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.

What is provided is a method and system for more precisely, accurately, and reliably detecting aquatic species, namely infestations of invasive aquatic plants. The system and methods disclosed herein allow for a more effective determination of a treatment plan to reduce any potential negative impact on the aquatic ecology and to minimize any unnecessary human exposure to toxic chemicals.

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 sensor element is used to collect environment information on a surface of the earth or a surface layer of the earth by being scattered in a target region where the environment information is collected. At least one of reflection properties, transmission properties, absorption properties, or luminescence properties with respective to an electromagnetic wave with a specific wavelength, or light emitting properties changes in accordance with an environment.

A light source section configured to couple a plurality of laser beams having different wavelengths and emit measuring light; an illuminating section configured to illuminate a measurement target at a predetermined angle; a light receiving section configured to receive reflected measuring light from the measurement target; and a controlling section configured to compute a reflectance at each of the wavelengths, based on a light receiving result. The light source section includes: a first and a second light source configured to emit each laser beams having different wavelengths; and a dichroic mirror disposed in optical axes of the laser beams intersected, configured to combine the laser beams. The light receiving section includes: a first, a second and a third light receiving unit configured to receive the reflected measuring light from different distance. The controlling section is configured to select which of results from each light receiving unit to use.

A method of determining the nitrogen status of an area of land includes determining a critical nitrogen concentration for aboveground vegetation of plants in the area of land based on a dry weight biomass of entire plants and determining an actual nitrogen concentration for the aboveground vegetation of the plants. A critical nitrogen concentration for the entire plants is determined based on the dry weight biomass of the entire plants. The actual nitrogen concentration for the aboveground vegetation, the critical nitrogen concentration for the aboveground vegetation, the critical nitrogen concentration for the entire plants and the dry weight biomass of the entire plants are combined to form the nitrogen status for the area of land.

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.

A soil analysis system that provides a field deployable device that is configured to remotely measure in situ soil suction through correlation with relative humidity at the soil surface.

Systems, and methods for controlling a modular system for improved real-time yield monitoring and sensor fusion of crops in an orchard are disclosed. According to some embodiments of the invention, a modular system for improved real-time yield monitoring and sensor fusion may include a collection vehicle, a modular processing unit, a volume measurement module, a three-dimensional point-cloud scanning module, an inertial navigation system, and a post-processing server. As the collection vehicle travels through an orchard, the volume measurement module calculates volume measurements of the windrow, the three-dimensional point-cloud scanning module assembles point-clouds of each plant in the orchard, and the inertial navigation system calculates geodetic positions of the collection vehicle. The modular processing unit may fuse the collected data together and transmit the fused data set to a post-processing server. The post-processing server may process the geodetic position data for errors which may be used for geo-referencing the fused data.

The present invention relates to a method of calculating a Topography Adjusted Vegetation Index (TAVI) based on a band ratio model and a solar altitude angle. The method includes the following steps: obtaining the apparent reflectance data of a remote sensing image through image preprocessing, analyzing the quality of the image and numerical distribution, calculating a Shadow Vegetation Index (SVI), and constructing a TAVI combinational algorithm: $\mathrm{TAVI}=\frac{B_{\mathrm{ir}}}{B_r}+f\left(\right).Math.\frac{1}{B_r},$
calculating an adjustment factor f() with the solar altitude angle, and finally obtaining anti-topographic effect TAVI vegetation information. The TAVI in the present invention is composed of two band ratio submodels RVI and SVI, the denominators of both of which are red band data of a remote sensing image, and the adjustment factor f(), which is calculated by a solar altitude angle as a parameter with a sensor factor applied, has great physical significance. The TAVI calculation method does not need digital elevation model (DEM) data and remote sensing image classification when not depending on ground survey data, and ensures that the interference of the topographic effects with the vegetation information can be effectively eliminated by the TAVI, thereby avoiding the problem of reduced inversion accuracy of ground vegetation information due to different registration accuracy of a remote sensing image and DEM data.