Determination of the valuation of agricultural land and creation of a price index

Abstract

The invention relates to an agricultural land price index platform that provides access to unmanned aerial vehicles in agricultural areas and enables the monitoring of pesticide application, fertilization, and mapping operations. The platform facilitates the collection of land- and crop-related data and brings agricultural producers together through dedicated software. By analyzing the collected agricultural land data, the platform performs price variation analyses and supports the determination of agricultural land values.

Description

[0001] DETERMINATION OF THE VALUATION OF AGRICULTURAL LAND AND CREATION OF A PRICE INDEX

[0002]

[0003] The invention relates to an agricultural land price index platform that enables access to unmanned aerial vehicles (UAVs) in agrarian areas; monitors pesticide application, fertilization, and mapping activities; facilitates the collection of land and crop data; brings agricultural producers together through dedicated software; and analyzes agricultural land data to perform price variation analyses.

[0004]

[0005] of

[0006] The execution and monitoring of operations such as pesticide application, fertilization, and mapping in agricultural areas require both human labor and mechanical resources and are time-consuming processes. In addition, to ensure sustainable and efficient agricultural production processes, accurate and real-time collection of land information, crop data, yield levels, and agrarian damage information must be transmitted rapidly and reliably to producers. Otherwise, failure to collect and utilize such data during the production process inevitably results in yield losses and unexpected declines in productivity.

[0007] Studies conducted to date indicate that data cannot be collected reliably due to the lack of facilitative practices for farmers. The absence of analytical evaluations of crops cultivated on agricultural lands, the inability to obtain accurate, up-to-date information, and the failure to share results on seasonal production efficiency constitute significant problems. Furthermore, farmers tend to refrain from providing information about their lands and crops. In addition, collecting such data is costly and requires long-term effort. The availability of fast, reliable production-related data plays a critical role in increasing productivity and farmers' incomes.

[0008] In determining agricultural land valuation and creating a price index, it is necessary to obtain information directly from farmers. However, obtaining such information is highly time-consuming and costly.Although various proposals and applications have been developed in the prior art regarding agricultural land price index platforms, these solutions remain insufficient.

[0009] Accordingly, some patent applications related to this purpose are discussed below.

[0010] Patent document US2022172467 discloses a distributed cellular system that provides integrated farming services to small-scale farmers. The system uses low-cost mini drones and mini agbots to help farmers manage their farms, increase crop yields, and reduce costs for fertilization, irrigation, and equipment. It is also stated that the system provides yield forecasts for various crops and supplies insurance companies with information regarding crops affected by natural disasters such as hailstorms, floods, and droughts. However, this invention does not include a method for agricultural land valuation or for creating an agricultural land price index.

[0011] Patent document AU2021106696 describes a drone-based technology that successfully detects plant diseases at early stages by using deep learning methods to identify stressors affecting plant health. Multispectral cameras mounted on drones capture reflected light across selected regions of the electromagnetic spectrum using specialized filters, enabling the detection of disease symptoms and distinguishing stressed plants from healthy ones using spectral signatures. This allows farmers to apply fungicides efficiently before diseases cause crop damage. Although this technology contributes to plant pathology research and big data analysis, it does not include information related to the determination of agricultural land prices.

[0012] Patent document WO2022114454 relates to an agricultural UAV designed to spray pesticides or fertilizers. The UAV features a storage tank mounted on its body and a telescopic support system that adjusts the spraying nozzle to an optimal height, thereby minimizing waste and preventing environmental damage. Although this invention improves spraying efficiency, it does not provide a method for determining agricultural land value or creating a price index.

[0013] Therefore, due to the limitations of existing solutions and unmet needs in the technical field, further development is necessary.Objective of the Invention:

[0014] The primary objective of the invention is to establish a web-based and mobile application platform, powered by proprietary software, that brings together UAV operators and agricultural producers, enabling the monitoring of pesticide application, fertilization, and mapping activities.

[0015] Another objective of the invention is to provide access to UAVs operating in the agricultural sector via secure payment systems and to collect data, such as land characteristics, crop information, yield levels, and agricultural damage data, from these UAVs.

[0016] Another objective of the invention is to analyze agricultural land data and perform price variation analyses in conjunction with external factors such as economic indicators.

[0017] Another objective of the invention is to ensure that data flow through the web and mobile application is periodic and continuously updated rather than collected on a one-time basis, due to the regular execution of pesticide application, fertilization, and mapping activities.

[0018] Another objective of the invention is to create a web-based platform that reduces farmers' costs by calculating agricultural land price indices through computations performed after data are collected from farmers and stored in a large data pool.

[0019] Brief Description of the Figures:

[0020] FIGURE 1: The system diagram of the agricultural land price index platform subject to the invention.

[0021] Reference Numerals:

[0022] Server

[0023] Database

[0024] InterfaceDetailed Description of the Invention:

[0025] The invention relates to an agricultural land price index platform that enables access to UAVs in agricultural areas, monitors pesticide application, fertilization, and mapping activities, collects land and crop data, brings agricultural producers together through software, and analyzes agricultural land data to perform price variation analyses.

[0026] The agricultural land price index platform comprises a server (1), a database (2), and an interface (3).

[0027] In the initial stage of determining the agricultural land price index, the interface (3) provided in the web-based and mobile application enables access to the platform that connects UAV operators with agricultural producers. Through secure payment systems, access is provided to UAVs operating in agricultural areas, and agricultural data are collected from these UAVs. Such data include land information, crop data, yield levels, and agricultural damage information.

[0028] The collected data are stored in the database (2), which serves as a large data pool and is dynamically updated through continuous data input. Accordingly, the server (1) continuously processes the data. The data are periodically processed on the server (1 ) together with UAV location data, producer information, and insurance data. As a result of these processes, agricultural land data are analyzed using machine learning techniques in conjunction with external factors, such as economic indicators, and price variation analyses are performed. Based on these analyses, future values of agricultural lands subject to market monitoring can be estimated, and their responses to external factors can be measured.

[0029] Factors Affecting the Value of Agricultural Land:

[0030] Agricultural Factors:

[0031] Soil quality, soil structure, soil productivity, land use capability, geological structure (lithology), stoniness, salinity, desertification potential, erosion level, slope, aspect, irrigation status, water quality, groundwater level, drainage conditions, crop pattern (crop rotation), organic farming practices, land consolidation status, and protected area status.Locational Factors:

[0032] Proximity to settlement areas, farm centers, highways, railways, transmission lines, water sources, urban fringe areas, food industry facilities, other industrial zones, protected areas, mining sites, disaster-prone areas (sinkholes), pollution sources, fault lines, pasture and meadow areas, and forest areas.

[0033] Environmental and Social Factors:

[0034] Air pollution, water pollution, precipitation, temperature, rural population, income level, agricultural subsidies, land rental prices, and land sales frequency.

[0035] Physical Factors:

[0036] Land size, geometric shape of the land, number of frontages, construction status, infrastructure availability, full ownership status, shared ownership status, and existence of easement rights.

[0037] The valuation of agricultural land and the creation of an agricultural land price index fundamentally involve calculations performed on the server (1 ) after data obtained from farmers are collected and stored in the database (2). The web-based interface (3) provides services to farmers by reducing data collection costs.

[0038] Since operations such as pesticide application, fertilization, and sowing are conducted regularly each season through the agricultural land price index platform, the data flow is not one-time but periodic and continuously updated. Furthermore, by providing low-cost services and reducing other production costs, farmers are encouraged to voluntarily enter data through the web or mobile application interface (3), thereby simplifying and accelerating operational processes.

Claims

CLAIMS1. An agricultural land price index platform that performs price variation analysis in agricultural areas, characterized in that it comprises:- at least one server (1) configured to periodically perform, by means of machine learning, analysis of agricultural land data together with the location data of unmanned aerial vehicles, agricultural producer information, and insurance data, and to estimate future values of agricultural lands and conduct price variation analyses;- at least one database (2) which periodically records location data of unmanned aerial vehicles, data related to pesticide application, fertilization, and mapping operations, agricultural producer information, and insurance information, and which continuously collects land data, crop data, yield data, and agricultural damage data; and- at least one interface (3) enabling monitoring of pesticide application, fertilization, and mapping operations, displaying data received from unmanned aerial vehicles, and allowing agricultural producers to enter agricultural, locational, environmental, social, and physical factors into the agricultural land price index platform.

2. The agricultural land price index platform according to Claim 1 , characterized in that the interface (3) is accessible via a website and a mobile application.

3. A method for creating an agricultural land price index platform, characterized in that it comprises the steps of:- continuously collecting, in the database (2), data entered by agricultural producers through the interface (3) and data obtained from unmanned aerial vehicles;- processing, by means of machine learning, the data continuously entered by agricultural producers and continuously obtained from unmanned aerial vehicles by the server (1);- performing machine learning-based analyses on the server (1) after processing thedata entered by agricultural producers and obtained from unmanned aerial vehicles; and- estimating, by the server (1 ), a future agricultural land price index based on the results of the analyses and measuring the responses of the price index to external factors.

4. The method for creating an agricultural land price index platform according to Claim 3, characterized in that, in the step of continuously collecting data entered by agricultural producers through the interface (3) and data obtained from unmanned aerial vehicles into the database (2), the data entered by agricultural producers comprise pesticide application, fertilization, agricultural factors, locational factors, environmental and social factors, and physical factors, and the data obtained from unmanned aerial vehicles comprise land data, crop data, yield data, and agricultural damage data.

5. The method for creating an agricultural land price index platform according to Claim 3 or 4, characterized in that, in the step of continuously collecting data entered by agricultural producers through the interface (3) and data obtained from unmanned aerial vehicles into the database (2), the agricultural factors entered comprise parameters including soil quality, soil structure, soil productivity, land use capability, geological condition, stoniness, salinity, desertification potential, erosion level, slope, aspect, irrigation status, water quality, groundwater level, drainage condition, crop pattern, organic farming presence, land consolidation status, and protected area status.

6. The method for creating an agricultural land price index platform according to Claim 3 or 4, characterized in that, in the step of continuously collecting data entered by agricultural producers through the interface (3) and data obtained from unmanned aerial vehicles into the database (2), the locational factors entered comprise parameters including proximity to settlement areas, proximity to farm centers, proximity to highways, proximity to railways, proximity to transmission lines, proximity to water sources, proximity to urban fringe areas, proximity to food industry facilities, proximity to other industrial zones, proximity to protected areas, proximityto mining sites, proximity to disaster-prone areas, proximity to pollution sources, proximity to fault lines, proximity to pasture and meadow areas, and proximity to forest areas.

7. The method for creating an agricultural land price index platform according to Claim 3 or 4, characterized in that, in the step of continuously collecting data entered by agricultural producers through the interface (3) and data obtained from unmanned aerial vehicles into the database (2), the environmental and social factors entered comprise parameters including air pollution, water pollution, precipitation, temperature, rural population, income level, agricultural subsidies, land rental value, and land sales frequency.

8. The method for creating an agricultural land price index platform according to Claim 3 or 4, characterized in that, in the step of continuously collecting data entered by agricultural producers through the interface (3) and data obtained from unmanned aerial vehicles into the database (2), the physical factors entered comprise parameters including land size, geometric shape of the land, number of frontages, construction status on the land, infrastructure availability on the land, full ownership status, shared ownership status, and existence of easement rights.

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