Chickpea is an increasingly important crop in the Montana agricultural system. From 2017 to 2021 the U.S. has planted an average of about 492,000 acres per year with Montana chickpea production accounting for around 44% of the U.S. total (USDA/NASS QuickStats accessed on 2/11/2021). This has led to an increase in breeding efforts for elite varieties adapted to the unique conditions in the Northern Great Plains. Breeding of chickpea often relies on traditional phenotyping techniques that are laborious, qualitative, and subject to variation based on the person collecting the data. Development of precision ag protocols that allow for rapid, quantitative, and repeatable results is needed to make crop development and production more efficient. This will lead to the release of better varieties and also boost production levels by enabling producers to assess the health of chickpea fields and address problems.

This project has multiple goals: first, to directly compare the data and inferences acquired from two multi-spectral cameras, the Parrot Sequoia and the Micasense Altum. These are two very common sensors for field level multispectral imaging, but the cost of each unit drastically differs and as of yet there are not many direct comparisons. Secondly, this project will be used to develop phenotyping methods, in-field assessment protocols, and efficient in-season data collection techniques specific to chickpea’s being grown in the Northern Great Plains of the United States. This project is in its first year and the plant materials consisting of released varieties common to MT and advanced breeding lines were planted on 4/25/2022 outside Conrad, MT at the Western Triangle Agricultural Research Center under Montana State University. Data will be collected using the above-mentioned sensors paired with an Aurelia Aerospace X6 Pro sUAS. As progress is made this project will expand to include: wheat, barley, field pea, lentil, and canola.