
After determining the historical average daily temperature for each date in the time period from the first inception date to the optimal harvest date of the first growth period, the processor may obtain a temperature interval in which the historical average daily temperature is located, and determine the historical temperature value for each date according to the temperature interval. Specifically, in one embodiment, determining the historical accumulated temperature value for each date according to the temperature interval in which the historical average daily temperature is located comprises determining the historical accumulated temperature value for each date according to formula (1):
wherein GDD is the historical accumulated temperature value, T mean Is the historical day average temperature.
The processor may determine the historical temperature value for each date according to equation (1) above. For example, if the historical average daily temperature T of a certain date mean In a first temperature interval, i.e. T mean <9 or T mean And when the date is more than or equal to 36, the historical temperature value of the date can be determined to be 0. If T is mean In a first temperature interval, namely T is more than or equal to 9 mean <36, then the historical temperature value of the date can be determined as T mean -9。
After determining the historical accumulated temperature value for each date, the processor may determine, from all of the historical accumulated temperature values, an accumulated temperature value required for the historical crop to grow from the first inception date to the optimal harvest date of the first growth period, i.e., a target accumulated temperature value. Wherein the first growth period of the historic crop can refer to a transplanting period of the historic crop. In particular, the processor may be based on
And determining a target temperature value. Wherein AGDD may refer to a target volume temperature value and GDD refers to a historical volume temperature value for each date from the first inception date to the best harvest date of the first growth period.
In one embodiment, the method further comprises: after determining a predicted harvest date for a crop to be predicted, acquiring meteorological data for a period of time before or after the predicted harvest date; under the condition that the meteorological data meet the preset meteorological conditions, the predicted harvest date of the crops to be predicted is adjusted according to the meteorological data; and under the condition that the meteorological data do not meet the preset meteorological conditions, harvesting the crops to be predicted according to the predicted harvesting date.
After determining the predicted harvest date for the crop to be predicted, the processor may acquire meteorological data for a period of time before or after the predicted harvest date. The meteorological data may include rainfall, humidity, and the like. Under the condition that the meteorological data meet the preset meteorological conditions, the processor can adjust the predicted harvest date of the crops to be predicted according to the meteorological data. For example, crops can be harvested 2 days after the predicted harvest date to avoid the difficulty in operating the agricultural harvester due to poor meteorological data of the predicted harvest date, so that the loss and the yield of the crops are increased. In the case that the weather data does not satisfy the preset weather conditions, the processor may harvest the crop to be predicted according to the predicted harvest date. Wherein, the preset meteorological conditions can be correspondingly set according to meteorological data. For example, if the weather data is precipitation, the preset weather condition may be a preset precipitation. If the meteorological data is humidity, the preset meteorological condition may be a preset humidity.
In one embodiment, as shown in FIG. 2, a flow diagram of another method for predicting a harvest date for a crop is provided.
The processor can acquire the historical remote sensing image of the planting area where the planted crops are located, and performs S-G filtering processing on the historical remote sensing image. Then, the processor can obtain the NDVI time sequence of the history remote sensing image after the S-G filtering processing, namely the corresponding first vegetation index. The processor can determine the first derivative of the NDVI corresponding to each date group, that is, the corresponding second vegetation index, by performing first derivation on the first vegetation index of the history remote sensing image subjected to the S-G filtering processing. The processor may determine the smallest first derivative of NDVI to determine the yellow-ripe period of the planted crop based on the date set to which the smallest first derivative of NDVI corresponds. Wherein, the planted crops can be rice. The processor may further determine the harvest loss rate of the crop to be predicted at different dates within the yellow maturity-full maturity period, so as to determine the date corresponding to the minimum loss rate as the optimal harvest period. The crop to be predicted may be rice.
Through above-mentioned technical scheme, can more accurately determine the prediction harvest date that corresponds with loss rate and output, can make crops carry out the in good time results at prediction harvest date, when promoting the output of crops by a wide margin, also can avoid crops to produce higher loss rate when the results, reduce human cost and time cost.
Fig. 1-2 are flow diagrams of a method for predicting a harvest date of a crop in one embodiment. It should be understood that although the various steps in the flow diagrams of fig. 1-2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-2 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least some of the sub-steps or stages of other steps.
Embodiments of the present application provide a storage medium having a program stored thereon, which when executed by a processor, implements the above-described method for predicting a crop harvest date.
The embodiment of the application provides a processor, and the processor is used for running a program, wherein the program runs to execute the method for predicting the harvest date of the crops.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 3. The computer apparatus includes a processor a01, a network interface a02, a memory (not shown in the figure), and a database (not shown in the figure) connected through a system bus. Wherein the processor a01 of the computer device is arranged to provide computing and control capabilities. The memory of the computer apparatus includes an internal memory a03 and a nonvolatile storage medium a04. The nonvolatile storage medium a04 stores an operating system B01, a computer program B02, and a database (not shown). The internal memory a03 provides an environment for running the operating system B01 and the computer program B02 in the nonvolatile storage medium a04. The database of the computer device is used for storing data such as remote sensing images. The network interface a02 of the computer apparatus is used for communicating with an external terminal through a network connection. The computer program B02 is executed by the processor a01 to implement a method for predicting a crop harvest date.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The embodiment of the application provides equipment, the equipment comprises a processor, a memory and a program which is stored on the memory and can run on the processor, and the following steps are realized when the processor executes the program: obtaining a remote sensing image of a planting area where crops to be predicted are located; determining an initial date of a growth period of a crop to be predicted according to the remote sensing image, wherein the growth period of the crop at least comprises a first growth period and a second growth period; determining a base incubation value required for a crop to be predicted to grow from a first inception date of a first growth period to a second inception date of a second growth period; determining a predicted temperature value of each growth day of the crops to be predicted in a preset time period after the second initial day; determining a temperature accumulation difference value between the basic temperature accumulation value and a target temperature accumulation value, wherein the target temperature accumulation value is the temperature accumulation value required by the planted historical crops to grow from the first initial date of the first growth period to the optimal harvest date, and the optimal harvest date is determined according to the loss rate and the yield of the historical crops in the actual harvest date; and determining the growth day corresponding to the predicted accumulated temperature value reaching the accumulated temperature difference value as the predicted harvest date of the crops to be predicted.
In one embodiment, the growth period of the crop further comprises a third growth period, the method further comprising: acquiring a historical remote sensing image of the planted historical crops before determining a temperature accumulation difference value between the basic temperature accumulation value and the target temperature accumulation value; determining a first initial date of a first growing period and a third initial date of a third growing period of the historical crops according to the historical remote sensing images; optionally selecting N dates within a preset time period after the third initial date as actual harvesting dates of the historical crops, and determining the loss rate and yield of the crops harvested within each actual harvesting date; and determining the actual harvest date corresponding to the minimum loss rate and the maximum yield as the optimal harvest date of the historical crops.
In one embodiment, the method further comprises: determining a historical daily average temperature of the historical crop for each date in a period from a first inception date of a first growing period to the optimal harvesting date after determining the harvesting date corresponding to the minimum loss rate and the maximum yield as the optimal harvesting date of the historical crop; determining a historical accumulated temperature value of each date according to a temperature interval in which the historical average daily temperature is located; and determining the accumulated temperature value required by the historical crops to grow from the first initial date of the first growth period to the optimal harvest date according to all the historical accumulated temperature values.
In one embodiment, determining the historical integrated temperature value for each date based on the temperature interval in which the historical day average temperature is located comprises determining the historical integrated temperature value for each date based on equation (1):
wherein GDD is the historical accumulated temperature value, T mean Is the historical day average temperature.
In one embodiment, the number of the remote sensing images is multiple, each remote sensing image corresponds to each growth day of the crop to be predicted, and the growth period further comprises a third growth period; determining the inception date of the growth period of the crop to be predicted according to the remote sensing image comprises the following steps: determining any two consecutive growth days as a date group; preprocessing the remote sensing image corresponding to each date group, and determining a first vegetation index of each preprocessed remote sensing image; determining a second vegetation index corresponding to each date group according to the first vegetation index of each preprocessed remote sensing image; arranging each date group in the order of growth days; for any selected date group, determining that the crop to be predicted enters a first growth period when a second vegetation index corresponding to the selected date group is a preset value and the second vegetation index of the date group arranged after the selected date group is higher than that of the selected date group, and determining the earliest date in the first date group after the selected date group as the first initial date of the first growth period of the crop to be predicted; for any selected date group, determining that the crop to be predicted enters a second growth period under the condition that a second vegetation index corresponding to the selected date group is a preset value, the second vegetation index of the date group arranged after the selected date group is lower than the second vegetation index of the selected date group, and the second vegetation index of the date group arranged after the selected date group is smaller than a preset value, and determining the earliest date in the first date group after the selected date group with the second vegetation index smaller than the preset value as a second initial date of the second growth period; and for any one selected date group, determining that the crop to be predicted enters a third growth period when the second vegetation index corresponding to the selected date group is smaller than that of the date group arranged before the selected date group and is smaller than that of the date group arranged after the selected date group, and determining the date with the earliest time in the selected date group as the third initial date of the third growth period.
In one embodiment, determining the second vegetation index corresponding to each date group according to the first vegetation index of each preprocessed remote sensing image comprises: determining a second vegetation index corresponding to each date group according to formula (2):
NDVI FD ＝(NDVI i+1 -NDVI i )/Δ DOY (2)
wherein NDVI FD Is the second vegetation index, NDVI i+1 Is the first vegetation index, NDVI, of the preprocessed remote sensing image corresponding to the i +1 th growth day i After pretreatment for the ith growth dayFirst vegetation index, delta, of the remote sensing image DOY The date interval between the i +1 th growth day and the i growth day.
In one embodiment, the method further comprises: after determining a predicted harvest date for a crop to be predicted, acquiring meteorological data for a period of time before or after the predicted harvest date; under the condition that the meteorological data meet the preset meteorological conditions, the predicted harvest date of the crops to be predicted is adjusted according to the meteorological data; and under the condition that the meteorological data do not meet the preset meteorological conditions, harvesting the crops to be predicted according to the predicted harvesting date.
In one embodiment, the crop to be predicted is rice.
The present application also provides a computer program product adapted to perform a program for initializing method steps for predicting a crop harvest date when executed on a data processing device.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art to which the present application pertains. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for predicting a harvest date of a crop, the method comprising:
obtaining a remote sensing image of a planting area where crops to be predicted are located;
determining the initial date of the growth period of the crop to be predicted according to the remote sensing image, wherein the growth period of the crop at least comprises a first growth period and a second growth period;
determining a base caloric value required for the crop to be predicted to grow from a first inception date of the first growth period to a second inception date of the second growth period;
determining a predicted temperature value of the crop to be predicted in each growth day within a preset time period after the second initial day;
determining a temperature accumulation difference value between the basic temperature accumulation value and a target temperature accumulation value, wherein the target temperature accumulation value is a temperature accumulation value required by the planted historical crop to grow from a first initial date of a first growing period to an optimal harvesting date, and the optimal harvesting date is determined according to the loss rate and yield of the historical crop within an actual harvesting date;
and determining the growth day corresponding to the predicted accumulated temperature value reaching the accumulated temperature difference value as the predicted harvest date of the crops to be predicted.
2. The method for predicting the harvest date of a crop as claimed in claim 1, wherein the growth period of the crop further comprises a third growth period, the method further comprising:
acquiring a historical remote sensing image of the planted historical crop before determining a temperature accumulation difference value between the basic temperature accumulation value and the target temperature accumulation value;
determining a first initial date of a first growing period and a third initial date of a third growing period of the historical crops according to the historical remote sensing images;
optionally selecting N dates within a preset time period after the third initial date as actual harvesting dates of the historical crops, and determining the loss rate and yield of the crops harvested within each actual harvesting date;
and determining the actual harvest date corresponding to the minimum loss rate and the maximum yield as the optimal harvest date of the historical crops.
3. The method for predicting a crop harvest date of claim 2, further comprising:
determining a historical daily average temperature for each date within a period of time from a first inception date of a first growth period to a best harvest date for the historical crop after determining the harvest date corresponding to a minimum loss rate and a maximum yield as the best harvest date for the historical crop;
determining a historical temperature accumulation value of each date according to the temperature interval of the historical daily average temperature;
and determining the accumulated temperature value required by the historical crops to grow from the first initial date of the first growth period to the optimal harvest date according to all the historical accumulated temperature values.
4. The method for predicting a harvest date for a crop as claimed in claim 3, wherein determining the historical temperature buildup value for each date based on the temperature interval in which the historical day average temperature is located comprises determining the historical temperature buildup value for each date based on equation (1):
wherein GDD is the historical accumulated temperature value, T mean Is the historical day average temperature.
5. The method for predicting the harvest date of a crop as claimed in claim 1, wherein there are a plurality of said remote sensing images, each remote sensing image corresponding to each growth day of said crop to be predicted, said growth periods further comprising a third growth period;
the determining the initial date of the growth period of the crop to be predicted according to the remote sensing image comprises the following steps:
determining any two consecutive growth days as a date group;
preprocessing the remote sensing image corresponding to each date group, and determining a first vegetation index of each preprocessed remote sensing image;
determining a second vegetation index corresponding to each date group according to the first vegetation index of each preprocessed remote sensing image;
arranging each date group according to the growth day sequence;
for any selected date group, determining that the crop to be predicted enters a first growth period when the second vegetation index corresponding to the selected date group is a preset value and the second vegetation index of the date group arranged after the selected date group is higher than the second vegetation index of the selected date group, and determining the earliest date in the first date group after the selected date group as the first initial date of the first growth period of the crop to be predicted;
for any selected date group, determining that the crop to be predicted enters a second growth period if the second vegetation index corresponding to the selected date group is a preset value, the second vegetation index of the date group arranged after the selected date group is lower than the second vegetation index of the selected date group, and the second vegetation index of the date group arranged after the selected date group is smaller than a preset value, and determining the earliest date in the first date group after the selected date group with the second vegetation index smaller than the preset value as a second initial date of the second growth period;
for any selected date group, determining that the crop to be predicted enters a third growing period and determining the date with the earliest time in the selected date group as a third initial date of the third growing period when the second vegetation index corresponding to the selected date group is smaller than the second vegetation index of the date group arranged before the selected date group and the second vegetation index corresponding to the selected date group is smaller than the second vegetation index of the date group arranged after the selected date group.
6. The method of claim 5, wherein determining the second vegetation index for each date group from the first vegetation index for each preprocessed remote sensed image comprises:
determining a second vegetation index for each date group according to equation (2):
NDVI FD ＝(NDVI i+1 -NDVI i )/Δ DOY (2)
wherein NDVI FD Is the second vegetation index, NDVI i+1 Is the first vegetation index, NDVI, of the preprocessed remote sensing image corresponding to the i +1 th growth day i Pre-treated for the ith growth dayFirst vegetation index, Δ, of the sensed image DOY The date interval between the i +1 th growth day and the i-th growth day.
7. The method for predicting a crop harvest date of claim 1, wherein the method further comprises:
after determining a predicted harvest date for the crop to be predicted, acquiring meteorological data for a period of time before or after the predicted harvest date;
under the condition that the meteorological data meet preset meteorological conditions, adjusting the predicted harvest date of the crops to be predicted according to the meteorological data;
and under the condition that the meteorological data do not meet the preset meteorological conditions, harvesting the crops to be predicted according to the predicted harvesting date.
8. The method for predicting the harvest date of a crop according to any one of claims 1 to 7, wherein the crop to be predicted is rice.
9. A machine-readable storage medium having instructions stored thereon, which when executed by a processor causes the processor to be configured to perform a method for predicting a crop harvest date according to any one of claims 1 to 8.
10. A processor configured to perform the method for predicting a crop harvest date according to any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115860285A (en) * | 2023-03-01 | 2023-03-28 | 浙江领见数智科技有限公司 | Method and device for predicting optimal transplanting period of tobacco |
| CN116797601A (en) * | 2023-08-24 | 2023-09-22 | 西南林业大学 | Image recognition-based Huashansong growth dynamic monitoring method and system |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115860285A (en) * | 2023-03-01 | 2023-03-28 | 浙江领见数智科技有限公司 | Method and device for predicting optimal transplanting period of tobacco |
| CN115860285B (en) * | 2023-03-01 | 2023-10-31 | 浙江领见数智科技有限公司 | Prediction method and device for optimal transplanting period of tobacco |
| CN116797601A (en) * | 2023-08-24 | 2023-09-22 | 西南林业大学 | Image recognition-based Huashansong growth dynamic monitoring method and system |
| CN116797601B (en) * | 2023-08-24 | 2023-11-07 | 西南林业大学 | Image recognition-based Huashansong growth dynamic monitoring method and system |
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