University of Minnesota

Earth Scout Gbc

David Mulla

Vassilios Morellas

Nikolaos Papanikolopoulos

Dimitris Zermas

Mike E Bazakos

Troy Schmidtke

Dipesh Karki

Michael Immer

Volkan Isler

Pratap Rajkumar Tokekar

Christopher Burg

Oleg Myslov

Ian V MacRae

Mitra Sangroula

Robert L Koch

Subigya Shakya

Zachary Peter Dragan Marston

Peder Lindberg

Michael Bazakos

Daniel Kaiser

Joshua Vander Hook

A method for continuous real time monitoring of a crop growth including the use of a telescoping sensor mount capable of systematically extending a sensor above a crop growth canopy. The method includes determining a value associated with soil saturation, soil nitrogen mineralization, and growing degree units. These values are made available to the user real time. The telescoping sensor mount of the invention includes a foldaway tripod support system and is further capable of being powered with solar energy.

Plant growth platform

Systems, techniques, and devices for detecting plant biometrics, for example, plants in a crop field. An imaging device of an unmanned vehicle may be used to generate a plurality of images of the plants, and the plurality of images may be used to generate a 3D model of the plants. The 3D model may define locations and orientations of leaves and stems of plants. The 3D model may be used to determine at least one biometric parameter of at least one plant in the crop. Such detection of plant biometrics may facilitate the automation of crop monitoring and treatment.

Crop models and biometrics

Crop biometrics detection

A method of determining whether to treat soybeans for soybean aphids, the method includes collecting at least one image of a soybean canopy using one or more remote sensing instruments and processing the image into spectral reflectance data and selecting from the spectral reflectance data optimal spectral wavelength bands. The selected reflectance data is classified into one of a plurality of classification groupings using a machine learned classification model. To treat or not treat the soybean canopy for aphids is determined based on the classification of the reflectance data into one of the class groupings.

Remote-sensing-based detection of soybean aphid induced stress in soybean

Pixel color values representing an image of a portion of a field are received where each pixel color value has a respective position within the image. A processor identifies groups of the received pixel color values as possibly representing a Nitrogen-deficient plant leaf. For each group of pixel color values, the processor converts the pixel color values into feature values that describe a shape and the processor uses the feature values describing the shape to determine whether the group of pixel color values represents a Nitrogen-deficient leaf of a plant. The processor stores in memory an indication that the portion of the field is deficient in Nitrogen based on the groups of pixel color values determined to represent a respective Nitrogen-deficient leaf.

Automated detection of nitrogen deficiency in crop

A system includes an unmanned aerial vehicle and an unmanned surface vehicle. The unmanned aerial vehicle has a memory storing a plurality of collection points and at least one sensor for collecting sensor data from each of the collection points. The unmanned surface vehicle is capable of moving to a plurality of locations. The unmanned aerial vehicle travels through the air between at least two collection points stored in the memory and the unmanned aerial vehicle is carried between at least two collection points stored in the memory by the unmanned surface vehicle.

Symbiotic unmanned aerial vehicle and unmanned surface vehicle system

Growing community of inventors

Minneapolis, MN, United States of America

David Mulla