An example computer-implemented method includes receiving a plurality of agricultural data records including yield properties of products grown in fields and raw field features of the fields. The method also includes transforming the raw field features into distinct feature classes that characterize key features affecting yield of the one or more products, and generating, using data from the plurality of agricultural data records and the distinct feature classes, genomic-by-environmental relationships between one or more products, yield properties of the one or more products, and field features associated with the one or more products. Further, the method includes generating, based at least in part on the genomic-by-environmental relationships, predicted yield performance for a set of products associated with one or more target environments, generating product recommendations for the one or more target environments based on the predicted yield performance for the set of products, and providing one or more instructions configured to cause display of the product recommendations.

An example computer-implemented method includes receiving a plurality of agricultural data records including yield properties of products grown in fields and raw field features of the fields. The method also includes transforming the raw field features into distinct feature classes that characterize key features affecting yield of the one or more products, and generating, using data from the plurality of agricultural data records and the distinct feature classes, genomic-by-environmental relationships between one or more products, yield properties of the one or more products, and field features associated with the one or more products. Further, the method includes generating, based at least in part on the genomic-by-environmental relationships, predicted yield performance for a set of products associated with one or more target environments, generating product recommendations for the one or more target environments based on the predicted yield performance for the set of products, and providing one or more instructions configured to cause display of the product recommendations.

A system for proximity-based analysis of multiple entities includes a first communication device associated with a first entity and an additional communication device associated with an additional entity, wherein the first communication device and the additional communication device are communicatively couplable. The system includes one or more processors communicatively coupled to at least one of the first communication device or the at least an additional communication device. The one or more processors are configured to: identify a spatial relationship between the first entity and the additional entity based on one or more signals from the first communication device or the additional communication device, identify an operation unit defined by an association between the first entity and the additional entity based on the spatial relationship between the first entity and the at least the additional entity, and report one or more characteristics of the operation unit.

A system for proximity-based analysis of multiple entities includes a first communication device associated with a first entity and an additional communication device associated with an additional entity, wherein the first communication device and the additional communication device are communicatively couplable. The system includes one or more processors communicatively coupled to at least one of the first communication device or the at least an additional communication device. The one or more processors are configured to: identify a spatial relationship between the first entity and the additional entity based on one or more signals from the first communication device or the additional communication device, identify an operation unit defined by an association between the first entity and the additional entity based on the spatial relationship between the first entity and the at least the additional entity, and report one or more characteristics of the operation unit.

A method and apparatus for determining a reflectance, of at least a portion of a target object, in at least one selected wavelength range of electromagnetic (EM) radiation are disclosed. The method comprises, for each selected wavelength range, providing a digital image including at least one target object and a plurality of reference objects, each reference object having respective non-identical predetermined reflectance characteristics, with a digital camera arrangement that provides output image data that comprises digital numbers that are responsive to radiation, in only a selected wavelength range, incident at a sensing plane of the digital camera arrangement. A relationship between a first set of the digital numbers is determined and a first set of the respective predetermined reflectance characteristics of the reference objects. Responsive to the relationship, a further set of digital numbers is transformed to allocate a value of reflectance for each of the digital numbers in the further set. For at least a portion of the target object, a corresponding first group of allocated values of reflectance is determined and responsive to the first group of allocated values, determining a reflectance of the portion of the target object.

The present disclosure provides systems, methods and apparatuses for inducing a desired biological response in an organism through the use of one or more repetitive signals from one or a series of LED lights designed to emit the signal with multiple pulsed components. Each component of the signal contains a one or more light color spectrum or wavelength that is within 50 nm of the peak absorption of a photon receptor of the organism corresponding to the desired biological response. Each component has a repetitive ON duration with an OFF duration and an intensity where the relationship between the ON duration and OFF duration of the first component and the second component induces the desired response in the organism through the stimulation or excitation of a molecule associated with a photoreceptor and the reset of the molecule.

Described herein are mobile sensing units for capturing raw data corresponding to certain characteristics of plants and their growing environment. Also described are computer devices and related methods for collecting user inputs, generating information relating to the plants and/or growing environment based on the raw data and user inputs, and displaying same.

A work management system for managing work to be performed on a work subject. The work management system includes multiple work robots, a storage unit, a generator, and an instruction unit. The work robots each include movement means capable of moving to any location. The storage unit stores target information on a target state of the work subject and current state information on a current state of the work subject. The generator generates work procedure information indicating a work procedure to be performed by the work robots so that the work subject is brought close to the target state, on the basis of the target information and the current state information. The work procedure information includes work instruction information for instructing the work robots to perform one or more types of work to be performed on the work subject.

A work management system for managing work to be performed on a work subject. The work management system includes multiple work robots, a storage unit, a generator, and an instruction unit. The work robots each include movement means capable of moving to any location. The storage unit stores target information on a target state of the work subject and current state information on a current state of the work subject. The generator generates work procedure information indicating a work procedure to be performed by the work robots so that the work subject is brought close to the target state, on the basis of the target information and the current state information. The work procedure information includes work instruction information for instructing the work robots to perform one or more types of work to be performed on the work subject.

A crop yield prediction method and system. The method includes: obtaining a test normalized difference vegetation index and test meteorological data of a to-be-tested area; and inputting the test normalized difference vegetation index and the test meteorological data into a hierarchical linear regression model, to obtain a predicted yield of the to-be-tested area; where a method for determining the hierarchical linear regression model is: obtaining a training normalized difference vegetation index of a crop planting area; obtaining training meteorological data and measured yield data of the crop planting area; constructing a first regression equation and a second regression equation, where dependent variables of the second regression equation are a slope and an intercept of the first regression equation; and inputting the training normalized difference vegetation index and the measured yield data into the first regression equation, and inputting the training meteorological data into the second regression equation.