A method of inspecting plants for contamination includes generating a first series of images of a plant, identifying a region of interest displayed in the first series of images, comparing a color parameter of the region of interest to a color criterion associated with a type of contamination, comparing a morphological parameter of the region of interest to a reference parameter associated with the type of contamination, and upon determining that the color parameter meets the color criterion and that the morphological parameter sufficiently matches the reference parameter, identifying the region of interest as a region of contamination on the plant. The method further includes transmitting an instruction to lift a cutter of a harvester up from a planting bed to avoid harvesting the plant in response to identifying the region of interest as the region of contamination, and generating a second series of images of an additional plant.

A method of inspecting plants for contamination includes generating a first series of images of a plant using a camera mounted to a frame being moved along a planting bed by a harvester, identifying a region of interest displayed in the first series of images as a region of contamination on the plant based on a color criterion and a morphological criterion applied to the region of interest, and transmitting data including an instruction to increase a vertical distance between the plant and a cutter of the harvester to avoid harvesting the plant in response to identifying the region of interest as the region of contamination. The method further includes generating a second series of images of an additional plant as the frame continues to be moved along the planting bed by the harvester while the vertical distance between the plant and the cutter is being increased.

A method for detecting a field navigation line after ridge sealing of crops includes the following steps. A field crop image is acquired. Image color space transformation, image binaryzation, longitudinal integration, neighborhood setting and region integration calculation are sequentially performed on the field crop image to obtain a crop row image. Detections of an initial middle ridge, a left ridge and a right ridge are performed on the crop row image to obtain center lines of the initial middle ridge, left ridge and right ridge. Center lines of a left (right) crop row are established by using an area 1 between the center lines of the left (right) ridge and the initial middle ridge. A center line model of a middle ridge is established by using an area 0 between the center lines of the left and right crop rows, namely a navigation line of a field operation machine.

A method for automatically identifying global solar photovoltaic (PV) panels based on a cloud platform by using remote sensing. Optical images in a study area for a whole specific year are collected based on the cloud platform, and preprocessing is performed to obtain a surface reflectance image. Seven time-series images are derived and constructed based on spectral features of a solar PV panel: a solar PV panel index image, a water index image, a vegetation index image, a difference image between a first shortwave infrared band and a second shortwave infrared band, a difference image between the first shortwave infrared band and a near-infrared band, a blue band image, and a first shortwave infrared band image. Data in the seven time-series images are synthesized and reconstructed to obtain input data required by a model. A remote sensing theoretical model for automatically identifying a solar PV panel is constructed.

Disclosed herein are methods, devices, modules, and systems which may be employed to accurately track, and subsequently target, objects of interest relative to a moving body, such as a vehicle. An object tracking method may be implemented by a detection system with a sensor coupled to the moving body. A targeting system may be used to target objects tracked by the detection system, such as for automated crop cultivation or maintenance. Devices disclosed herein may be configured to locate, identify, and autonomously target a weed with a beam, such as a laser beam, which may burn or irradiate the weed. The methods, devices, modules, and systems may be used for agricultural crop management or for at-home weed control.

Crop loss estimation allows a user to monitor and estimate damage to the crops due to various natural events/factors. State of the art systems used for the crop loss estimation have the disadvantage that they do not convey to the users extent of damage. In addition to this, the existing methods do not take into account the recovery factor of the crops due to multiple factors and end up in overestimating the loss. The disclosure herein generally relates to crop monitoring, and, more particularly, to a method and system for crop loss estimation. In this method, crop loss is assessed based on real-time weather parameters and remote sensing data collected and processed, and crops are classified as being in one of a repairable damage class and a permanent damage class. The system also quantifies the crop loss, which allows the user to understand magnitude of the crop loss.

In embodiments, acquiring sensor data associated with a plant growing in a field, and analyzing the sensor data to extract one or more phenotypic traits associated with the plant from the sensor data. Indexing the one or more phenotypic traits to one or both of an identifier of the plant or a virtual representation of a part of the plant, and determining one or more plant insights based on the one or more phenotypic traits, wherein the one or more plant insights includes information about one or more of a health, a yield, a planting, a growth, a harvest, a management, a performance, and a state of the plant. One or more of the health, yield, planting, growth, harvest, management, performance, and the state of the plant are included in a plant insights report that is generated.

Embodiments relate to a crop harvesting apparatus configured to garner or harvest crops from plants via vacuum suction and sort the garnered crops via a quick-switching gate system. A vacuum source generates the vacuum suction for the apparatus so that crops are garnered (or plucked) from the plant via suction through an end-effector, which are then transferred to a crop sorter by way of tubing that has a smooth inner surface. The crop sorter utilizes a gate system that exploits vacuum suction from the vacuum source and gravity to quickly and effectively sort the garnered crops into a hopper and a rejection bin.

In a computer-implemented method, one or more digital aerial images of a property of a current or potential policyholder may be received. The digital aerial image(s) may be processed to determine one or more features of the property, including one or more features of a tree. A predicted location of roots of the tree is determined based upon the tree feature(s). The property feature(s) is/are analyzed to determine a risk of damage to a structure located on the property, by analyzing at least the predicted location of roots of the tree to determine a risk of damage to a foundation of the structure. Based at least in part on this risk, a risk output is generated that includes an indication of whether action should be taken to mitigate the risk of damage and/or whether insurance coverage should be offered, and/or includes a measure of the risk of damage.

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.