The present disclosure provides a system for monitoring unstructured environments. A predetermined path can be determined according to an assignment of geolocations to one or more agronomically anomalous target areas, where the one or more agronomically anomalous target areas are determined according to an analysis of a plurality of first images that automatically identifies a target area that deviates from a determination of an average of the plurality of first images that represents an anomalous place within a predetermined area, where the plurality of first images of the predetermined area are captured by a camera during a flight over the predetermined area. A camera of an unmanned vehicle can capture at least one second image of the one or more agronomically anomalous target areas as the unmanned vehicle travels along the predetermined path.

A computer-implemented method, computer program product and computer system (100) for determining the impact of herbicides on crop plants (11) in an agricultural field (10). The system includes an interface (110) to receive an image (20) with at least one crop plant representing a real world situation in the agricultural field (10) after herbicide application. An image pre-processing module (120) rescales the received image (20) to a rescaled image (20a) matching the size of an input layer of a first fully convolutional neural network (CNN1) referred to as the first CNN. The first CNN is trained to segment the rescaled image (20a) into crop (11) and non-crop (12, 13) portions, and provides a first segmented output (20s1) indicating the crop portions (20c) of the rescaled image with pixels belonging to representations of crop. A second fully convolutional neural network (CNN2), referred to as the second CNN, is trained to segment said crop portions into a second segmented output (20s2) with one or more sub-portions (20n, 20l) with each sub-portion including pixels associated with damaged parts of the crop plant showing a respective damage type (11-1, 11-2). A damage measurement module (130) determines a damage measure (131) for the at least one crop plant for each damage type (11-1, 11-2) based on the respective sub-portions of the second segmented output (20s2) in relation to the crop portion of the first segmented output (20s1).

A harvester selectively harvests edible crowns ready for harvesting. The harvester may include an imaging system for capturing image(s) of the edible crowns and a de-leafing component that removes leaves of the broccoli plant. For example, broccoli plants typically have an abundance of leaves that reside beneath, alongside of, and even above the edible crowns. The leaves may conceal the edible crowns and impact a quality of the image(s). The de-leafing component may be positioned in front of the imaging system, relative to a direction of travel of the harvester, to remove the leaves and isolate or expose the edible crown. Therein, the imaging system may image the edible crowns for use in determining whether the edible crowns are ready for harvesting.

Implementations are directed to assigning corresponding semantic identifiers to a plurality of rows of an agricultural field, generating a local mapping of the agricultural field that includes the plurality of rows of the agricultural field, and subsequently utilizing the local mapping in performance of one or more agricultural operations. In some implementations, the local mapping can be generated based on overhead vision data that captures at least a portion of the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the portion of the agricultural field captured in the overhead vision data. In other implementations, the local mapping can be generated based on driving data generated during an episode of locomotion of a vehicle through the agricultural field. In these implementations, the local mapping can be generated based on GPS data associated with the vehicle traversing through the agricultural field.

Providing an object detection engine, training the object detection engine to identify a weed, training the object detection engine to identify a crop, providing an image from a sensor to the object detection engine, discerning with the object detection engine the weed from the crop, and plotting a path from the weed to a spot spray assembly upon identification of the weed by the object detection engine.

A system and method for detecting changes in an agricultural field uses a time series of target images of the agricultural field in which a vegetation index value is calculated for each target image. A target trend line is calculated from the time series of the vegetation index values. A time series of candidate images of one or more candidate fields having one or more attributes that correspond to one or more attributes of the agricultural field is also acquired in which an expected trend line can be determined from calculated vegetation index values representative of respective candidate images. An alert is generated in response to a deviation of the target trend line from the expected trend line that meets alert criteria.

A system gathers data from a plurality of sources across a wide geographic region, and produces from the gathered information output to a user, which output indicates to the user factors that may influence supply of product to, and/or operation of, a supply chain. Illustrative embodiments are able to determine that data in a previously received dataset has been changed by its corresponding data source, and subsequently update a corresponding data record maintained by the system. Illustrative embodiments train and employ one or more neural networks to identify anomalies in large datasets, and in some embodiments to predict the impact of various factors on crop production.

A crop detection system and method of using the same includes a machine vision system mounted to a mobile vehicle. The machine vision system includes an information capturing device connected to a computer having a processor and memory. The memory includes stored crop and field information. Positioning members are mounted to an extend forward of the mobile structure. The information capturing device includes a camera, a sensor, a transceiver and/or a stereo sensor configuration and is positioned to sense the presence, size, location and orientation of characteristics of a crop.

Systems and methods for scalable comparisons between two pixel maps are provided. In an embodiment, an agricultural intelligence computer system generates pixel maps from non-image data by transforming a plurality of values and location values into pixel values and pixel locations. The non-image data may include data relating to a particular agricultural field, such as nutrient content in the soil, pH values, soil moisture, elevation, temperature, and/or measured crop yields. The agricultural intelligence computer system converts each pixel map into a vector of values. The agricultural intelligence computer system also generates a matrix of metric coefficients where each value in the matrix of metric coefficients is computed using a spatial distance between to pixel locations in one of the pixel maps. Using the vectors of values and the matrix of metric coefficients, the agricultural intelligence computer system generates a difference metric identifying a difference between the two pixel maps. In an embodiment, the difference metric is normalized so that the difference metric is scalable to pixel maps of different sizes. The difference metric may then be used to select particular images that best match a measured yield, identify relationships between field values and measured crop yields, identify and/or select management zones, investigate management practices, and/or strengthen agronomic models of predicted yield.

An agricultural method includes providing a positive air pressure chamber to prevent outside contaminants from entering the chamber; growing crops in a plurality of cells in the chamber, each cell having multi-grow benches or levels, each cell further having connectors to vertical hoists for vertical movements in the chamber; maintaining pre-set temperature, humidity, carbon dioxide, watering and lighting levels to achieve predetermined plant growth; using motorized transport rails to deliver benches for operations including seeding, harvesting, grow media recovery, and bench wash; dispensing seeds in the cell with a mechanical seeder coupled to the transport rails; growing the crops with computer controlled nutrients, light and air level; and harvesting the crops and delivering the harvested crop at a selected outlet of the chamber.