Drone Imagery Analysis to Boost Soybean Yield

Purdue University, in collaboration with the North Central Soybean Research Program, is embarking on a groundbreaking initiative known as SOYGEN3 (Science Optimized Yield Gains across ENvironments). Led by Professor Katy Rainey and Professor Keith Cherkauer, this project aims to leverage drone imagery analysis to predict soybean biomass and develop models for selecting high-yielding soybean varieties in the face of climate change. With funding exceeding $900,000 and the involvement of eight universities, including Purdue, this research endeavor holds tremendous promise for the future of soybean production.

Expanding Capabilities:

Building upon their successful endeavors in predicting soybean biomass using drone imagery in Indiana, Rainey and Cherkauer are now expanding their capabilities to incorporate all the public soybean breeding programs in the north-central region of the United States. Breeders from 11 states will contribute drone imagery collected on a panel of 1,200 soybean varieties. Purdue University will process and modify these images to predict biomass, utilizing cutting-edge software called Plot Phenix, which rapidly converts aerial crop photographs into valuable information for plant breeding, crop modeling, and precision agriculture.

Addressing Climate Change Challenges:

The overarching goal of the SOYGEN3 project is to develop methods and models for selecting soybean varieties that thrive in future extreme environments influenced by climate change. The project recognizes that climate change brings about significant alterations in environmental conditions and increases the frequency of extreme weather events. By analyzing drone imagery from diverse locations, the research team can account for variations in heat resistance, water availability, and other factors that impact soybean growth and yield.

Advantages of Multilocational Analysis:

The inclusion of soybean breeders and farmers from different states within the project provides a unique opportunity to observe the effects of varying environments on soybean performance. Locations with distinct climate patterns, such as those in Minnesota and Indiana, require heat-resistant varieties due to temperature differences. Furthermore, even areas with similar average precipitation can experience contrasting conditions in different years. By analyzing imagery from a range of locations, the research team can develop robust models that account for environmental diversity and support the selection of soybean varieties suitable for various climatic scenarios.

The Role of Drone Technology:

Drone technology plays a pivotal role in gathering high-resolution aerial imagery for analysis. The drones used in the project capture imagery in red, green, and blue (RGB), providing essential data for crop modeling and analysis. Purdue University's collaboration with GRYFN, a Purdue-affiliated company, has facilitated the acquisition of a new drone platform equipped with multispectral and thermal cameras. This enhanced imaging capability will yield richer datasets, enabling the team to develop more precise recommendations for soybean breeders and farmers participating in the SOYGEN3 project.

Sustaining Global Soybean Production:

Soybeans play a vital role in ensuring future protein food security, with 95% of global soybean production used as animal feed. To sustain the growing demand for soybeans, researchers must deepen their understanding of how weather and climate influence yield in diverse environments. By incorporating genetic variation and utilizing genomic prediction models, breeders can strategically select soybean varieties with enhanced resilience and productivity. Purdue University's pioneering efforts to combine drone imagery analysis, genetics, and biomass predictions position them at the forefront of soybean research in both the public and private sectors.

The collaborative SOYGEN3 project led by Purdue University represents a significant step forward in harnessing drone imagery analysis to enhance soybean yield and address the challenges posed by climate change. By leveraging advanced technology, incorporating genetic variation, and developing predictive models, researchers.