The Climate Corporation

Jeffrey Gerard

Wei Guan

Pramithus Khadka

Yiqun Li

Marian Farah

In an embodiment, a method comprises determining, in received yield data, one or more passes, each pass including a plurality of observations. For each pass of the one or more passes, one or more discrete derivatives are determined, and based on the one or more discrete derivatives first outlier data is generated. First filtered data is generated by removing the first outlier data from the yield data. Furthermore, for each observation in the yield data, a plurality of nearest neighbor observations is determined, and used to determine a plurality of absolute differences in yield values. Based on the plurality of absolute differences, second outlier data is determined. Second filtered data is generated by removing the second outlier data from the first filtered data. Using a presentation layer of a computer system, a graphical representation of the second filtered data is generated and displayed on the computing system.

Automatically detecting outlier values in harvested data

A system for detecting clouds and cloud shadows is described. In one approach, clouds and cloud shadows within a remote sensing image are detected through a three step process. In the first stage a high-precision low-recall classifier is used to identify cloud seed pixels within the image. In the second stage, a low-precision high-recall classifier is used to identify potential cloud pixels within the image. Additionally, in the second stage, the cloud seed pixels are grown into the potential cloud pixels to identify clusters of pixels which have a high likelihood of representing clouds. In the third stage, a geometric technique is used to determine pixels which likely represent shadows cast by the clouds identified in the second stage. The clouds identified in the second stage and the shadows identified in the third stage are then exported as a cloud mask and shadow mask of the remote sensing image.

Cloud detection on remote sensing imagery

A method for estimating growth stage threshold values for a specific hybrid seed at a specific geo-location using historical growth stage data and observed growth stage data comprises using a server computer system, storing a historical crop growth model of one or more hybrid seeds measured from one or more fields over a particular period of time. The historical crop growth model includes growth stage threshold estimates for one or more hybrid seeds. The server computer system receives, via a network, one or more digital measurement values specifying one or more observed growth stage values for a particular hybrid seed at a particular field over a particular period of time. The server computer system transforms the growth stage thresholds into growth stage duration values for the historical crop data and the observed crop data. The server computer system then generates a posterior distribution of growth stage duration values for the particular hybrid seed using a multivariate distribution of growth stage duration value data, which is comprised of historical and observed growth stage data, a covariate matrix describing correlations between different growth stages, and an error matrix used to represent variations in the multivariate distribution. The server computer system estimates mean duration values and variance values for the different growth stages for the particular hybrid seed and then calculates estimated crop growth threshold values for the particular hybrid seed. The server computer system then sends the estimated crop growth threshold values to one or more external computer systems for the purposes of updating and programming crop management instructions.

Growing community of inventors

Seattle, WA, United States of America

Jeffrey Gerard