AI Case Study

Slantrange aims at measuring crop populations and detecting weeds with machine vision

Slantrage has partnered with Bayer Crop Science to enhance plant breeding using remote sensing hardware systems, computer vision and artificial intelligence analytics. Based on drone-based imagery, geo-referenced data sources, such as soil characterisation, weather time-series, and satellite data the company claims that its solution can aid farmers collect 10 times more data about their fields in one third of the time.

Industry

Basic Materials

Agriculture

Project Overview

"Such was the case with Slantrange, a San Diego-based startup, which has developed a machine vision system to measure crop populations and detect weeds.

The company’s plant counting algorithm was initially developed for the Midwest growing region. This algorithm didn’t perform well when first tested in a South African field, which had lower planting densities and sandier (more reflective) soils.
However, overnight the Slantrange team re-trained their algorithm with the new data. The updated version of their software was deployed back in South Africa a mere two days from the issue being first reported. Slantrange recently announced a major partnership with Bayer Crop Science to aid in plant breeding." (Forbes)

"Aerial remote sensing using drones has emerged as a powerful and efficient technology to gain valuable new insights into crop performance in both research and commercial settings. The agreement provides funding to deploy and evaluate SLANTRANGE’s proprietary airborne sensor and analytics technology on three key crops within Bayer’s Crop Efficiency Research Program during the 2017 North American growing season. The work will be focused on collecting and analyzing data on various crop phenotypes as a means of assessing genotype and/or treatment performance. The data products will include basic crop metrics such as stress conditions and biomass as well as advanced custom analytics leveraging SLANTRANGE’s multispectral signature processing and machine vision techniques.

The results will be used to evaluate how high-resolution drone-based imagery and advanced data analytics can be used in conjunction with other geo-referenced data sources, such as soil characterization, weather time-series, and satellite data, to gain new agronomic insights and provide more customized and sustainable agronomic recommendations for improving yields." (prweb)

Slantrage has "architected a different approach specifically to address the needs of agricultural applications that generate large volumes of data.

That includes two important changes:

* Processing starts on-board the sensor, during the flight so by the time you land, much of the processing is already complete.
* We’ve developed a new approach to creating orthomosaics which requires only 20% image overlap and is so efficient, it can be completed on-site within minutes." (blog.slantrange)"

Reported Results

Slantrage claims that its solution can provide speed and efficiency and eliminate low network speeds and upload times as well as enable farmers to collect the 10 times more data in one third of the time.

Technology

remote sensing hardware systems, computer vision and artificial intelligence analytics

Function

Operations

Field Services

Background

"Some have criticized AI as being too rigid for agriculture environments, indicating that there is just too much variability. To some extent this is true, however with advances in computing power AI algorithms can be quickly retrained with additional data." (Forbes)

Benefits

Data

drone-based imagery, geo-referenced data sources, such as soil characterization, weather time-series, and satellite data