Genetic purity and productivity monitoring system and method for sunflower, corn and sweet corn plants

Abstract

The invention relates to a method for determining sunflower isolation boundaries and detecting corn tassels in order to maintain genetic purity in the production of sunflower, corn and sweet corn plants on agricultural fields, performed by a system comprising an unmanned aerial vehicle (10) and a user terminal for remote control of said unmanned aerial vehicle.

Description

[0001] GENETIC PURITY AND PRODUCTIVITY MONITORING SYSTEM AND METHOD FOR SUNFLOWER, CORN AND SWEET CORN PLANTS

[0002] TECHNICAL FIELD

[0003] The invention relates to a system and method developed for determining sunflower isolation boundaries and detecting corn tassels in order to maintain genetic purity in the production of sunflower, corn and sweet corn plants on agricultural fields.

[0004] PRIOR ART

[0005] The agricultural sector is constantly developing new methods and technologies with the aim of increasing productivity and quality. In order to maintain genetic purity in the production of plants such as sunflower, corn and sweet corn; determination of isolation boundaries, control of genetic operations, and detection of tassels is of great importance.

[0006] Setting isolation boundaries is a critical step to maintain the genetic purity of plants such as sunflowers, corn and sweet corn. Traditionally, these boundaries have often been set using geographic distancing and physical barriers. However, monitoring large areas and clearly defining boundaries can create difficulties in current terrain conditions. Manual determination of isolation boundaries is time-consuming and can be difficult to enforce precisely and effectively in large and rough agricultural areas. Furthermore, it also creates extra financial burdens along with loss of time for seed companies.

[0007] Genetic operations, especially in hybrid seed production, aim to preserve certain genetic characteristics and to produce products with desired characteristics. Supervision of these operations is crucial both for maintaining genetic purity and for production efficiency. Traditional methods are performed by field workers visually and with manual drone observations. However, carrying out these inspections in large agricultural areas requires intensive labor and time. Furthermore, human errors in manual inspections can compromise the maintenance of genetic purity. The detection of tassels in corn and sweet corn species also affects concepts such as labor, engineering, and time.

[0008] In corn production, tassel detection is vital for monitoring and controlling the pollination process. Traditionally, tassel detection is done manually, which is a time-consuming and costly process. Manual tassel detection, especially in large corn fields, makes it difficult to monitor the entire area homogeneously and can be prone to human errors. Accurate tassel detection is essential for productive and high-quality production of corn and effective monitoring of this process can improve production efficiency.

[0009] The manual nature of traditional methods severely limits the efficiency of monitoring and inspection processes over large agricultural areas. These methods include physical on-site monitoring and inspections of agricultural workers / machine-based labor by field workers, which requires intensive labor. These processes are timeconsuming and can incur large costs. For example, manual tassel detection in a large cornfield can take days or even weeks, which both increases labor costs and leads to loss of time. Homogeneous monitoring of large areas is very difficult with manual methods since it may not be possible to cover a large area with the same efficiency and precision with manpower. Furthermore, manual inspections are prone to human error, and overlooked tassels or mis-detections can compromise the maintenance of genetic purity. Human errors can cause undesirable genetic changes in production by causing undesirable mixtures during pollen spread and pollination processes. This situation can cause great losses and deterioration of genetic purity, especially in hybrid seed production. Therefore, these limitations of traditional manual methods can adversely affect efforts to increase productivity and quality in agriculture.

[0010] As a result, all the above-mentioned problems have made it imperative to make an innovation in the relevant technical field.

[0011] SUMMARY OF THE INVENTION

[0012] The present invention relates to a genetic purity and development monitoring system and method for sunflower, corn and sweet corn plants for eliminating the above- mentioned disadvantages and bringing new advantages to the relevant technical field. An object of the invention is to introduce a method for determining sunflower isolation boundaries and detecting corn tassels in order to maintain genetic purity in the production of sunflower, corn and sweet corn plants on agricultural fields.

[0013] Another object of the invention is to introduce a method that increases productivity in the production of sunflower, corn and sweet corn plants on agricultural fields.

[0014] In order to achieve all the purposes mentioned above and that will emerge from the detailed description below, the present invention relates to a method for determining sunflower isolation boundaries and detecting corn tassels in order to maintain genetic purity in the production of sunflower, corn and sweet corn plants on agricultural fields, performed by a system comprising an unmanned aerial vehicle and a user terminal for remote control of said unmanned aerial vehicle. Accordingly, it comprises the steps of obtaining at least one image of agricultural field from at least one image capture unit provided on said unmanned aerial vehicle, transmitting the obtained image to the user terminal in real time, applying said agricultural field image as input to a first artificial intelligence model that is trained with sunflower field and corn field images in the user terminal and, upon receiving at least one agricultural field image as input, that detects at least one of the sunflower field and corn field in the agricultural field image it has received as input, and detecting at least one of the sunflower field and corn field in the image, selecting at least one of the detected sunflower field and corn field and taking images of said field when it is determined that the unmanned aerial vehicle has approached the selected field up to the defined distance, transmitting the obtained images to the user terminal in real time, if the selected field is a sunflower field, applying said sunflower field image as input to a second artificial intelligence model that is trained with images of flowered sunflowers and sunflowers that have not flowered in the user terminal and, upon receiving at least one sunflower field image as input, that detects the flowering status of the sunflowers in the sunflower field image it has received as input, and detecting the flowering status of the sunflowers in the image, if the selected field is corn field, applying said corn field image as input to a third artificial intelligence model that is trained with corn top tassel images in the user terminal and, upon receiving at least one corn top tassel image as input, that detects the information regarding the tassel density in the corn image it has received as input, and detecting the corn top tassel density information in the image, generating a report output from the detected data, transmitting said report output to a network server via a communication unit.

[0015] A possible embodiment of the invention is characterized in that it comprises the steps of sending a route information from the user terminal to the unmanned aerial vehicle, and automatically directing the unmanned aerial vehicle to the route.

[0016] A possible embodiment of the invention is characterized in that it comprises the steps of inputting manual route commands from the user terminal, and moving the unmanned aerial vehicle according to manual route commands.

[0017] A possible embodiment of the invention is characterized in that it comprises the steps of sending a route information from the network server to the unmanned aerial vehicle, and automatically directing the unmanned aerial vehicle to the route.

[0018] A possible embodiment of the invention is characterized in that it comprises the step of transmitting the images received from the image capture unit via the user terminal in real time to a user interface connected to the network server via the communication unit.

[0019] A possible embodiment of the invention is characterized in that it comprises the steps of sending a route information from a network server to the unmanned aerial vehicle, and guiding the unmanned aerial vehicle according to route information via the remote user terminal.

[0020] A possible embodiment of the invention is characterized in that it is a system comprising an unmanned aerial vehicle and a user terminal that allows remote control of the unmanned aerial vehicle configured to perform the method of any one of claim 1 to claim 6 for determining sunflower isolation boundaries and detecting corn top tassels in order to maintain genetic purity in the production of sunflower and corn plants on agricultural fields.

[0021] Another possible embodiment of the invention is characterized in that the unmanned aerial vehicle comprises an image capture unit for obtaining at least one image of agricultural field, a communication unit for exchanging data, and a network server connected with communication unit.

[0022] BRIEF DESCRIPTION OF THE DRAWING

[0023] Fig. 1 shows a representative view of the working scenario of the system.

[0024] DETAILED DESCRIPTION OF THE INVENTION

[0025] In this detailed description, the subject of the invention is explained by way of example only for a better understanding of the subject, which shall not create any limiting effect.

[0026] The invention relates to a system and method developed for determining sunflower isolation boundaries and detecting corn tassels in order to maintain genetic purity in the production of sunflower, corn and sweet corn plants on agricultural fields. Thus, by maintaining the genetic integrity of the plants in agricultural fields, the risks of hybridization are minimized and it becomes possible to obtain higher yield and quality by closely monitoring the development processes of the plants. This situation contributes to the management of agricultural fields in a more precise and efficient way.

[0027] Referring to Fig. 1 , said system comprises an unmanned aerial vehicle (10) and a user terminal (40) for remote control of said unmanned aerial vehicle. Said user terminal (40) can be a computer, a mobile application provided on a mobile device, etc. In a possible embodiment of the invention, the unmanned aerial vehicle (10) can drive autonomously according to the route information received from the user terminal (40). In an alternative embodiment of the invention, the unmanned aerial vehicle (10) can be moved according to manual route commands entered from the user terminal (40). In both methods, it is ensured that the unmanned aerial vehicle (10) is directed to the designated route. The unmanned aerial vehicle (10) comprises an image capture unit (12) for taking images of agricultural field. In a possible embodiment of the invention, it is preferred to use at least one camera, at least one optical device with high resolution, etc., as said image capture unit (12). The unmanned aerial vehicle (10) transmits the images received by means of the image capture unit (12) to the user terminal (40) in real time. The unmanned aerial vehicle (10) transmits the images received by means of the image capture unit (12) to a network server (20) in real time. In a possible embodiment of the invention, a network server (20) is a system that provides services to other computers on the network. As is well known in the art, the web server (20) can provide a variety of services, such as file sharing, serving web pages, sending e-mails, and database management. Clients can connect to the server to access these services. The unmanned aerial vehicle (10) comprises a communication unit (40) to transmit data to at least one of the user terminal (20) and the network server (11 ). Said communication unit (11) is configured to provide wireless communication. In an alternative embodiment of the invention, said communication unit (11) is configured to provide wired communication.

[0028] The unmanned aerial vehicle takes at least one image of the agricultural field by means of the image capture unit (12). The image capture unit (12) collects images from different parts of the agricultural field at regular intervals. The obtained images are transmitted in real time to at least one of the network server (20) and the user terminal (40) via the communication unit (11 ) using wireless communication protocols such as Wi-Fi, LTE, etc. Real-time transmission allows images to be analyzed without delay. This ensures that the loss of time in the methods used in the present art is reduced.

[0029] A first artificial intelligence model (Al model) is used to process agricultural field images in the user terminal (40). Said first artificial intelligence model is pre-trained to detect sunflower and corn fields. The training data includes images of sunflowers and corn taken from different agricultural fields. Said first artificial intelligence model analyzes color, pattern, and image characteristics in agricultural field images, and detects whether said agricultural field is a sunflower field or corn field.

[0030] At least one of the detected sunflower or corn field is selected. This selection can be made according to the criteria determined by the user (for example, density in a certain region, type of agricultural field to be worked on, etc.). When the unmanned aerial vehicle (10) approaches the selected field up to a defined distance, more detailed images of the agricultural field are obtained. Said images can be of higher resolution and detail. In a possible embodiment of the invention, images captured from altitude of 40 meters in sunflower field and from altitude of 10 meters in corn field are used. The images obtained by means of the image capture unit (12) can be transmitted from the user interface to the network server (20) in real time. If the selected field is sunflower field, the images taken of the sunflower field are applied to a second artificial intelligence model to check the genetic purity of the sunflowers at the user terminal (40). Said second artificial intelligence model was trained on images of flowered sunflowers and non-flowered sunflowers. The second artificial intelligence model receives the sunflower field images captured by the unmanned aerial vehicle (10) as input and detects the flowering status of the sunflowers in these images. In order to determine the flowering status of the sunflowers in the image, the color, pattern, etc. of the sunflower are determined. For this, a detailed analysis is carried out using computer vision techniques such as pixel density, color tones, and pattern recognition. The flowering status of sunflowers is an indication of maturity. In fact, by monitoring the blooming and flowering processes, the maturation process of sunflowers is monitored. The flowering status of sunflowers is crucial in terms of maintaining genetic purity and disease detection. Accurate detection of the flowering process minimizes the risks of hybridization and ensures that genetic integrity is maintained. Furthermore, color changes in sunflowers can indicate problems such as disease or nutrient deficiencies. Therefore, color analysis also plays a critical role for disease detection. The detected flowering status is processed via the user terminal (40) and a report output is generated to be presented to the user. Said report output and visuals are transmitted to the network server (20) via the communication unit.

[0031] If the selected field is corn field, the user terminal (40) applies the images taken of the corn field to a third artificial intelligence model for tassel detection.

[0032] Said third artificial intelligence model takes as input the corn field field captured by the unmanned aerial vehicle (10) and transmitted to the user terminal (40). The third artificial intelligence model detects and evaluates the corn top tassel density in these images. The third artificial intelligence model analyzes visual data, such as specific patterns, shapes, and color variations in the image, to determine corn top tassel density. The corn top tassel density is a critical indicator for assessing the stages of development of the corn plant and pollen production. Tassel density provides crucial information regarding the health, growth status, and productivity of corn. This analysis process is used to understand the pollen production capacities and fertilization success of corn plants. Determining the density of corn tassels allows agricultural practices to be optimized and necessary interventions to be made in a timely manner. The detected data is processed by the user terminal (40) and a report output is generated to be presented to the user. Said report output and visuals are transmitted to the network server (20) via the communication unit.

[0033] The web server (20) transmits said report outputs to a user interface (a mobile application, website, etc.). In a possible embodiment of the invention, it may be preferred to use a mobile application, a website, etc., which is accessed via a mobile device, as said user interface (30). The user can instantly access data related to agricultural field through the user interface (30).

[0034] In an alternative embodiment of the invention, the images obtained from the image capture unit (12) are transmitted to the network server (20). The network server (20), like the user terminal (40), processes the images and generates the report output. The network server (20) uses a first artificial intelligence model (Al model) to process agricultural field images. Thus, it is possible to determine whether the agricultural field is sunflower field or corn field through the network server (20). The network server (20) generates a route information for directing the unmanned aerial vehicle (10) to the detected agricultural field. Thus, it is ensured that the unmanned aerial vehicle (10) is directed according to said route information. In the event that the network server (20) detects that the unmanned aerial vehicle (10) has approached the detected agricultural field up to the determined distance, it checks the genetic purity of the sunflowers with the second artificial intelligence model if the agricultural field is sunflower field, and detects corn tassels with the third artificial intelligence model if the agricultural field is corn field.

[0035] Thus, the genetic purity of sunflowers is maintained, increasing product quality and productivity. Thus, the developmental stages of corn plants and pollen production are closely monitored, increasing productivity and improving product quality. This system automates the processes of monitoring and analyzing agricultural fields, increasing agricultural productivity and seed quality.

[0036] The method performed by the system, the details of which are described above, comprises the following steps in a main embodiment of the invention:

[0037] - obtaining at least one image of agricultural field from at least one image capture unit (12) provided on said unmanned aerial vehicle (10),

[0038] - transmitting the obtained image to the user terminal (40) in real time, - applying said agricultural field image as input to a first artificial intelligence model that is trained with sunflower field and corn field images in the user terminal (40) and, upon receiving at least one agricultural field image as input, that detects at least one of the sunflower field and corn field in the agricultural field image it has received as input, and detecting at least one of the sunflower field and corn field in the image,

[0039] - selecting at least one of the detected sunflower field and corn field and taking images of said field when it is determined that the unmanned aerial vehicle (10) has approached the selected field up to the defined distance,

[0040] - transmitting the obtained images to the user terminal (40) in real time,

[0041] - if the selected field is a sunflower field, applying said sunflower field image as input to a second artificial intelligence model that is trained with images of flowered sunflowers and sunflowers that have not flowered in the user terminal (40) and, upon receiving at least one sunflower field image as input, that detects the flowering status of the sunflowers in the sunflower field image it has received as input, and detecting the flowering status of the sunflowers in the image,

[0042] - if the selected field is corn field, applying said corn field image as input to a third artificial intelligence model that is trained with corn tassel images in the user terminal (40) and, upon receiving at least one corn top image as input, that detects the information regarding the tassel density in the corn image it has received as input, and detecting the corn top tassel density information in the image,

[0043] - generating a report from the detected data,

[0044] - transmitting report to a network server (20) via a communication unit (11 ).

[0045] In an alternative embodiment of the invention, the method includes the following steps in addition to the following main embodiment:

[0046] -comprising the step of sending a route information from the user terminal (40) to the unmanned aerial vehicle (10), and automatically directing the unmanned aerial vehicle (10) to said route.

[0047] In an alternative embodiment of the invention, the method includes the following steps in addition to the following main embodiment: comprising the step of inputting manual route commands through the user terminal (40), and moving the unmanned aerial vehicle (10) according to said route commands.

[0048] In an alternative embodiment of the invention, the method includes the following steps in addition to the following main embodiment: comprising the step of sending a route information from the network server (20) to the unmanned aerial vehicle (10), and automatically directing the unmanned aerial vehicle (10) to said route.

[0049] In an alternative embodiment of the invention, the method includes the following steps in addition to the following main embodiment: comprising the step of transmitting the images received from the image capture unit via the user terminal in real time to a user interface connected to the network server via the communication unit.

[0050] In an alternative embodiment of the invention, the method includes the following steps in addition to the following main embodiment: comprising the step of sending a route information from the network server (20) to the unmanned aerial vehicle (10), and directing the unmanned aerial vehicle (10) according to said route information via a remote user terminal (40).

[0051] The scope of protection of the invention is specified in the appended claims and cannot be limited to what is described for illustrative purposes in this detailed description. It is clear that a person skilled in the art can produce similar embodiments in the light of what is explained above, without deviating from the main theme of the invention.

[0052] REFERENCE NUMERALS GIVEN IN THE DRAWING

[0053] 10 Unmanned aerial vehicle

[0054] 11 Communication unit

[0055] 12 Image capture unit

[0056] 20 Network server

[0057] 30 User interface

[0058] 40 User terminal

Claims

CLAIMS1. A method for determining sunflower isolation boundaries and detecting corn tassels in order to maintain genetic purity in the production of sunflower, corn and sweet corn plants on agricultural fields, performed by a system comprising an unmanned aerial vehicle (10) and a user terminal for remote control of said unmanned aerial vehicle, characterized in that it comprises the steps of:- obtaining at least one image of agricultural field from at least one image capture unit (12) provided on said unmanned aerial vehicle (10),- transmitting the obtained image to the user terminal (40) in real time,- applying said agricultural field image as input to a first artificial intelligence model that is trained with sunflower field and corn field images in the user terminal (40) and, upon receiving at least one agricultural field image as input, that detects at least one of the sunflower field and corn field in the agricultural field image it has received as input, and detecting at least one of the sunflower field and corn field in the image,- selecting at least one of the detected sunflower field and corn field and taking images of said field when it is determined that the unmanned aerial vehicle (10) has approached the selected field up to the defined distance,- transmitting the obtained images to the user terminal (40) in real time,- if the selected field is a sunflower field, applying said sunflower field image as input to a second artificial intelligence model that is trained with images of flowered sunflowers and sunflowers that have not flowered in the user terminal (40) and, upon receiving at least one sunflower field image as input, that detects the flowering status of the sunflowers in the sunflower field image it has received as input, and detecting the flowering status of the sunflowers in the image,- if the selected field is corn field, applying said corn field image as input to a third artificial intelligence model that is trained with corn tassel images in the user terminal (40) and, upon receiving at least one corn top image as input, that detects the information regarding the tassel density in the corn image it has received as input, and detecting the corn top tassel density information in the image,- generating a report from the detected data- transmitting report to a network server (20) via a communication unit (11)2. A method according to claim 1 , characterized in that it comprises the steps of sending a route information from the user terminal (40) to the unmanned aerial vehicle (10), and automatically directing the unmanned aerial vehicle (10) to said route.

3. A method according to claim 1 , characterized in that it comprises the steps of inputting manual route commands from the user terminal (40), and moving the unmanned aerial (10) vehicle according to said route commands.

4. A method according to claim 1 , characterized in that it comprises the steps of sending a route information from the network server (20) to the unmanned aerial vehicle (10), and automatically directing the unmanned aerial vehicle (10) to said route.

5. A method according to claim 1 , characterized in that it comprises the step of transmitting the images received from the image capture unit via the user terminal in real time to a user interface connected to the network server via the communication unit.

6. A method according to claim 1 , characterized in that it comprises the steps of sending a route information from the network server (20) to the unmanned aerial vehicle (10), and directing the unmanned aerial vehicle (10) according to said route information via a remote user terminal (40).

7. A system comprising an unmanned aerial vehicle (10) configured to perform the method of any one of claim 1 to claim 6 for determining sunflower isolation boundaries and detecting corn tassels in order to maintain genetic purity in the production of sunflower and corn plants on agricultural fields, and a user terminal for remote control of said unmanned aerial vehicle.

8. A system according to claim 7, characterized in that the unmanned aerial vehicle (10) comprises an image capture unit (12) for obtaining at least one image of agricultural field, a communication unit (11 ) for exchanging data, and a network server (20) associated with said communication unit.

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