Abstract: An object is to provide an image accumulation apparatus, a method, and a non-transitory computer-readable medium capable of extracting a characteristic video based on an individual emotion. An image accumulation apparatus according to the present disclosure includes an image acquisition unit, an expression classification unit, and an image accumulation unit. The image acquisition unit acquires image data. The expression classification unit classifies face image data included in the image data into a predetermined emotion. The image acquisition unit accumulates the image data associated with emotion identifiers for identifying the classified emotions so that the image data is distributable to a terminal.

Abstract: Provided is an analysis apparatus or the like capable of appropriately determining emotions of a learner or an examinee in online learning or an online examination. An analysis apparatus includes: an emotion data acquisition unit that acquires emotion data regarding learning of each learner, the emotion data being obtained by performing emotion analysis on face image data of a plurality of learners in online learning; and an analysis data generation unit that aggregates emotion data regarding the plurality of learners, compares the emotion data of the plurality of learners, and generates analysis data in which the emotion data of one or more learners is identified.

Abstract: An analysis apparatus (100) includes: emotion data acquisition means (111) for acquiring emotion data that includes time data, the emotion data being generated based on face image data of participants in an online meeting; meeting data acquisition means (112) for acquiring meeting data including image data that includes time data in the meeting; analysis data generation means (113) for generating analysis data regarding the meeting based on the emotion data; screen generation means (114) for generating a playback screen including a seek bar for specifying a time, the analysis data that corresponds to the specified time, and the image data; and output means (115) for outputting the playback screen.

Abstract: An analysis apparatus comprises at least one memory storing instructions, and at least one processor configured to execute the instructions to acquire emotion data including time data from an emotion data generation apparatus that generates the emotion data from face image data of participants in an online meeting, acquire meeting data regarding the meeting including the time data, generate chapters for the meeting based on the meeting data, generate analysis data regarding the meeting based on the emotion data for each of the chapters, output the generated analysis data.

Abstract: An analysis apparatus comprises at least one memory storing instructions, and at least one processor configured to execute the instructions to comprises at least one memory storing instructions, and at least one processor configured to execute the instructions to acquire emotion data from an emotion data generation apparatus that generates emotion data from face image data of a meeting participant in an online meeting, generate analysis data for the meeting on the basis of the emotion data, acquire meeting data including attribute data of the meeting, store message data in which a pattern of a message to be presented to a user is associated with the meeting data, select the message on the basis of the analysis data and the message data, and store an analysis result including the selected message in a storage unit in an outputtable manner.

Abstract: An analysis apparatus acquires individual emotion data for each participant generated on the basis of face image data of the participants in an online meeting during the meeting. The analysis apparatus generates, for each participant, analysis data indicating a degree of emotion in the online meeting on the basis of the individual emotion data. The analysis apparatus stores each piece of the analysis data for each participant in association with corresponding color tone information. The analysis apparatus generates, as a display image indicating a state of the online meeting, an image in which element figures represented by the color tone information associated with the analysis data are disposed for each of a plurality of the participants who have participated in the online meeting. The analysis apparatus outputs the generated display image.

Abstract: An analysis apparatus sequentially acquires emotion data generated for each first period based on face image data of participants during an online meeting. The analysis apparatus generates analysis data indicating one analysis value regarding an evaluation for a second period in an ongoing online meeting based on emotion data including at least the latest data. The analysis apparatus sequentially outputs the generated analysis data.

Abstract: A field management apparatus 10 is provided with a learning model generation unit 11 that generates a learning model 15, to learn feature amounts of the image of the phenomenon that results from the fault in the field equipment, an image acquisition unit 12 that acquires an aerial image of a target region, an image specification unit 13 that applies the aerial image to the learning model 15, and specifies an image of the phenomenon that results from the fault in the field equipment in the aerial image, and a fault location specification unit 14 that specifies a fault location of the field equipment in the target region, based on the image of the phenomenon that results from the fault in the field equipment.

Abstract: A vegetation index calculation apparatus (10) is provided with a learning model generation unit (11) that generates a learning model, by using an image of a crop targeted for calculation of a vegetation index and an image of plants other than the crop to learn a feature amount of the image of the crop, an image acquisition unit (12) that acquires an aerial image of a target region where the crop is being grown, a specification unit (13) that applies the aerial image acquired by the image acquisition unit (12) to the learning model generated by the learning model generation unit (11), and specifies the image of the crop in the aerial image acquired by the image acquisition unit (12), and a vegetation index calculation unit (14) that calculates the vegetation index of the crop, using the image of the crop specified by the specification unit (13).

Abstract: A vegetation index calculation apparatus (10) is provided with a specification unit (11) that collates height distribution data indicating a distribution of the height of plants that exist in a target region with predicted height data of a crop targeted for calculation of a vegetation index, and specifies a region where the crop exists within the target region, and a vegetation index calculation unit (12) that calculates the vegetation index of the crop that exists in the region specified by the specification unit (11).

Abstract: Provided are a soil estimation device, a soil estimation method and a program that are capable of improving the accuracy of estimation of a state in soil without increasing the number of sensors that detect the state in the soil. The soil estimation device 100 is provided with an estimated model generation unit 10. The estimated model generation unit 10 generates an estimated model based on at least one among geographical information that specifies a geographical feature of a field of interest and soil distribution information that specifies a soil distribution in the field. The estimated model is a model for estimating, from a measured value that indicates the state in the soil at one location within the field, the state in the soil at a location other than the one location.

Abstract: A vegetation index calculation apparatus (10) is provided with a learning model generation unit (11) that generates a learning model, by using an image of a crop targeted for calculation of a vegetation index and an image of plants other than the crop to learn a feature amount of the image of the crop, an image acquisition unit (12) that acquires an aerial image of a target region where the crop is being grown, a specification unit (13) that applies the aerial image acquired by the image acquisition unit (12) to the learning model generated by the learning model generation unit (11), and specifies the image of the crop in the aerial image acquired by the image acquisition unit (12), and a vegetation index calculation unit (14) that calculates the vegetation index of the crop, using the image of the crop specified by the specification unit (13).

Abstract: A vegetation index calculation apparatus (10) is provided with a learning model generation unit (11) that generates a learning model, by using an image of a crop targeted for calculation of a vegetation index and an image of plants other than the crop to learn a feature amount of the image of the crop, an image acquisition unit (12) that acquires an aerial image of a target region where the crop is being grown, a specification unit (13) that applies the aerial image acquired by the image acquisition unit (12) to the learning model generated by the learning model generation unit (11), and specifies the image of the crop in the aerial image acquired by the image acquisition unit (12), and a vegetation index calculation unit (14) that calculates the vegetation index of the crop, using the image of the crop specified by the specification unit (13).

Abstract: A field management apparatus 10 is provided with a learning model generation unit 11 that generates a learning model 15, to learn feature amounts of the image of the phenomenon that results from the fault in the field equipment, an image acquisition unit 12 that acquires an aerial image of a target region, an image specification unit 13 that applies the aerial image to the learning model 15, and specifies an image of the phenomenon that results from the fault in the field equipment in the aerial image, and a fault location specification unit 14 that specifies a fault location of the field equipment in the target region, based on the image of the phenomenon that results from the fault in the field equipment.

Abstract: Disclosed is a control device that enables a terminal device to display an accurate field area. The control device receives position information obtained by position information obtainment means carried by a worker of the field; stores the received position information to storage means; and generates a field area identified by a field identifier on a map including the field based on the stored position information.

Abstract: A vegetation index calculation apparatus (10) is provided with a specification unit (11) that collates height distribution data indicating a distribution of the height of plants that exist in a target region with predicted height data of a crop targeted for calculation of a vegetation index, and specifies a region where the crop exists within the target region, and a vegetation index calculation unit (12) that calculates the vegetation index of the crop that exists in the region specified by the specification unit (11).

Abstract: Provided are a soil estimation device, a soil estimation method and a program that are capable of improving the accuracy of estimation of a state in soil without increasing the number of sensors that detect the state in the soil. The soil estimation device 100 is provided with an estimated model generation unit 10. The estimated model generation unit 10 generates an estimated model based on at least one among geographical information that specifies a geographical feature of a field of interest and soil distribution information that specifies a soil distribution in the field. The estimated model is a model for estimating, from a measured value that indicates the state in the soil at one location within the field, the state in the soil at a location other than the one location.

Abstract: In order to be capable of efficiently controlling harvesting processing of crops in conjunction with the harvesting season and harvesting volume, an information processing device has: an information generation unit that generates first harvesting schedule information for crops, including harvesting season information and harvesting volume information, on the basis of field information including at least one out of weather information, soil information, crop information, crop growth information, and agribusiness history information for a field; and a plan generation unit that generates a harvesting processing plan including at least one plan out of a harvesting plan, transportation plan, and processing plan for the field, on the basis of the first harvesting schedule information.

Abstract: A broadcast receiving apparatus makes it possible to select a desired television program quickly by channel-surfing. The broadcast receiving apparatus outputs information of the received broadcast content as television program information of a channel for which the broadcast content is received, includes an operation detector that detects a channel that selects operation, an operation speed calculator that calculates a channel selecting operation speed from the detected channel selecting operation, a list item count determiner that determines, according to the calculated channel selecting operation speed, the number of channels for which the television program information is displayed, a channel determiner that determines channels to be listed so that the television program information is displayed for the determined number of channels, and a display that outputs the television program information of the determined channels to be listed.

Abstract: Provided is a broadcast receiving apparatus which makes it possible to select a desired television program quickly by channel-surfing.