Image data is obtained that indicates an extent to which one or more objects reflect, scatter, or absorb light at each of multiple wavelength bands, where the image data was collected while a conveyor belt was moving the object(s). The image data is preprocessed by performing an analysis across frequencies and/or performing an analysis across a representation of a spatial dimension. A set of feature values is generated using the image preprocessed image data. A machine-learning model generates an output using to the feature values. A prediction of an identity of a chemical in the one or more objects or a level of one or more chemicals in the object(s) is generated using the output. Data is output indicating the prediction of the identity of the chemical in the object(s) or the level of the one or more chemicals in at least one of the one or more objects.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for image segmentation and chemical analysis using machine learning. In some implementations, a system obtains a hyperspectral image that includes a representation of an object. The system segments the hyperspectral image to identify regions of a particular type on the object. The system generates a set of feature values derived from image data for different wavelength bands that is located in the hyperspectral image in the identified regions of the particular type. The system generates a prediction of a level of one or more chemicals in the object based on an output produced by a machine learning model in response to the set of feature values being provided as input to the machine learning model. The system provides data indicating the prediction of the level of the one or more chemicals in the object.

Provided are an image analysis method, an image analysis device, a program, and a recording medium capable of more easily eliminating an influence of an illuminance distribution in a case where an object is imaged.

The embodiment of the present invention acquires first image data obtained by imaging an object, which develops color according to an amount of external energy in a case where the external energy is applied, with a first sensitivity, acquires second image data obtained by imaging the object with a second sensitivity different from the first sensitivity, calculates a ratio of an image signal value indicated by the first image data with respect to an image signal value indicated by the second image data, and estimates the amount of the external energy applied to the object, based on a correspondence relationship between the amount of the external energy and the ratio, and a calculation result of the ratio in a calculation step.

Provided are an image analysis method, an image analysis device, a program, and a recording medium capable of more easily eliminating an influence of an illuminance distribution in a case where an object is imaged.

The embodiment of the present invention acquires first image data obtained by imaging an object, which develops color according to an amount of external energy in a case where the external energy is applied, with a first sensitivity, acquires second image data obtained by imaging the object with a second sensitivity different from the first sensitivity, calculates a ratio of an image signal value indicated by the first image data with respect to an image signal value indicated by the second image data, and estimates the amount of the external energy applied to the object, based on a correspondence relationship between the amount of the external energy and the ratio, and a calculation result of the ratio in a calculation step.

An agricultural method includes providing a positive air pressure chamber to prevent outside contaminants from entering the chamber; growing crops in a plurality of cells in the chamber, each cell having multi-grow benches or levels, each cell further having connectors to vertical hoists for vertical movements in the chamber; maintaining pre-set temperature, humidity, carbon dioxide, watering and lighting levels to achieve predetermined plant growth; using motorized transport rails to deliver benches for operations including seeding, harvesting, grow media recovery, and bench wash; dispensing seeds in the cell with a mechanical seeder coupled to the transport rails; growing the crops with computer controlled nutrients, light and air level; and harvesting the crops and delivering the harvested crop at a selected outlet of the chamber.

A method for analyzing 2D material thin film and a system for analyzing 2D material thin film are disclosed. The detection method includes the following steps: capturing sample images of 2D material thin films; measuring the 2D material thin films by a Raman spectrometer; performing a visible light hyperspectral algorithm on the sample images by a processor to generate a plurality of visible light hyperspectral images; performing a training and validation procedure, performing an image feature algorithm on the visible light hyperspectral images, and establishing a thin film prediction model based on a validation; and capturing a thin-film image to be measured by the optical microscope, performing the visible light hyperspectral algorithm, and then generating a distribution result of the thin-film image to be measured according to an analysis of the thin film prediction model.

A method for low visibility driving includes receiving image data from a visible-light camera. The image data includes an image of an area in front of a vehicle. The method includes receiving sensor data from an object-detecting sensor. The object-detecting sensor is configured to detect an object in front of the vehicle. The sensor data includes information about the object in front of the vehicle. The method further includes detecting the object in front of the vehicle using the sensor data received from the object-detecting sensor and determining whether the visible-light camera is unable to detect the object in front of the vehicle that was detected by the object-detecting sensor. The method further includes commanding a display to generate a virtual image using the sensor data to identify the object in front of the vehicle.

A method for low visibility driving includes receiving image data from a visible-light camera. The image data includes an image of an area in front of a vehicle. The method includes receiving sensor data from an object-detecting sensor. The object-detecting sensor is configured to detect an object in front of the vehicle. The sensor data includes information about the object in front of the vehicle. The method further includes detecting the object in front of the vehicle using the sensor data received from the object-detecting sensor and determining whether the visible-light camera is unable to detect the object in front of the vehicle that was detected by the object-detecting sensor. The method further includes commanding a display to generate a virtual image using the sensor data to identify the object in front of the vehicle.

A large-scale forest height remote sensing retrieval method includes: acquiring Ice, Cloud and land Elevation Satellite (ICESAT-2) tree height data, Landsat data, Shuttle Radar Topography Mission (SRTM) data, Worldclim data, forest type data and ecological zoning data within a target zone, and preprocessing the data; carrying out georeferencing on the processed data to generate a first data set; calculating spectral features, terrain features and climatic factor features of an image, and combining the calculated features with the ecological zoning data and the forest type data to obtain a second data set; extracting eigenvalues of a same geographical location from the second data set, and combining the extracted eigenvalues with the tree height data to generate training data; constructing a random forest model covering a large zone as an ecological zoning tree height retrieval model, and dividing the obtained training data into a training sample and a verification sample.

A method and system for detecting an aflatoxin on a grain, seed or nut which includes sorting a plurality of the grain seeds in single file, capturing a plurality of shortwave infrared images of each seed, comparing the wavelengths from the captured image with the wavelengths indicative of an aflatoxin presence at a predetermined concentration, and ejecting from a group of the seeds those seeds that have an aflatoxin concentration greater than the predetermined concentration as indicated by the wavelengths from the captured images.