JP-7856108-B2 - Crop cultivation support device, crop cultivation support method, and crop cultivation support program

JP7856108B2JP 7856108 B2JP7856108 B2JP 7856108B2JP-7856108-B2

Inventors

森洋治
星野綾子
遠藤雄也
渡部悠紀
矢島成人

Assignees

日本電気株式会社

Dates

Publication Date: 20260511
Application Date: 20210915

Claims (10)

A means of receiving requests that include any of the following: size, taste, harvest time, and yield of the crop to be cultivated. A trained model that has learned the relationship between methods for growing multiple crops and the results of growing the multiple crops, including the size, taste, harvest time, and yield of the crops, and a generation means that generates response information including the method for growing the target crop based on the request, An output means for outputting the aforementioned cultivation method, Equipped with, The aforementioned trained model is a trained graph that includes multiple nodes related to the cultivation of crops that have been cultivated in the past, and links that show the relationships between those nodes, and the relationships between the nodes have been learned . The system further comprises a link prediction means that uses a target graph containing multiple nodes relating to the target to be trained and the trained graph to perform link prediction for predicting the relationships between nodes that are not connected by links in the target graph and the trained graph, The generation means generates the response information based on the link prediction in the growth target graph. A device to support crop growth.
The system further comprises evidence generation means for generating evidence information including past cases similar to the cultivation methods of the crops to be cultivated, The crop cultivation support device according to claim 1, wherein the output means further outputs the basis information.
The link prediction means uses the target growth graph, which includes a plurality of nodes relating to the target growth, and the grown-up graph to predict the relationships between nodes that are not connected by links in the target growth graph and the grown-up graph, and predicts nodes that link to nodes in the target growth graph from among the nodes in the grown-up graph relating to operations performed during the growth of crops that were grown in the past. The crop cultivation support apparatus according to claim 1 or 2, wherein the generation means generates the response information corresponding to the node predicted by the link prediction means.
The aforementioned receiving means further accepts input of conditions for the cultivated graph, The crop cultivation support device according to claim 3, wherein the link prediction means predicts a node that links to a node included in the cultivation target graph from among the nodes relating to operations performed during the cultivation of crops that have been cultivated in the past and are included in the cultivated graph that satisfies the above conditions.
The crop cultivation support device according to claim 3, further comprising an evaluation means for evaluating the recommendation level of the node predicted by the link prediction means, based on other nodes included in the cultivated graph that include the node predicted by the link prediction means.
The aforementioned receiving means further receives input of at least one of the content and timing of the work to be performed on the training target, The link prediction means uses the target graph, which includes nodes indicating at least one of the content and timing of the input work, and the completed graph to calculate the probability that a node indicating a predetermined training result will be linked to the target graph by the link prediction, which predicts the relationship between nodes that are not connected by links in the target graph and the completed graph. The crop cultivation support apparatus according to claim 1 or 2, wherein the generation means generates the response information based on the probability calculated by the link prediction means.
The receiving means receives input of desired cultivation results for the crop to be cultivated. The link prediction means uses the cultivation target graph, which includes nodes showing the input cultivation results, and the cultivated graph to predict the relationships between nodes that are not connected by links in the cultivation target graph and the cultivated graph, and predicts nodes that link to nodes in the cultivation target graph from among the nodes in the cultivated graph that relate to operations performed during the cultivation of crops that have been cultivated in the past. The crop cultivation support apparatus according to claim 1 or 2, wherein the generation means generates the response information based on the node calculated by the link prediction means.
The link prediction means uses the target crop graph, which includes a plurality of nodes relating to the target crop, and the cultivated crop graphs, which are generated for each of the plurality of crops that have been cultivated in the past , to predict the relationships between nodes that are not connected by links in the target crop graph and the cultivated crop graph, thereby identifying the plurality of crops that have been cultivated in the past that have a predetermined relationship with the target crop. The crop cultivation support apparatus according to claim 1 or 2, wherein the generation means generates the response information relating to the crop identified by the link prediction means.
Computers We accept requests that include any of the following: size, taste, harvest time, and yield of the crop to be cultivated. Based on a trained model that has learned the relationship between cultivation methods for multiple crops and cultivation results including size, taste, harvest time, and yield of the multiple crops, and the request, response information including the cultivation method for the target crop is generated. Output the aforementioned breeding method, The aforementioned trained model is a trained graph that includes multiple nodes related to the cultivation of crops that have been cultivated in the past, and links that show the relationships between those nodes, and the relationships between the nodes have been learned . Using the target graph containing multiple nodes related to the target to be trained and the trained graph, link prediction is performed to predict the relationships between nodes that are not connected by links in the target graph and the trained graph. Based on the link prediction in the growth target graph, the response information is generated. Methods for supporting crop cultivation.
For computers, A process that accepts requests that include one of the following: size, taste, harvest time, and yield of the crop to be cultivated. A trained model that has learned the relationship between methods for growing multiple crops and the results of growing the crops, including their size, taste, harvest time, and yield, and a request, generates response information including the method for growing the target crop. A process to output the aforementioned cultivation method, Make it run, The aforementioned trained model is a trained graph that includes multiple nodes related to the cultivation of crops that have been cultivated in the past, and links that show the relationships between those nodes, and the relationships between the nodes have been learned . A process for predicting links to predict the relationships between nodes in the target graph and the developed graph that are not connected by links, using a target graph containing multiple nodes related to the target to be trained and the developed graph. Based on the link prediction in the aforementioned growth target graph, the response information is generated. Agricultural crop cultivation support program.

Description

This invention relates to a crop cultivation support device, etc., that generates information related to crop cultivation. In crop cultivation, a wide variety of tasks are involved, and the content and timing of each task affect the cultivation results. Generally, the determination of such tasks and their timing relies on experience and intuition. Furthermore, as shown in Patent Document 1 below, technologies for acquiring plant cultivation management information using sensors are also known. Japanese Patent Publication No. 2017-184678 This is a block diagram showing the configuration of a crop cultivation support device according to a first exemplary embodiment of the present invention.This is a flowchart showing the flow of a crop cultivation support method according to the first exemplary embodiment of the present invention.This diagram illustrates feature learning in graph-based relational learning.This figure shows an overview of a crop cultivation support method according to a second exemplary embodiment of the present invention.This is a block diagram showing the configuration of a crop cultivation support device according to a second exemplary embodiment of the present invention.This is a flowchart showing the processing flow performed by the crop cultivation support device according to a second exemplary embodiment of the present invention.This figure shows an example of response information.This figure shows an overview of a crop cultivation support method according to a third exemplary embodiment of the present invention.This is a block diagram showing the configuration of a crop cultivation support device according to a third exemplary embodiment of the present invention.This is a flowchart showing the processing flow performed by a crop cultivation support device according to a third exemplary embodiment of the present invention.This figure shows an overview of a crop cultivation support method according to a fourth exemplary embodiment of the present invention.This is a block diagram showing the configuration of a crop cultivation support device according to a fourth exemplary embodiment of the present invention.This is a flowchart showing the processing flow performed by the crop cultivation support device according to the fourth exemplary embodiment of the present invention.This figure shows an overview of a crop cultivation support method according to a fifth exemplary embodiment of the present invention.This is a block diagram showing the configuration of a crop cultivation support device according to a fifth exemplary embodiment of the present invention.This is a flowchart showing the processing flow performed by the crop cultivation support device according to the fifth exemplary embodiment of the present invention.This diagram illustrates an example of predicting the training results of a target based on features calculated from the training target graph and the training completion graph.This is a diagram illustrating the configuration for a software-based system to support crop cultivation. [Exemplary Embodiment 1] A first exemplary embodiment of the present invention will be described in detail with reference to the drawings. This exemplary embodiment is the basic form of the exemplary embodiments described later. (Crop cultivation support device) The configuration of the crop cultivation support device 1 according to this exemplary embodiment will be described with reference to Figure 1. Figure 1 is a block diagram showing the configuration of the crop cultivation support device 1. As shown in the figure, the crop cultivation support device 1 includes a receiving unit (receiving means) 11, a generation unit (generation means) 12, and an output unit (output means) 13. The reception unit 11 receives a request that includes one of the following: the size, taste, harvest time, or yield of the crop to be cultivated. The generation unit 12 generates response information, including the cultivation method for the crop to be cultivated, based on the request and a trained model that has learned the relationship between cultivation methods for multiple crops and cultivation results, including one of the following: the size, taste, harvest time, or yield of multiple crops. The output unit 13 outputs the above response information. According to the crop cultivation support device 1 having the above configuration, it receives a request regarding the crop to be cultivated. Then, based on the request, it generates response information including the cultivation method for the crop to be cultivated, using a trained model that has learned the relationship between cultivation methods for multiple crops and cultivation results including size, taste, harvest time, and yield of multiple crops. This allows for the generation of useful response information for cultivating the target crop, taking into account various information about crops cultivated in the past. Therefore, the above configuration has the ef