A fish-quality determination system (1) analyzes, through machine learning, a relationship among image data taken of cross-sections of tails of fish, boat data indicating fishing boats that caught the fish, and quality data indicating quality of the fish. When having acquired, from a user device (3), image data of a cross-section of the tail of a fish subject to determination and boat data indicating a fishing boat that caught the fish, the system (1) uses, as an input, the image data of the cross-section of the tail of the fish subject to the determination and the boat data indicating the fishing boat that caught the fish that have been acquired so as to estimate and output quality of the fish subject to the determination on the basis of the analyzed relationship. The output quality of the fish subject to the determination is displayed on the user device (3).

A fish-quality determination system (1) analyzes, through machine learning, a relationship among image data taken of cross-sections of tails of fish, boat data indicating fishing boats that caught the fish, and quality data indicating quality of the fish. When having acquired, from a user device (3), image data of a cross-section of the tail of a fish subject to determination and boat data indicating a fishing boat that caught the fish, the system (1) uses, as an input, the image data of the cross-section of the tail of the fish subject to the determination and the boat data indicating the fishing boat that caught the fish that have been acquired so as to estimate and output quality of the fish subject to the determination on the basis of the analyzed relationship. The output quality of the fish subject to the determination is displayed on the user device (3).

An estimation program according to the application concerned causes a computer to execute an obtaining step and an estimating step. The obtaining step includes obtaining behavior information which indicates the behavior exhibited by a specific fish species having a predetermined physical abnormality. The estimating step includes estimating, based on the behavior information obtained at the obtaining step and based on state information indicating the state of the target fish for processing, behavioral features of the target fish for processing that are attributable to the predetermined physical abnormality seen in the target fish for processing.

A geographic position of an agricultural machine is captured. Agricultural data is received that corresponds to a geographic position. Georeferenced visual indicia are displayed that are indicative of the received agricultural data.

A geographic position of an agricultural machine is captured. Agricultural data is received that corresponds to a geographic position. Georeferenced visual indicia are displayed that are indicative of the received agricultural data.

A geographic position of an agricultural machine is captured. Agricultural data is received that corresponds to a geographic position. Georeferenced visual indicia are displayed that are indicative of the received agricultural data.

Methods, devices, and systems may be utilized for detecting one or more properties of a plant area and generating a map of the plant area indicating at least one property of the plant area. The system comprises an inspection system associated with a transport device, the inspection system including one or more sensors configured to generate data for a plant area including to: capture at least 3D image data and 2D image data; and generate geolocational data. The datacenter is configured to: receive the 3D image data, 2D image data, and geolocational data from the inspection system; correlate the 3D image data, 2D image data, and geolocational data; and analyze the data for the plant area. A dashboard is configured to display a map with icons corresponding to the proper geolocation and image data with the analysis.

Methods, devices, and systems may be utilized for detecting one or more properties of a plant area and generating a map of the plant area indicating at least one property of the plant area. The system comprises an inspection system associated with a transport device, the inspection system including one or more sensors configured to generate data for a plant area including to: capture at least 3D image data and 2D image data; and generate geolocational data. The datacenter is configured to: receive the 3D image data, 2D image data, and geolocational data from the inspection system; correlate the 3D image data, 2D image data, and geolocational data; and analyze the data for the plant area. A dashboard is configured to display a map with icons corresponding to the proper geolocation and image data with the analysis.

A dynamic graph includes a plurality of nodes and edges at a plurality of time steps; each node corresponds to a geographic location in a first area where pest infestation information is available for a subset of locations. Each edge connects two of the nodes which are geographically proximate, has a direction based on wind direction, and has a weight based on relative wind speed. Assign node features based on weather data as well as labels corresponding to pest infestation severity. Train a graph convolutional network on the dynamic graph. Based on predicted future weather conditions for a second area different than the first area, use the trained graph convolutional network to predict, via inductive learning, pest infestation severity for future times for a new set of nodes corresponding to new geographic locations in the second area for which no pest infestation information is available.

An automated method for search includes: receiving search characters and a viewport location on a displayed geographic area corresponding to an area of interest; simultaneously searching a first entry source and a second entry source to obtain corresponding first suggested entries and second suggested entries, where the first suggested entries correspond to geographic locations that are closer to the viewport location; ranking the first suggested entries according to first rules of relevancy, and generating first ranked suggested entries; ranking the second suggested entries according to second rules of relevancy, and generating second ranked suggested entries; combining the first and second ranked suggested entries into a combined set of suggested entries, and ranking the combined set of suggested entries according to combined rules of relevancy, and generating combined ranked suggested entries; and transmitting the combined ranked suggested entries to a user for selection of a desired type ahead entry.