The present invention relates to systems and methods for monitoring agricultural products. In particular, the present invention relates to monitoring fruit production, plant growth, and plant vitality. According to embodiments of the invention, a plant analysis system is configured determine a spectral signature of a plant based on spectral data, and plant color based on photographic data. The spectral signatures and plant color are associated with assembled point cloud data. Morphological data of the plant can be generated based on the assembled point cloud data. A record of the plant can be created that associates the plant with the spectral signature, plant color, spectral data, assembled point cloud data, and morphological data, and stored in a library.

Fertilizer application is challenged by inadequate constraints on key factors that determine how much to apply, and when to apply. Systems and methods for are disclosed that can make use of prior information (e.g. from field trials), time series data from a radiation measuring device, and episodic but spatially extensive satellite data, to constrain these uncertainties.

The present invention is directed to a self consistent system for generation and adaptive implementation of overflying multi sensor measurements and derivation of actionable aggregants pertinent to determination of status and proactive management models of distributed resource. The system includes at least one set of calibrated overflying multisensor detectors arranged for detecting signals from electromagnetic radiation redirected by a plurality of underlying structures having a combination of features having variable scale lengths.

The present invention relates to systems and methods for monitoring agricultural products. In particular, the present invention relates to monitoring fruit production, plant growth, and plant vitality.

The present disclosure provides a method and system for identifying plant species based on hyperspectral data, wherein the method includes: performing atmospheric radiation correction for the hyperspectral data of plants to be identified adopting Linear Regression method, to obtain corrected hyperspectral data, wherein, the hyperspectral data of the plants to be identified are collected by a hyperspectral ground object spectrometer provided within an unmanned aerial vehicle (UAV); performing external parameter orthogonalisation (EPO) processing for the corrected hyperspectral data; performing first order differential processing for the EPO processed hyperspectral data, to obtain hyperspectral data highlighting absorption peak information; performing discrete wavelet transformation processing for the hyperspectral data highlighting the absorption peak information, to obtain wavelet coefficients corresponding to the plants to be identified.

A system comprising: at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to: receive, as input, spectral image data representing spectral reflectance associated with at least one plant, calculate a first and second chlorophyll fluorescence indices in respective first and second wavelength bands, based, at least in part, on said spectral reflectance, and derive a quantum yield value with respect to said at least one plant, by: (i) dividing said first chlorophyll fluorescence index in a sum of said first and second chlorophyll fluorescence indices, and (ii) multiplying (i) by a vegetation index value associated with said at least one plant.

An apparatus for remote detection of plant growth dynamics is described. The apparatus includes an excitation LED (light emitting diode) module, a detection module and a controller module coupled to the excitation LED module and the detection module. The excitation LED module includes at least one LED. Each LED is configured to emit an excitation light in response to an excitation control signal. The excitation light has an emitted light spectrum. The detection module includes a photodetector configured to detect an initial chlorophyll a fluorescence (ChlF) light and an excited ChlF light from a plant species. The photodetector is further configured to convert the detected initial ChlF light into an initial detection electrical signal and the detected excited ChlF light into an excited detection electrical signal. The excited ChlF light is emitted from the plant species in response to receiving the excitation light. The controller module is configured to provide the excitation control signal to the excitation module, to capture the initial and excited detection electrical signals from the detection module and to determine chlorophyll fluorescence data based, at least in part, on the initial and excited detection electrical signals. The excitation LED module and the detection module are configured to be positioned remotely from the plant species. The chlorophyll fluorescence data represents a growth characteristic of the plant species.

A signal processing apparatus, method, and program that enable growth conditions of vegetation to be easily confirmed are disclosed. An NDVI relative value calculation provides a relative value to an average of a vegetation index indicative of growth conditions of grass, on the basis of a sensing image. An image indicative of the growth conditions of an inspection object can be displayed on the basis of the relative value. Further, an NDVI average calculation section calculates an NDVI average obtained by averaging normalized difference vegetation indexes NDVI indicative of the growth conditions of the grass in the entire grass, and a correlation coefficient calculation section calculates a correlation coefficient that matches the NDVI average with a predetermined NDVI specified value. The NDVI relative value calculation applies the correlation coefficient in providing the relative value in each measurement unit in which a measurement is performed on the grass.

A vegetation index calculation method is a method for calculating a vegetation index indicating a vegetation state in an earth's surface using an imaging unit and a flight vehicle, and the vegetation index calculation method includes an illumination spectrum acquisition step of acquiring a spectrum of illumination light with which the earth's surface is irradiated and a reflection spectrum acquisition step of acquiring a reflection spectrum of the earth's surface by the illumination light. The vegetation index calculation method also includes a calibration step of calibrating the reflection spectrum using the illumination spectrum and a step of calculating the vegetation index of the earth's surface based on a calibrated spectrum obtained through the calibration step.

Fertilizer application is challenged by inadequate constraints on key factors that determine how much to apply, and when to apply. Systems and methods for are disclosed that can make use of prior information (e.g. from field trials), time series data from a radiation measuring device, and episodic but spatially extensive satellite data, to constrain these uncertainties.