JP-2026076024-A - Method and system for estimating the state of weed occurrence in a field

Abstract

[Problem] To support the determination of whether additional control measures are necessary after herbicide application. [Solution] A method is disclosed for predicting the future state of weeds in a field after herbicide application in the current growing season, which is performed by one or more computing devices. The method includes: acquiring first information indicating the state of weeds in the field during one or more past growing seasons; acquiring second information indicating the estimated leaf age of the weeds at the time of herbicide application in the field during the current growing season; acquiring third information associated with the herbicide, which indicates the maximum leaf age of the weeds at which the herbicide is effective; and generating and providing to the user fourth information indicating the future state of weeds in the field after herbicide application, based on the first information, the second information, and the third information. [Selection Diagram] Figure 3

Inventors

• 入川 太郎
• 宮内 海南斗
• 佐藤 孝彦

Assignees

• 株式会社クボタ

Dates

Publication Date

20260511

Application Date

20241023

Claims (20)

1. A method for predicting future weed growth in a field after herbicide application during the current growing season, which is performed by one or more computing devices, To obtain first information indicating the weed occurrence state in the field during one or more past growing seasons, To obtain second information indicating the estimated leaf age of the weeds at the time of application of the herbicide in the field during the current cropping season, To obtain third information associated with the herbicide, which indicates the maximum leaf age of the weed in which the herbicide is effective, Based on the first information, the second information, and the third information, a fourth piece of information is generated and provided to the user, indicating the future state of weed growth in the field after the herbicide has been applied. A method that includes this.
2. Generating the above fourth information is Based on the second and third pieces of information, a fifth piece of information is generated that shows the difference between the estimated leaf age of the weed at the time of application of the herbicide and the maximum leaf age of the weed at which the herbicide is effective. Based on the first information and the fifth information, generate the fourth information, The method according to claim 1, including the method described in claim 1.
3. Generating the above fourth information is The probability is determined using a predictive model that defines the relationship between multiple input variables, including the first and fifth pieces of information, and the probability of a high incidence of weeds in the field during the current growing season. To generate the fourth piece of information based on the aforementioned probability, The method according to claim 2, including the method described in claim 2.
4. Generating the above fourth information is The probability is determined using a predictive model that defines the relationship between multiple input variables, including the first to third pieces of information, and the probability of a high incidence of weeds in the field during the current growing season. Based on the aforementioned probability, the fourth piece of information is generated, The method according to claim 1, including the method described in claim 1.
5. The method according to claim 3, wherein the prediction model is a logistic regression model.
6. To obtain the information described in the second paragraph, To obtain information regarding temperature from a specific day prior to the date of application of the herbicide during the current cropping season until the date of application, The second information is generated by determining the estimated leaf age of the weed on the day the herbicide is applied, based on the temperature information. The method according to claim 1, including the method described in claim 1.
7. The temperature information mentioned above indicates the effective accumulated temperature from the specified date to the spraying date. Generating the second information includes determining the estimated leaf age using a model that defines the relationship between the effective accumulated temperature and the estimated leaf age. The method according to claim 6.
8. The aforementioned field is a paddy field where rice is cultivated. The aforementioned specific day is the day of puddling during the current cropping season, or the day from one week before the puddling day to the day of rice planting. The method according to claim 6.
9. The method according to claim 1, wherein the acquisition of the second piece of information is performed after the herbicide has been sprayed.
10. To obtain the information in the previous third instance, To obtain information that identifies the type of herbicide, Based on information identifying the type of herbicide and a database recording the relationship between the type of herbicide and the maximum leaf age of the weed at which the herbicide is effective, the maximum leaf age of the weed is determined. The method according to claim 1, including the method described in claim 1.
11. The method according to claim 1, wherein the first piece of information indicates the amount of weeds in the field during the previous cropping season.
12. The method according to claim 1, wherein the first piece of information indicates the amount of weeds in the field during multiple past growing seasons.
13. Obtaining the aforementioned first information is To obtain information that identifies the aforementioned field, Based on information identifying the field and a database recording the relationship between the field and the amount of weeds in one or more past growing seasons, the amount of weeds in one or more past growing seasons is determined. The method according to claim 11, including the method described in claim 11.
14. The method according to claim 1, wherein the first information is determined based on images of the field taken during one or more past growing seasons, or information indicating the amount of weeds during one or more past growing seasons entered by the user.
15. The method according to claim 1, wherein obtaining the first information includes generating the first information based on images of the field taken during one or more past growing seasons.
16. The method according to claim 1, wherein the fourth piece of information indicates the abundance of weeds in the field at harvest time during the current growing season, or the risk of a high incidence of weeds.
17. The method according to claim 1, wherein providing the fourth information to the user includes displaying the fourth information on the display of the computer used by the user.
18. The method according to claim 17, wherein displaying the fourth information includes displaying the map including the field in association with the fourth information.
19. The method according to claim 18, wherein displaying the fourth information includes displaying the area of the field on the map in different colors according to the amount of weeds indicated by the fourth information, or the risk of weed proliferation.
20. A system comprising one or more computing devices that perform the method according to any one of claims 1 to 19.

Description

This disclosure relates to a method and system for estimating the weed occurrence in a field. Research and development of smart agriculture, utilizing ICT (Information and Communication Technology) and IoT (Internet of Things), is progressing as a next-generation agricultural approach. Smart agriculture aims to improve productivity, alleviate labor shortages, and reduce environmental impact. For example, smart agriculture is being used for weed control in fields. Patent Document 1 discloses a computer system that identifies weed-bearing locations based on captured images of a field and location information of the shooting point, and then uses a drone to perform the task of spraying herbicides on those locations. International Publication No. 2020/157878 Figure 1 is a block diagram showing a schematic configuration of a system including a computing device that performs the method according to an exemplary embodiment of the present disclosure.Figure 2 is a block diagram showing the schematic configuration of a computing device.Figure 3 is a flowchart showing an example of a method for predicting the state of weed growth according to an exemplary embodiment of the present disclosure.Figure 4 is a schematic diagram illustrating an example of the relationship between the timing of some tasks performed in a rice transplanting field and the method in this embodiment.Figure 5 shows an example of a database in which the first piece of information is recorded.Figure 6 shows an example of a database in which the third piece of information is recorded.Figure 7 is a graph showing an example of a model for the progression of leaf age in weeds.Figure 8 is a flowchart showing an example of a process for acquiring second information based on a leaf age progression model.Figure 9 shows an example of the relationship between information and a model used in exemplary embodiments of this disclosure.Figure 10 shows another example of the relationship between information and a model used in exemplary embodiments of this disclosure.Figure 11 is a flowchart showing the method for generating the fourth piece of information in the example shown in Figure 9.Figure 12 is a flowchart showing the method for generating the fourth piece of information in the example shown in Figure 10.Figure 13 shows an example of information displayed on a screen.Figure 14 is a schematic diagram showing an example of the system configuration according to an exemplary embodiment of the present disclosure.Figure 15 is a block diagram showing an example configuration of a server, work vehicle, drone, and terminal device.Figure 16 is a flowchart showing an example of the process of generating and recording the first piece of information. The embodiments of the present invention will be described below. However, unnecessarily detailed descriptions may be omitted. For example, detailed descriptions of already well-known matters and redundant descriptions of substantially identical configurations may be omitted. This is to avoid unnecessarily verbose descriptions and to facilitate understanding for those skilled in the art. The inventors provide the accompanying drawings and the following description to enable those skilled in the art to fully understand the invention, and do not intend to limit the subject matter described in the claims. In the following description, components having the same or similar function are denoted by the same reference numerals. The following embodiments are illustrative examples for realizing the technical concept of the present invention, and the present invention is not limited to these embodiments. For example, the numerical values, shapes, materials, steps, and order of steps shown in the following embodiments are merely examples, and various modifications are possible as long as they do not create a technical inconsistency. Furthermore, it is possible to combine one embodiment with others. The size and positional relationships of the components shown in each drawing may be exaggerated for ease of understanding. <Terminology> "Weeds" are plants present in a field that are not the crops being cultivated. "Weed occurrence status" refers to the quantity of weeds, such as the amount of weeds, the number of weeds, or the percentage of the field occupied by weeds. "Predicting future weed occurrence status" includes determining indicator values related to weed quantity, such as the predicted number of weeds at a future point in time (e.g., harvest time), whether there will be many or few weeds, or the probability of a high weed outbreak. "Leaf age" is a value that expresses the growth stage of a plant in terms of the number of leaves. For example, depending on the number of leaves, leaf age can be expressed as 1-leaf stage, 1.5-leaf stage, 2-leaf stage, 2.5-leaf stage, 3-leaf stage, etc. The "estimated leaf age" of weeds at the time of herbicide application is an estimated value of the leaf age of the weeds at the time the herbicide is