The present disclosure relates to an unmanned aerial vehicle, a communication method, and a program capable of more accurately identifying an identification target.

A communication unit receives identifier information regarding an identifier corresponding to context information of flight, and a control unit extracts feature information by using the identifier information from sensor data acquired by a sensor mounted on an unmanned aerial vehicle. Further, the communication unit transmits the extracted feature information to a server. The technology according to the present disclosure is applicable to a drone.

Calculation of NDVI values with reduced errors caused by variations of sun-light reflection-vs-incidence angles is disclosed. One example of a device includes a data processor that retrieves an image captured by a camera and analyzes the retrieved image to calculate NDVI values indicating activity levels of a plant. The data processor selects plural sampling points from among constituent pixels of the retrieved image, calculates a sampling-point two-dimensional distribution approximation model formula that approximates a two-dimensional distribution of NDVI values and sun-light reflection-vs-incidence angles at the selected sampling points, generates, on the basis of the calculated sampling-point two-dimensional distribution approximation model formula, a corrected NDVI value calculation formula for calculating corrected NDVI values obtained by reducing variations of the NDVI values caused by variations of the sun-light reflection-vs-incidence angles, and calculates the corrected NDVI values according to the generated corrected NDVI value calculation formula.

A method for verifying regenerative management practices in agricultural parcels includes: determining a regenerative carbon footprint value for a parcel that comprises a difference of a regenerative carbon footprint and a baseline carbon footprint, where the baseline carbon footprint is derived by calculating greenhouse gas emissions based on simulating crops under current management practices, and where the regenerative carbon footprint is derived by calculating greenhouse gas emissions based on simulating crops under regenerative management practices corresponding to a plan proposed by a grower; approving and publishing carbon credits according to the plan; for key dates corresponding to each of the regenerative management practices, processing remotely sensed images against corresponding crop curves to determine compliance/noncompliance indicators corresponding to the key dates; and storing the compliance/noncompliance indicators database, and determining at a verification date compliance with the plan.

A method for verifying regenerative management practices in agricultural parcels includes: determining a regenerative carbon footprint value for a parcel that comprises a difference of a regenerative carbon footprint and a baseline carbon footprint, where the baseline carbon footprint is derived by calculating greenhouse gas emissions based on simulating crops under current management practices, and where the regenerative carbon footprint is derived by calculating greenhouse gas emissions based on simulating crops under regenerative management practices corresponding to a plan proposed by a grower; approving and publishing carbon credits according to the plan; for key dates corresponding to each of the regenerative management practices, processing remotely sensed images against corresponding crop curves to determine compliance/noncompliance indicators corresponding to the key dates; and storing the compliance/noncompliance indicators database, and determining at a verification date compliance with the plan.

The present invention provides a drone (unmanned aerial vehicle) capable of photographing a base part of the stem and a side of the leaf of the field crops for evaluating their growth status. A camera is positioned on the unmanned aerial vehicle such that the camera's field of view is directed backward with respect to the direction of the unmanned aerial vehicle. The camera captures an image of the crop temporarily knocked down by the downdraft created by the rotor of the drone, which exposes the base part of the stem and the side of the leaf to the sky. Optionally, the depression angle of the camera is automatically adjusted depending on the flight speed, wind force, and wind direction. Optionally, the camera body is automatically rotated to be directed to backward when the drone changes flying directions.

The present invention provides a drone (unmanned aerial vehicle) capable of photographing a base part of the stem and a side of the leaf of the field crops for evaluating their growth status. A camera is positioned on the unmanned aerial vehicle such that the camera's field of view is directed backward with respect to the direction of the unmanned aerial vehicle. The camera captures an image of the crop temporarily knocked down by the downdraft created by the rotor of the drone, which exposes the base part of the stem and the side of the leaf to the sky. Optionally, the depression angle of the camera is automatically adjusted depending on the flight speed, wind force, and wind direction. Optionally, the camera body is automatically rotated to be directed to backward when the drone changes flying directions.

Described herein are systems and methods for identifying, tracking, evaluating, and targeting objects, such as plants, crops, weeds, pests, or surface irregularities. The methods described herein may include identifying an object on a surface, categorizing the object, identifying a type of plant, locating a point on the surface corresponding to a feature of the object, determining a size of the object and/or evaluating a condition of the object. Such methods may be implemented in various crop management techniques, such as autonomous weed eradication, pest management, crop management, or soil management.

Described herein are systems and methods for identifying, tracking, evaluating, and targeting objects, such as plants, crops, weeds, pests, or surface irregularities. The methods described herein may include identifying an object on a surface, categorizing the object, identifying a type of plant, locating a point on the surface corresponding to a feature of the object, determining a size of the object and/or evaluating a condition of the object. Such methods may be implemented in various crop management techniques, such as autonomous weed eradication, pest management, crop management, or soil management.

Provided herein are devices, systems, and methods for sunlight delivery, shoot positioning, canopy division, positioning fruit into distinct zones, managing fruit maturity and quality, rain, wind, and hail protection, and reducing canopy management and harvest labor of one or more plants on a trellis comprising one or more panels and a standoff, wherein the panels divide the growth of plant shoots on the trellis, thereby modifying growth or development of the plants. In some embodiments, the panels collect light energy and direct the collected light energy to the plants, thereby modifying growth or development of the plants and their producing of fruit.

An approach to the prediction of soil density and subsoil crop growth may be provided. The approach may include subsoil sensor which can monitor changes in soil pressure and moisture conditions. The sensor data can be sent to a computer module which can process the data using a machine learning model predicting the soil density around a subsoil crop and the yield of the subsoil crop. A soil maintenance plan can be generated from the soil density prediction and/or the crop yield prediction. The soil maintenance plan can be sent to soil management robots, which can execute the soil maintenance plan.