CLIMATE LLC

A computer system and computer-implemented techniques for determining and presenting improved seeding rate recommendations for sowing hybrid seeds in a field is provided. In an embodiment, determining and presenting seeding rate recommendations for a field may be accomplished using a server computer system that receives over a digital communication network, electronic digital data representing hybrid seed properties, including hybrid seed type, and sowing row width. Using digitally programmed seeding query logic, within the server computer system, receiving digital data representing planting parameters including hybrid seed type information and sowing row width. The seeding query logic then retrieves a set of one or more seeding models from an electronic digital seeding data repository based upon the planting parameters. Each of the seeding model retrieved contain a regression model for the hybrid seed type modeling a relationship between plant yield and seeding rate on a specific field. Using mixture model logic, within the server computer system, generating an empirical mixture model in digital computer memory that represents a composite distribution of the set of one or more seeding models. The mixture model logic then generates an optimal seeding rate distribution dataset in digital computer memory based upon the empirical mixture model, where the optimal seeding rate distribution dataset represents the optimal seeding rate across all measure fields. Using optimal seeding rate recommendation logic, within the server computer system, calculating and presenting on a digital display device an optimal seeding rate recommendation that is based upon the optimal seeding rate distribution dataset.

In an embodiment, an integrated sensor system with modular sensors and wireless connectivity components for monitoring properties of field soil is described. In an embodiment, an integrated sensor system comprises one or more sensors that are configured to determine one or more measures of at least one property of soil. The integrated sensor system also includes one or more processing units that are configured to receive, from the sensors, the measures of at least one property of soil and calculate soil property data based on the measures. The system further includes a transmitter that is configured to receive the soil property data from the processing units, establish a communications connection with at least one computer device, and automatically transmit the soil property data to the at least one computer device via the communications connection. In an embodiment, the communications connection is a wireless connection established between the transmitter and a smart hub or a LoRA-enabled device. In an embodiment, the computer sensors, the processors, and the transmitter are installed inside a portable probe.

A computer system and computer-implemented techniques for determining crop harvest times during a growing season based upon hybrid seed properties, weather conditions, and geo-location of planted fields is provided. In an embodiment, determining crop harvest times for corn fields may be accomplished using a server computer system that receives over a digital communication network, electronic digital data representing hybrid seed properties, including seed type and relative maturity, and weather data for the specific geo-location of the agricultural field. Weather data includes temperature, humidity, and dew point for a specified period of days. Using digitally programmed equilibrium moisture content logic within the computer system to create and store, in computer memory, an equilibrium moisture content time series for the specific geo-location that is based upon weather data. The equilibrium moisture content is used to determine the rate of grain dry down because it gives a basis for how strongly water vapor will dissipate from a kernel to open air. Using digitally programmed grain moisture logic of the computer system to calculate and store in computer memory R6 moisture content for a specific hybrid seed based on a plurality of hybrid seed data. Using digitally programmed grain dry down logic of the computer system to create and store in computer memory a grain dry down time series model for the specific hybrid seed at the specific geo-location that represents the estimated moisture content of the kernel over specified time data points. The grain dry down time series is based upon the equilibrium moisture content time series, the estimated R6 date, the estimated R6 moisture content value, and specific hybrid seed properties. Using digitally programmed harvest recommendation logic of the computer system to determine and display a harvest time recommendation for harvesting crop grown from a specific hybrid seed plant based on the grain dry down time series and the desired moisture level of the grower.

Systems and methods for determining a risk of damage to a crop on an agronomic field are described. In an embodiment, a computer system receives, for each hour of a first day, weather data identifying temperature values and humidity values associated with a geographic location. The computer system determines, for a particular hour of the first day, that a temperature value is within a first range of values and a humidity value is within a second range of values and, in response, identifies the particular hour as a risk hour. The computer system computes, for a second day, a risk value for one or more agronomic fields at the geographic location based, at least in part, on one or more identified risk hours between a day of planting a crop on the one or more agronomic fields and the second day. The computer system determines that the risk value is above a risk value threshold and, in response, determines that the crop on the one or more agronomic fields is at risk of suffering damage from a particular crop damaging factor. The computer system stores data indicating that the crop is at risk of suffering damage from the particular crop damaging factor.

In an embodiment, autonomous vehicles with global positioning systems (GPS) are used for field inspection to reduce fuel and labor costs and improve reliability with increased consistency in field crop inspection. A vehicle may be programmed to traverse a field while using sensors to detect objects and operating in a first image capture mode, for example, capturing low-resolution images of objects in the field, typically crops. Under program control, machine vision techniques are used with the low-resolution images to recognize crops, non-crop plant material or undefined objects. Under program control, 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. Under program control, 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 cease traversing temporarily and switch to a second image capture mode, for example, capturing a high-resolution image of the object, for use in disease analysis or classification, weed analysis or classification, alert notifications or other messages, or other processing. In this manner, a field may be rapidly traversed and imaged using coarse-level, rapid techniques that require lower processing resources, storage or memory, while automatically switching to execute special processing only when necessary to resolve unexpected objects or to perform operations such as disease classification that benefit from high-resolution images and more intensive use of processing resources, storage or memory.

Methods are provided for generating a prescription map for the application of crop inputs. In one method, the user draws a boundary on a map within a user interface and the system identifies relevant soil data and generates a soil map overlay and legend for changing the application prescription for various soils and soil conditions. In another method, the user instead drives a field boundary which is recorded on a planter monitor using a global positioning receiver, and the system generates a soil map and legend for changing the application prescription.

In an embodiment, computer processes are programmed to determine planting plans based on planting characteristics data received for an agricultural field and a plurality of management zone delineation options defined for the agricultural field. Each of the planting plans specifies different planting recommendations for the agricultural field. A first graphical representation of the planting plans, and an interactive object is generated and displayed on a computing device. The interactive object is configured to receive input specifying a relative importance ratio between the planting plans for determining a new planting plan for the agricultural field. A particular relative importance ratio is received via the interactive object, and is used to determine the new planting plan, which specifies new planting recommendations for the agricultural field. Information about the new planting plan is displayed on the computer device.

In one embodiment, a computer-implemented method includes receiving digital field data from an agricultural field representing one or more parameters of the field, soil, or crops in the field; retrieving historical data for the same field from one or more field databases; training and/or applying machine learning models to the field data and the historical data to derive representations of causality of one or more agronomic processes pertaining to the field; receiving user input specifying an anomaly to address via treatment, application or experiment; automatically adjusting the treatment, application or experiment to create a modified treatment, application or experiment that is most likely to generate result data that is usable to train machine learning models in an optimal manner.

A method for precisely applying chemicals targeted by digital maps developed from remotely sensed data, including: obtaining EOS data through a growing season of a crop growing in a field; processing the EOS data to reflectance values; removing error-inducing effects of atmospheric alteration from the processed EOS data; calculating from the processed EOS data a crop performance index that indicates one or more poor performing areas of the field; generating one or more maps of the crop performance index to allow a user to determine whether each of the one or more poor performing areas of the field are due to biological pests instead of topographic or soil constraints in discrete locations of the field; guiding the user to the one or more poor performing areas of the field using the one or more maps to allow the user to scout the one or more poor performing areas of the field to confirm and identify the biological pests; and providing guidance for a chemical application at the one or more poor performing areas that were confirmed as having the biological pests. Other embodiments are provided.

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.