A threshing state management system includes an image capture unit 80 that captures an image of a threshed material threshed by a threshing apparatus, a state detection neural network 72 that outputs a threshing processing state in the threshing apparatus based on image input data generated based on the captured image from the image capture unit 80, a parameter determination unit 73 that determines a control parameter of the threshing apparatus based on the threshing processing state, and a threshing control unit TU that controls the threshing apparatus based on the control parameter.

A method of producing a model to detect changes in forest cover is disclosed. The method includes obtaining forest-cover classification data of a land area. The land area includes one or more subregions having unchanged forest-cover classifications between a first time and a second time. The method further includes obtaining image data of the subregions at multiple times. For at least one forest-cover classification, the method includes applying a statistical analysis to the image data to determine one or more threshold values representing measurement variations. The method further includes comparing subsequently obtained image data to the one or more threshold values and classifying the one or more subregions as changed or unchanged based on the comparison of subsequently obtained image data to the one or more threshold values.

A system for measuring chlorophyll concentration in a leaf sample includes a leaf-holding illuminator device with a main body containing a power source, a plurality of switchable light sources emitting light at different spectra (e.g., red and white light from a broadband light source), and a cap detachably secured to the main body using one or more fastening means. The leaf sample is interposed between the main body and the cap and held in place during imaging. The system includes a mobile electronic device having a camera configured to capture an image of the leaf illuminated by the plurality of switchable light sources, the mobile electronic device having wireless connectivity to a network and an application contained therein configured to transfer the images to a remote sever or computer via the network for data processing. A final chlorophyll index value is calculated based on the transferred images.

Versions of the disclosure relate to methods of imaging and detecting germinated seeds on a soilless growth medium.

Some implementations herein relate to a graphical user interface (GUI) that facilitates dynamically partitioning agricultural fields into clusters on an individual agricultural field-basis using agricultural features. A map of a geographic area containing a plurality of agricultural fields may be rendered as part of a GUI. The agricultural fields may be partitioned into a first set of clusters based on a first granularity value and agricultural features of individual agricultural fields. The individual agricultural fields may be visually annotated in the GUI to convey the first set of clusters of similar agricultural fields. Upon receipt of a second granularity value different from the first granularity value, the agricultural fields may be partitioned into a second set of clusters of similar agricultural fields. The map of the geographic area may be updated so that individual agricultural fields are visually annotated to convey the second set of clusters.

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.

An online agricultural system manages and optimizes interactions of entities within the system to enable the execution of transaction and the transportation of crop products. The online agricultural system accesses historic and environmental data describing factors that may impact crop product transactions and/or transportation to determine market prices for crop products and crop product transportation. Responsive to receiving a request from an entity, the online agricultural system determines an optimal transaction for the entity, such as a price for selling a crop product, an available crop product for purchase, or a transportation opportunity to transport a crop product.

An object identification and collection method is disclosed. The method includes receiving a pick-up path that identifies a route in which to guide an object-collection system over a target geographical area to pick up objects, determining a current location of the object-collection system relative to the pick-up path, and guiding the object-collection system along the pick-up path over the target geographical area based on the current location. The method further includes capturing images in a direction of movement of the object-collection system along the pick-up path, identifying a target object in the images; tracking movement of the target object through the images, determining that the target object is within range of an object picker assembly on the object-collection system based on the tracked movement of the target object, and instructing the object picker assembly to pick up the target object.

The present invention provides a fruit harvesting, dilution and/or pruning system comprising: (a) a computerized system for mapping an orchard or a map of trees position and their contour in a plantation; (b) a management system for autonomous unmanned aircraft vehicle (UAV) fleet management for harvesting, diluting or pruning fruits; and a method for UAV autonomous harvesting, dilution and/or pruning of an orchard.