The present invention relates to the technical field of identification on adulterated meat, and in particular, to a method for identifying raw meat and high-quality fake meat based on a gradual linear array change of a component. The present invention spatially characterizes changing rules of featured components in the meat with the utilization of sensitivities of the visible/near-infrared spectral signals to changes of the components in the meat and the advantage that spectral scanning can acquire optical signals of the samples spatially and consecutively, further constructs the identification model according to differences in components and spectra of a region of interest in the hyperspectral image by taking a derivative for characterizing rates of change of the featured components.

Multispectral imaging and related techniques are provided to detect thermal and non-thermal anomalies at reduced false detection rates. A multispectral imaging system includes an infrared light imaging sensor that captures infrared image data in a first spectral band, of a scene and an ultraviolet light imaging sensor that captures ultraviolet image data in a second spectral band, of the scene. The system also includes a processor that combines the ultraviolet image data and the infrared image data to generate composite image data, determines a ratio of a first radiant intensity in the first spectral band to a second radiant intensity in the second spectral band, from the composite image data, and determines whether the ratio corresponds to a predetermined radiant intensity ratio of a known thermal or electrical anomaly. The processor can detect the thermal or electrical anomaly when the determined ratio corresponds to the predetermined radiant intensity ratio.

The present invention discloses a method for selective crop management in real time. The method comprises steps of: (a) producing a biosensor plant, said biosensor plant comprises a visual biomarker, said biomarker is encoded by at least one modified genetic locus comprising (i) preselected reporter gene allele having a phenotype detectable by a sensor, and (ii) a regulatory region of a preselected gene allele responsive to at least one parameter or condition of said plant or its environment, said regulatory region is operably linked to said reporter gene, such that the expression of said reporter gene phenotype is correlated with the status of said at least one parameter or condition of said biosensor plant or its environment; (b) acquiring image data of a target area comprising a plurality of said biosensor plants via said sensor and processing said data to generate a signal indicative of the phenotypic expression of said reporter gene allele of said biosensor plant; and (c) communicating said signal to an execution unit communicably linked to the sensor, said execution unit is capable of exerting in real time a selective monitoring and/or treatment of said target area or a portion thereof comprising said biosensor plants, said treatment is being responsive to said status of said parameter or condition of the biosensor plant or its environment. The present invention further discloses systems and plants related to the aforementioned method.

Methods and systems estimate crop coefficients of a crop. At least one image sensor system captures a plurality of multispectral images of the crop and image data is derived from the multispectral images. At least one vegetation index of the crop is determined based on image data in at least a first spectral band. The reflectance of the crop monotonically increases and reaches a reflectance of at least 20% for at least one wavelength in the first spectral band. A crop coefficient of the crop is estimated based on the determined at least one vegetation index.

A method, apparatus and computer program product for fusing information, to be performed by a device comprising a processor and a memory device, the method comprising: receiving one or more distance readings related to the environment from a Lidar device emitting light in a predetermined wavelength; receiving an image captured by a multi spectra camera, the multi spectra camera being sensitive at least to visible light and to the predetermined wavelength; identifying within the image points or areas having the predetermined wavelength; identifying one or more objects within the image; identifying correspondence between each of the light points or areas and one of the readings; associating the object with a distance, based on the reading and points or areas within the object; and outputting indication of the object and the distance associated with the at least one object.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for detecting objects located in an area of interest. In accordance with one embodiment, a method is provided comprising: receiving, via an interface provided through a general instance on a cloud environment, imaging data comprising raw images collected on the area of interest; upon receiving the images: activating a central processing unit (CPU) focused instance on the cloud environment and processing, via the image, the raw images to generate an image map of the area of interest; and after generating the image map: activating a graphical processing unit (GPU) focused instance on the cloud environment and performing object detection, via the image, on a region within the image map by applying one or more object detection algorithms to the region to identify locations of the objects in the region.

Implementations relate to detecting/replacing transient obstructions from high-elevation digital images. A digital image of a geographic area includes pixels that align spatially with respective geographic units of the geographic area. Analysis of the digital image may uncover obscured pixel(s) that align spatially with geographic unit(s) of the geographic area that are obscured by transient obstruction(s). Domain fingerprint(s) of the obscured geographic unit(s) may be determined across pixels of a corpus of digital images that align spatially with the one or more obscured geographic units. Unobscured pixel(s) of the same/different digital image may be identified that align spatially with unobscured geographic unit(s) of the geographic area. The unobscured geographic unit(s) also may have domain fingerprint(s) that match the domain fingerprint(s) of the obscured geographic unit(s). Replacement pixel data may be calculated based on the unobscured pixels and used to generate a transient-obstruction-free version of the digital image.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for improved image segmentation using hyperspectral imaging. In some implementations, a system obtains image data of a hyperspectral image, the image data comprising image data for each of multiple wavelength bands. The system accesses stored segmentation profile data for a particular object type that indicates a predetermined subset of the wavelength bands designated for segmenting different region types for images of an object of the particular object type. The system segments the image data into multiple regions using the predetermined subset of the wavelength bands specified in the stored segmentation profile data to segment the different region types. The system provides output data indicating the multiple regions and the respective region types of the multiple regions.

A system and a method for removing haze from remote sensing images are disclosed. One or more hazy input images with at least four spectral channels and one or more target images with the at least four spectral channels are generated. The one or more hazy input images correspond to the one or more target images, respectively. A dehazing deep learning model is trained using the one or more hazy input images and the one or more target images. The dehazing deep learning model is provided for haze removal processing.

A method of locating a defect in an e-coat on a surface can include acquiring an image of the surface. A correction coefficient can be applied to the image to form an adjusted image. The correction coefficient can relate pixel values of the image to a calibration value. The adjusted image can be separated into a spectral component which can be modified by a block average determination to create a modified spectral component. The spectral components can be compared with the modified spectral components to form a difference image. The difference image can be dilated and eroded. A region of interest can be identified from an image region using a blob detection. The defect can be classified as a defect type. The defect can be repaired or a coding parameter can be altered based on the defect.