CLIMATE LLC

Systems and methods are provided for use in mapping tillage in fields based in remote data. One example computer-implemented method includes accessing, by a computing device, an image of one or more fields, where the image includes multiple pixels and where each of the pixels includes a value for each of multiple bands. The method also includes deriving, by the computing device, at least one index value the image and generating a map of tillage for the one or more fields using a trained model and the at least one index value for each of the pixels of the image. The method further includes storing the map of tillage for the one or more fields in a memory and causing display of the map of tillage for the one or more fields at an output device.

Systems and methods are provided for use in mapping irrigation in fields based on remote data. One example computer-implemented method includes accessing, by a computing device, at least one image of one or more fields; applying, by the computing device, a trained model to identity at least one irrigation segment in the at least one image; compiling a map of the one or more fields including the at least one identified irrigation segment; and storing, by the computing device, the map of the at least one identified irrigation segment for the one or more fields in a memory; and/or causing display of the map of the at least one identified irrigation segment for the one or more fields at an output device.

A computer system and computer-implemented techniques for determining and presenting improved seeding rate recommendations for planting seeds in a field are provided. In an embodiment, a computer-implemented method includes receiving digital data representing planting parameters including seed type information and planting row width, and retrieving a set of seeding models based upon the planting parameters, where each of the seeding models includes a regression model defining a relationship between plant yield and seeding rate on a specific field. The method also includes generating an empirical mixture model as a composite distribution of the set of seeding models, generating a seeding rate distribution for the planting parameters based upon the empirical mixture model, and calculating a seeding rate recommendation based on the seed rate distribution. The method then also includes planting plant seeds in the specific field consistent with the seeding rate recommendation.

Systems and methods are provided for generating a plurality of data files, by: displaying a field map corresponding to an agricultural field through a graphical user interface, selecting multiple locations on the field map, identifying, for each selected location, a corresponding data file, generating and displaying a geographic region comprising locations in the identified corresponding data files, and updating the graphical user interface to include a data panel corresponding to the geographic region.

Described herein are implements that include a vehicle having a system for sensing or testing soil and/or vegetation as the vehicle traverses a field.

Systems and methods for identifying clouds and cloud shadows in satellite imagery are described herein. In an embodiment, a system receives a plurality of images of agronomic fields produced using one or more frequency bands. The system also receives corresponding data identifying cloud and cloud shadow locations in the images. The system trains. a machine learning system to identify at least cloud locations using the images as inputs and at least data identifying pixels as cloud pixels or non-cloud pixels as outputs. When the system receives one or more particular images of a particular agronomic field produced using the one or more frequency bands, the system uses the one or more particular images as inputs into the machine learning system to identify a plurality of pixels in the one or more particular images as particular cloud locations.

Systems and methods are provided for managing agricultural activities in a field region. In one example, a computer-implemented method includes identifying temperature grids for the field region, and identifying weather stations for the temperature grids. Each weather station is located at a weather station location in the temperature grids. The computer-implemented method also includes computing weight values based on the weather station locations of the weather stations, such that the weather stations that are more proximate to their respective grids have higher weights than weather stations that are less proximate to their respective grids, and receiving temperature readings from the weather stations. The computer-implemented method then includes computing a plurality of weighted temperatures based on the weight values, computing field condition data for the field region based on the weighted temperatures, and determining at least one field activity for the field region based on the field condition data.

Techniques for recommending side-by-side plantings of pairs of seeds include a server computer receiving agricultural data records that represent crop seed data describing seed and yield properties of seeds and first data for agricultural fields where the seeds were planted. The server receives second data for available seeds and automatically calculates a dataset of success probability scores that describe the probability of a successful yield on the target fields. Data is organized as pairs to facilitate comparison of actual plantings to optimized plantings that have a probability of success (POS), in terms of yield lift or increased yield season-over-season, for different yield values. Confidence values are generated and stored in association with the POS values and can be used as a basis of visual output to support planting and/or field management decisions as part of an automated intelligent agricultural decision support system.

Autonomous vehicles with global positioning systems are used for field inspection. A vehicle may be programmed to traverse a field, while using sensors to detect objects in the field, and then capture low-resolution images of the objects. Machine vision techniques are used with the low-resolution images to recognize the objects as crops, non-crop plant material or undefined objects. Location data is used to correlate recognized objects with digitally stored field maps to resolve whether a particular object is in a location at which crop planting is expected or not expected. Depending on whether an object in a low-resolution digital image is recognized as a crop, and whether the object is in an expected geo-location for crops, the vehicle may switch to a second image capture mode, for example, capturing a high-resolution image of the object, and/or execute a disease analysis and/or weed analysis on the images of the objects.

A server computer system receives a dataset of candidate hybrid seeds for planting on one or more target fields, which comprises data regarding one or more hybrid seeds, probability of success values associated with each of the one or more hybrid seeds that describe a probability of a successful yield, and historical agricultural data associated with each of the one or more hybrid seeds further receives one or more properties for one or more target fields, and selects a subset of the one or more hybrid seeds based on the probabilities of success values. The server computer system generates representative yield values for hybrid seeds in the subset based on the historical agricultural data, generates a dataset of risk values for the subset, which describes an amount of risk associated with the representative yield values for each hybrid seed in the subset based on the historical agricultural data associated with each hybrid seed in the subset, and selects a list of target hybrid seeds from the subset for planting on the one or more target fields based on the dataset of risk values, the representative yield values for the subset, and the one or more properties for the one or more target fields. In addition, the server computer system generates a prescription related to planting the list of target hybrid seeds in the one or more target fields for a next period determines a safety stock for the list for the next period with respect to a supply chain based on the prescription, and causes displaying values of the safety stock, thereby causing production of the safety stock.