CLIMATE LLC

Systems and methods are provided for directing crop disease treatments to agricultural fields. An example computer-implemented method includes receiving a user input from a user at a communication device associated with an interface indicative of likelihood of a first disease in a plurality of agricultural fields and, in response, determining a likelihood of occurrence of the first disease for ones of the plurality of agricultural fields. The method then also includes generating an interface indicative of the likelihood of occurrence of the first disease in the ones of the plurality of agricultural fields and causing the interface to be displayed at the communication device associated with the user.

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.

Techniques for providing improvements in agricultural science by optimizing irrigation treatment placements for testing are provided, including analyzing a plurality of digital images of a field to determine vegetation density changes in a sector of the field. The techniques proceed by comparing a distribution of pixel characteristics in the digital images for each field sector to determine sectors in which minimal differences are present. Instructions for irrigation placements and testing may then be displayed or modified based on the results of the sector determinations.

Systems and methods for determining a degree or rate of error for treatments at a field are disclosed herein. In some embodiments, a system receives first data corresponding to a first agronomic treatment at a field and second data corresponding to a second agronomic treatment at the field. The system uses the first and second data to determine differences and overlaps between characteristics of the first and second data to quantify an error rate at individual subsets of the field. Based on the quantified error rate, a number of metrics and yield data may be interpreted differently or modified to fit the result of the quantified error rate. The system uses the quantified error rate to predict the effectiveness of treatments, modify the rate of experienced yields at a field, and recommend real-time and subsequent corrective actions to take at the field.

A computer-implemented method for improving crop yield in fields includes accessing data representative of predicted performance of seeds for a field and creating an experiment for the field based on the predicted performance of the seeds. The experiment includes a specific prescription to plant the seeds in the field. The method also includes transmitting the prescription to a planter and/or a harvester associated with the field and collecting, in real time, planting data for the seeds as the seeds are planted in the field and harvesting data for a crop grown in the field from the planted seeds. The method further includes validating that execution of the experiment in the field is consistent with the prescription and generating a recommendation based on a comparison of the collected harvesting data for the crop grown in the field to the predicted performance of the seeds included in the prescription.

A computer-implemented method of prescribing spatially-variable application rates of one or more nutrients for an agricultural field is disclosed. The method comprises calculating, by a processor, an average NDVI* map for a crop across multiple prior crop-growing seasons for discrete pixels of the agricultural field; identifying, by the processor, a target yield for the crop in the agricultural field for an upcoming crop-growing season; calculating a provisional average nutrient application rate of a nutrient based on a replacement amount of the nutrient consumed at harvest for the crop and the target yield for the crop in the agricultural field; determining an average nutrient credit of the crop in the agricultural field for a prior crop growing season of the multiple prior crop-growing seasons; calculating a final average nutrient application rate of the nutrient for the target yield for the crop in the agricultural field based on the average nutrient credit and the provisional average nutrient application rate; computing a linear slope for a spatially-variable nutrient application guide based on the final average nutrient application rate and a median NDVI* value of the average NDVI* map; prescribing spatially-variable nutrient application rates of the nutrient across the agricultural field based on the linear slope to the NDVI* map for each of the discrete pixels.

A computer-implemented method of managing data related to an agricultural process is disclosed. The computer-implemented method comprises causing display of a first interface including a first map of one or more agricultural fields and receiving a selection input, by a user, to the first map designating a boundary of a region of the one or more agricultural fields. The computer-implemented method then also includes, in response to the selection input, performing an analysis of one or more of a plurality of types of farming data specific to the region of the one or more agricultural fields having a plurality of components, and displaying a second interface, overlayed on the first interface of the first map of the one or more agricultural fields. The second interface indicates a first component of the plurality of components of the analysis specific to the region of the one or more agricultural fields.

A connector is provided for use with a communication apparatus to facilitate communication of data, via the communication apparatus, between a vehicle or agricultural implement and a computing device. The connector includes a first portion first portion configured to couple the connector to the vehicle or agricultural implement, and a second portion configured to couple the connector to the communication apparatus. The second portion of the connector includes a substrate having at least one circuit trace defined along a surface of the substrate and configured to conductively transfer electronic signals between the connector and the communication apparatus.

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.

Described herein are systems and methods for capturing images of a field and performing agricultural data analysis of the images. In one embodiment, a computer system for monitoring field operations includes a database for storing agricultural image data including images of at least one stage of crop development that are captured with at least one of an apparatus and a remote sensor moving through a field. The computer includes at least one processing unit that is coupled to the database. The at least one processing unit is configured to execute instructions to analyze the captured images, to determine relevant images that indicate a change in at least one condition of the crop development, and to generate a localized view map layer for viewing the field at the at least one stage of crop development based on at least the relevant captured images.