WO2021256621A1

WO2021256621A1 - Smart farm crop transaction system using artificial intelligence, and method therefor

Info

Publication number: WO2021256621A1
Application number: PCT/KR2020/015402
Authority: WO
Inventors: 이창훈
Original assignee: 주식회사 글로벌코딩연구소
Priority date: 2020-06-18
Filing date: 2020-12-03
Publication date: 2021-12-23

Abstract

Disclosed are a smart farm crop transaction system using artificial intelligence, and a method therefor. The smart farm crop transaction method using artificial intelligence comprises the steps of: taking a photograph of a crop; analyzing tenderness of the crop via image analysis; transmitting sales information including the tenderness of the crop to a preliminary consumer; and receiving a reply on whether to purchase from the preliminary consumer.

Description

Smart farm crop trading system and method using artificial intelligence

The present invention relates to a smart farm crop trading system and method using artificial intelligence, and more particularly, to a trading system in which the status of crops is continuously checked and transactions are made by providing the cultivation status of cultivated plants to consumers.

The dictionary definition of smart farm is an intelligent farm created by grafting information and communication technology (ICT) to agricultural technology. These smart farms use the Internet of Things (IoT) technology to measure and analyze the temperature, humidity, sunlight, carbon dioxide, soil, etc. of the crop cultivation facility, and drive the control device according to the analysis result to change it to an appropriate state. . If smart farms are actively introduced, there is an advantage in that they can create high added values such as productivity, efficiency, and quality improvement throughout the production, distribution, and consumption processes of agriculture.

On the other hand, the conventional direct transaction of agricultural products on the Internet is structured so that consumers can view and purchase agricultural products when the seller takes pictures of the agricultural products to be sold on the website. There were advantages.

However, the conventional method of direct trading of agricultural products has a structure in which the consumer has no choice but to trust the seller and transact with the seller because it is impossible to check the cultivation environment and cultivation process of the crops from the point of view of the consumer.

In addition, when the demand for crops exceeds the supply, the price rises excessively and acts as a burden on consumers.

Accordingly, there is a problem in that the seller grows crops with the burden of forecasting the demand.

Therefore, a smart farm crop trading system and method using artificial intelligence that can purchase reliable crops by providing data so that the cultivation status of crops can be checked in real time and confirming it by the consumer is required. .

An object of the present invention is to provide a smart farm crop trading system and method using artificial intelligence so that the cultivation status of crops can be checked using artificial intelligence and provided to consumers to increase the transparency of cultivation and transaction efficiency. .

In order to achieve the above object, the smart farm crop trading method using artificial intelligence according to an embodiment of the present invention includes the steps of taking a picture of the crop, analyzing the maturity of the crop through image analysis, to the prospective consumer Transmitting the sales information including the maturity level of the crop and receiving a reply from the preliminary consumer whether to purchase.

At this time, the step of analyzing the maturity includes the steps of black processing around the crop, measuring the RGB value of the color part of the crop, and determining the maturity based on the size of the crop in the preset first level range, and The two-level range may include analyzing maturity based on the color of the crop.

In addition, the sending of the sales information may include at least one of a maturity level of a crop, an expected harvest time, a minimum price, and a minimum quantity.

Smart farm crop transaction system and method using artificial intelligence according to the present invention using artificial intelligence to check the cultivation status of crops and provide them to consumers to increase transparency of cultivation and transaction efficiency using artificial intelligence There is an effect that can be provided of the farm crop trading system and the method.

1 is an operation flowchart illustrating a smart farm crop trading method using artificial intelligence according to an embodiment of the present invention.

FIG. 2 is a flowchart illustrating in detail an embodiment of a step of taking a picture of a crop among the steps of FIG. 1 .

3 is a flowchart illustrating in detail an embodiment of a step of analyzing maturity among the steps of FIG. 1 .

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In this case, it should be noted that in the accompanying drawings, the same components are denoted by the same reference numerals as much as possible. In addition, detailed descriptions of well-known functions and configurations that may obscure the gist of the present invention will be omitted. For the same reason, some components are exaggerated, omitted, or schematically illustrated in the accompanying drawings.

1 is an operation flow diagram showing a smart farm crop trading method using artificial intelligence according to an embodiment of the present invention, and FIG. It is a flowchart, and FIG. 3 is a flowchart illustrating in detail an embodiment of the step of analyzing maturity among the steps of FIG. 1 .

Referring to FIG. 1 , the smart farm crop trading method using artificial intelligence may be performed through the following steps.

In step (S110), it is possible to take a picture of the crop.

In this case, a plurality of pictures of crops may be taken, and one or more selected images among a plurality of photographed images may be used for the following analysis.

In this regard, an embodiment of the detailed configuration of step S110 will be described in more detail with reference to FIG. 2 .

Referring to FIG. 2 , in step S111 , a plurality of pictures of the crops may be taken.

In step S112 , when an overlapping portion between the plurality of photos is searched, a portion having a high color value among the overlapping portions may be treated as an effective portion, and a portion having a relatively low color value may be subjected to noise processing.

In step S113, the plurality of photos are synthesized, but the overlapping part can be synthesized by composing an effective part.

In step S120, the maturity level of crops may be analyzed through image analysis.

Therefore, it is possible to analyze the maturity of the current crop through the size and color detected from the image of the crop. have.

In this regard, an embodiment of the detailed configuration of step S120 will be described in more detail with reference to FIG. 3 .

Referring to FIG. 3 , in step S121, the surrounding crop is blacked, in step S122 the RGB value of the color part of the crop is measured, and in step S123, the size of the crop is based on a preset first level range. to determine the maturity level, and in the preset second level range, the maturity level may be analyzed based on the color of the crop.

For example, when the maturity level of crops is divided into 20 stages, maturity levels 1 to 10 may be determined based on size, and maturity levels 11 to 20 may be determined based on color.

In step (S130), it is possible to send the sales information including the maturity level of the crop to the prospective consumer. In this case, the information sent to the preliminary consumer may include at least one of the maturity level of the crop, the expected harvest time, the minimum price, and the minimum quantity.

Next, in step (S140), it is possible to receive a reply whether to purchase from the preliminary consumer.

In this way, by using artificial intelligence in the smart farm crop trading system and method using artificial intelligence, it is possible to check the cultivation status of crops and provide them to consumers, thereby increasing the transparency of cultivation and the efficiency of transactions.

On the other hand, a smart farm crop trading method using artificial intelligence can be implemented by a smart farm crop trading system using artificial intelligence, and an example thereof will be described below.

The crop trading system of the present invention is a system that brokers the trade of crops produced in a plurality of production sites (producer A, producer B, .. producer C).

Before a detailed description of each configuration, the crop trading process between the buyer and the producer in the crop trading system will be briefly described.

A plurality of producers (Producer A, Producer B, .. Producer C) access the crop trading system through a wired or wireless network (eg, a network for Internet connection), and publish the crops they produce on the crop trading system.

Buyers (Buyer 1, Buyer 2, .. Buyer N) access the crop trading system via a wired or wireless network (eg a network for internet connection) and reserve the purchase of the desired crops among the crops posted by the producers. do.

For example, assuming that Buyer 1 has reserved the purchase of crops from Producer A, Producer A uses the producer terminal (P) to display the images of the reserved crops on the crop image server from the first image of cultivation to the image of the expected harvest date. It is filmed every day (or a specific period) and sent (ie, uploaded).

The growth image transmission server 300 transmits the uploaded image by date to the terminal C of the purchaser A as it is or through a predetermined editing process.

When the harvest proceeds according to the completion of the growth of the purchased crop, Producer A sends the harvested crop to Buyer 1 to complete the transaction process.

Next, each configuration will be described in detail.

The crop image server receives and stores images of crops grown in crop production areas. Since the crop image server stores images of crops, storage for image storage is included, but storage is omitted since it is a self-explanatory configuration.

Depending on the operator, the crop image server may include a same crop determination unit and a standard image recording unit.

The same crop determination unit determines whether the crop image received on a specific day is the same crop image as that of the first crop by comparing the received images of crops by date.

The size and shape of crops gradually change according to the growth process, and the same crop determination unit compares the received images by date and examines the similarity to determine whether the received image of the crop is an image of the same crop.

According to the same crop determination unit, it is possible to prevent a crop reserved for purchase from being replaced with a crop of the same type produced by another producer.

On the other hand, the standard image recorder stores the average growth image of crops. The average growth image corresponds to an image in which all images of crops previously grown by producer A are collected.

More specifically, if, for example, Producer A is a producer who grows 'mushrooms', the crop video server captures images by date taken when Producer A cultivates mushrooms for the first time, and shoots for the second time cultivating mushrooms. It stores both the image by date of cultivation and the image by date taken during cultivation for the N times.

According to an embodiment, the standard image recording unit may extract the size of crops for each day from the 1st to Nth images and select an image of a crop having an average size as a standard image (ie, an average growth image).

The 'average growth image' is used in the output calculation unit, which will be described later.

The reservation server uses the image of the crop stored in the crop image server to initiate sales information of the corresponding crop, and receives a reservation for the purchase of the crop from the purchaser terminal (C).

Since the configuration of the reservation server is similar to that of a typical online shopping mall, a detailed description thereof will be omitted.

The growth image transmission server transmits the image of the crop reserved for purchase to the purchaser terminal C with the purchase reservation. In this case, the image of the crop reserved for purchase may be directly transmitted from the producer terminal P to the purchaser terminal C, but predetermined image processing may be performed to further increase the reliability of the crop cultivation process.

For example, the growth image transmission server may transmit images of crops reserved for purchase by date, among images of a plurality of crops stored in the crop image server, to the purchaser terminal C having a purchase reservation.

The pricing server calculates the expected yield by reflecting the risks that occur in the process of crop growth and calculates the unit selling price of the crop. In the process of growing crops, growth conditions may change depending on weather conditions, so crop yields may change, and yields may change depending on conditions such as the germination success rate of crops.

The risk may be applied only to a specific production site (for example, Producer A), or it may be applied to all production sites (Producer A to Producer C).

According to an embodiment, the price calculation server may include a yield calculation unit and a risk calculation unit.

The yield calculation unit calculates the expected yield of the crop. Specifically, it calculates the expected yield of the crop by analyzing the image of the crop by date received from the crop image server. For example, the image of the crop by date received from the crop image server is compared with the average growth image stored in the standard image recorder, and the expected yield is calculated based on the similarity between the two sides.

The risk calculator adjusts the initial selling price disclosed in the reservation server based on the expected yield for each producer (producer A, producer B to producer C) calculated by the yield calculator. At this time, whether to change or maintain the contract price of Buyer 1, who is the reservation purchaser, may be changed according to risk setting.

A detailed operation of uploading an image of a crop to the crop image server will be described.

In order to increase the reliability of the crop image, the crop image server is set to receive the image of the crop photographed through a dedicated application installed in the mobile terminal (ie, the producer terminal (P)) of the producer who produces the crop.

Here, the dedicated application is installed in the producer terminal (P). The dedicated application can access the crop trading system after authenticating the producer.

The dedicated application captures images of specific crops by capturing images in batches of rows or furrows in which crops are grown by video shooting, and extracting images of individual crops from the captured images.

It is very cumbersome for producers to separate and photograph individual crops (Ga, Gb, Gc.... Gx) one by one. When a user runs a dedicated application and moves crops (Ga, Gb, Gc.... Gx) in batches as a video in units of columns or furrows,

The video is processed in a dedicated application and extracted as an individual video of each crop. Then, the crop images extracted as individual images are uploaded to the crop image server.

Although not shown, it may be preferable to provide a number or letter mark in a row in which crops (Ga, Gb, Gc.... Gx) are grown to assist with such image processing. In addition, it is preferable to provide a stick as a mark so that the relative size of the crop can be confirmed so that the growth process of the crop can be clearly identified.

The embodiments of the present invention disclosed in the present specification and drawings are merely provided for specific examples in order to easily explain the technical contents of the present invention and help the understanding of the present invention, and are not intended to limit the scope of the present invention. It will be apparent to those of ordinary skill in the art to which the present invention pertains that other modifications based on the technical spirit of the present invention can be implemented in addition to the embodiments disclosed herein.

Claims

taking pictures of crops;

analyzing the maturity level of crops through image analysis;

sending sales information including the maturity level of the crop to a prospective consumer; and

Step of receiving a reply from the prospective consumer on whether to purchase

A smart farm crop trading method using artificial intelligence, including
According to claim 1,

The step of taking a picture of the crop,

taking a plurality of pictures of the crops;

when an overlapping portion between the plurality of photos is searched for, processing a portion having a high color value among the overlapping portions as an effective portion and performing noise processing on a portion having a relatively low color value; and

Synthesizing the plurality of photos by composing and synthesizing the overlapping part as an effective part

A smart farm crop trading method using artificial intelligence, including
According to claim 1,

The step of analyzing the maturity is

Blacking the crop periphery;

measuring the RGB value of the crop color portion; and

determining the maturity level based on the size of the crop in the first preset level range, and analyzing the maturity level based on the color of the crop in the second preset level range;

A smart farm crop trading method using artificial intelligence, including
The method of claim 1,

The step of sending the sales information is,

A smart farm crop trading method using artificial intelligence, characterized in that the transmission includes at least one of the maturity of the crops, the expected harvest time, the minimum price, and the minimum quantity.