An information processing apparatus includes, when a first determiner determines the amount of food or beverage in a platter being decreased, a second determiner to determine whether food or beverage on the platter was consumed by one person of multiple people, or the food or beverage was moved onto one small plate of small plates, a consumption information generator to generate, when the food or beverage on the platter was consumed by the one person, consumption information in which an amount of food or beverage consumed by the one person, the one person, and the type of the food or beverage are associated with each other, and an estimator to increase, when the food or beverage was moved to one small plate, the amount of food or beverage on the one small plate by an amount of decrease in the food or beverage in the platter.

The application discloses a fruit picking method based on a three-dimensional parameter prediction model for a fruit. The method comprises: performing first-time image acquisition processing on a to-be-picked fruit to obtain a first image; determining a first range; controlling a manipulator to perform first-time moving processing; performing intermittent gas injection treatment to lead to forced vibration of the to-be-picked fruit; performing second-time image acquisition processing many times to obtain a plurality of second images; screening out, by taking the first image as an reference object, two appointed second images deviating from an equilibrium position to the maximum extent; jointly inputting the images into a preset three-dimensional parameter prediction model for the fruit so as to obtain predicted three-dimensional parameters; controlling the manipulator to perform second-time moving processing; and performing cutting processing on a fruit stem position to make the to-be-picked fruit fall onto the manipulator.

A citrus identification method comprises: performing first-time image acquisition processing on a target citrus tree to obtain a first image; inputting the first image into a first citrus fruit identification model to be processed to obtain a first identification result sequence; performing area interception processing on the first image to obtain a citrus fruit area; obtaining roundness integrity numerical values; selecting an appointed roundness integrity numerical value, and acquiring a defect position of an appointed citrus fruit in the first image; determining a first spatial range and a second spatial range; performing both first spray injection treatment and second spray injection treatment; performing second-time image acquisition processing to obtain a second image; inputting the second image into a second citrus fruit identification model to be processed to obtain a second identification result; and generating a citrus fruit identification result.

A crop residue monitoring system may include a harvester, a camera to capture an image of crop residue generated by the harvester, an analytical unit to derive a value for a crop residue parameter of the crop residue based upon an optical analysis of the image and a control unit to adjust a subsequent field operation based upon the value of the crop residue parameter.

The invention provides a computer-implemented method of detecting a liquid level inside a container, the method comprising the steps of: capturing, by a camera of a portable device, a first image of the container; providing the first image to an input layer of a convolutional neural network, CNN; obtaining, from a final layer of the CNN, the liquid level inside the container in the first image; and storing the obtained liquid level, wherein the CNN is configured to identify features of a plurality of volume indicators of the container in the first image and determine the liquid level in the container in the first image based on the identified features.

A method for analysis of yeast includes: receiving a microscopic image of yeast by a cloud server (2901), the microscopic image including a scaling pattern for determining a magnification; determining the magnification by the cloud server based on the scaling pattern (2902); and analyzing, by the cloud server, the microscopic image based on the magnification to obtain an analysis result (2903).

Disclosed is a method for inspecting a food product, the method includes: receiving image data representing the food product captured with an X-ray imaging unit; performing a texture analysis to image data for generating a first set of detections; performing a pattern analysis to at least part of the image data, the pattern analysis performed with a machine-learning component trained to identify objects with predefined pattern, for generating a second set of detections; generating an indication of an outcome of an inspection of the food product in accordance with a combination of the generated first set of detections and the second set of detections. Also disclosed is an apparatus and a computer program product.

The present invention provides a processing apparatus (10) including an acquisition unit (11) acquiring a captured image including a managed object related to a store, a foreign object region detection unit (12) detecting a foreign object region being a region in which a foreign object exists in the managed object included in the captured image, and a warning unit (13) executing warning processing depending on the size of the foreign object region.

A process includes receiving a target quality value, receiving a measured quality value, and sending an inspection control instruction. The inspection control instruction is based at least in part on the target quality value and the measured quality value. The measured quality value is generated by a product inspector configured to inspect a sample before being sorted and may also include data generated by an output product inspector configured to inspect a sample after passing a sorting phase. The target quality value indicates a desired quality value of an output group of samples. The inspection control instruction controls the method of inspection performed by the product inspector. The measured quality values are received by a computing device, which in turn outputs the inspection control instruction to a product inspector.

A method for product compliance includes receiving, at data processing hardware, a planogram defining a representative placement of a product on a display shelf and receiving at least one image from an imaging device having a field of view arranged to capture a top surface of the display shelf. The method also includes determining whether the product is disposed on the display shelf based on the at least one image. When the product is disposed on the display shelf, the method includes determining an actual placement of the product on the display shelf and comparing the actual placement of the product to the representative placement of the product defined by the planogram. The method further includes determining a planogram compliance based on the comparison of the actual placement of the product to the representative placement of the product and communicating the planogram compliance to a network.