A method and apparatus for determining a reflectance, of at least a portion of a target object, in at least one selected wavelength range of electromagnetic (EM) radiation are disclosed. The method comprises, for each selected wavelength range, providing a digital image including at least one target object and a plurality of reference objects, each reference object having respective non-identical predetermined reflectance characteristics, with a digital camera arrangement that provides output image data that comprises digital numbers that are responsive to radiation, in only a selected wavelength range, incident at a sensing plane of the digital camera arrangement. A relationship between a first set of the digital numbers is determined and a first set of the respective predetermined reflectance characteristics of the reference objects. Responsive to the relationship, a further set of digital numbers is transformed to allocate a value of reflectance for each of the digital numbers in the further set. For at least a portion of the target object, a corresponding first group of allocated values of reflectance is determined and responsive to the first group of allocated values, determining a reflectance of the portion of the target object.

A crop yield prediction method and system. The method includes: obtaining a test normalized difference vegetation index and test meteorological data of a to-be-tested area; and inputting the test normalized difference vegetation index and the test meteorological data into a hierarchical linear regression model, to obtain a predicted yield of the to-be-tested area; where a method for determining the hierarchical linear regression model is: obtaining a training normalized difference vegetation index of a crop planting area; obtaining training meteorological data and measured yield data of the crop planting area; constructing a first regression equation and a second regression equation, where dependent variables of the second regression equation are a slope and an intercept of the first regression equation; and inputting the training normalized difference vegetation index and the measured yield data into the first regression equation, and inputting the training meteorological data into the second regression equation.

The present disclosure relates to methods and systems for obtaining image information of an organism including a set of optical data; calculating a growth index based on the set of optical data; and calculating an anticipated harvest time based on the growth index, where the image information includes at least one of: (a) visible image data obtained from an image sensor and non-visible image data obtained from the image sensor, and (b) a set of image data from at least two image capture devices, where the at least two image capture devices capture the set of image data from at least two positions.

A spectral camera control device, being installed, along with a spectral camera provided with a liquid crystal tunable filter, in an aircraft capable of stationary flight. The spectral camera control device causes the spectral camera to capture a spectral image in a snapshot mode each time a transmission wavelength of the liquid crystal tunable filter is switched while the aircraft is in stationary flight, and the spectral camera control device causes a plurality of spectral images to be captured in succession at a same transmission wavelength when an SN ratio of the captured spectral image is less than a predetermined threshold.

An approach to remotely and noninvasively detect and evaluate the response of a plant or plant population to a man-made or natural treatment regime (e.g., herbicide, fungicide or fertilizer treatment) via spectral imaging methods and systems comprising the capture of a plurality of spectral images for a common plant scene, each associated with a selected wavelength region of the electromagnetic spectrum, the formulation of an index function from the spectral information indicative of the plant response over time, and the assessment of mathematical parameters quantifying the time-varying plant response to the treatment regime. The plant response to a treatment regime may be quantified in illustrative embodiments in a fraction of the time previously required by many conventional approaches. Applying varying herbicide dosages to segments of the same plant population enables easy determination of a dose-response curve.

Provided are an image processing apparatus, an image processing method, and an image processing program capable of achieving high accuracy in an index representing vegetation. An image processing apparatus (1) includes a normal map generation unit (12) and a reflection characteristic model generation unit (18). The normal map generation unit (12) obtains a normal vector characteristic based on a polarized image acquired. The reflection characteristic model generation unit (18) estimates a reflection characteristic model based on the normal vector characteristic obtained by the normal map generation unit (12).

A scanning device for use in gathering data relating to crops is described including: at least one camera; and a stroboscopic light source is associated with each at least one camera; the device is arranged to be moved around an area where a crop is grown and includes a means for determining the location of the device in the area; the device being arranged to take images of the crop from the at least one camera in synchrony with the stroboscopic light source as the device moves around the area; and wherein the stroboscopic light source has a light output of greater than 6×10.sup.−5 joules/cm.sup.2 (at 2 feet).

Apparatus include divergence optics removably coupled to receive a probe beam in a first imaging mode to cause the probe beam to diverge before impinging on a first area of a target surface, and to not receive the probe beam in a second imaging mode to cause the probe beam to impinge on a second area of the target surface smaller than the first area, collection optics configured to receive, in response to the probe beam, luminescence light emitted from the first area and spectral light emitted from the second area, and an optical detector coupled to the collection optics, wherein the optical detector includes a luminescence imaging detector portion and a spectral imaging detector portion adjacent to the luminescence imaging detector portion, wherein the luminescence imaging detector portion is configured to receive the luminescence light emitted from the first area and the spectral imaging detector portion is configured to receive the spectral light from the second area.

Crop is routed past a sample window on an agricultural combine harvester. Light it is impinged on the crop from an illumination source and reflected radiation is directed to a sensor. The output of the sensor is indicative of various constituents in the harvested crop. The illumination source is controlled based on the temperature proximate the crop sample.

A system, method, device and computer-readable medium for creating an ensemble model of water quality. The ensemble model is generated by determining a set of optimal component models for spectral regions of a body of water, and combining the optimal models. The optimal models can be based on remote sensing data, including satellite imagery. A K-fold partition approach or a global approach can be used to determine the optimal component models, and the optimal component models can be combined through spectral space partition rules to generate an ensemble model of water quality. The ensemble model not only has improved water quality prediction ability, but also has strong spatial and temporal extensibility. The spatial and temporal extensibility of the ensemble model is fundamentally important and desirable for long-term and large-scale remote sensing monitoring and assessment of water quality.