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  • Cornell Atkinson
  • Animal Science
  • Agriculture
  • Digital Agriculture

Agriculture is a major driver of New York State’s economy. Given this, Cornell’s Nutrient Management Spear Program (NMSP) seeks to develop management tools that help farmers realize more profit through sustainable approaches. Under the leadership of Sunoj Shajahan, formerly a Cornell research associate now faculty at the University of Illinois, and Subha Srinivasagan, Atkinson postdoctoral fellow, NMSP uses precision agriculture technologies to help meet this goal.

Precision agriculture harnesses technology and data science to precisely predict crop productivity and discovers optimal areas where fertilizer is needed — ultimately increasing crop yield. To estimate and predict corn grain and silage yields, Shajahan and Srinivasagan use time-series drone and satellite imagery. “Drones and satellites are remote sensing technologies that have paved the way for precision agriculture,” said Shajahan. “The images captured from these tools give us a birds-eye view of farm fields and data across the growing season, from planting until harvest and from year to year.”

While yield monitors are increasingly used to record yield, not all farms can afford the equipment or effectively manage the data generated. Using images from drones and satellites can be an alternative way of precisely mapping yield and may be used to estimate yields from past years as well. "Satellites enhance precision agriculture by providing real-time visual insights of crop health, weather, and soil conditions from above, and it is freely available,” Srinivasagan said.

“Drones and satellites are remote sensing technologies that have paved the way for precision agriculture.”

Shajahan joined NMSP in 2019. His research tested use of drones at different scales, from small plot to much large commercial corn fields. His research, published in Remote Sensing in 2021 and most recently in Biosystems Engineering in 2023, used multispectral images from drones to predict corn yield and compare the reliability of different machine learning models. The research showed that drone images can predict yield with considerable accuracy. Capturing drone images later in the season, when plants are in the reproductive and more mature growing stages, allows for more accurate estimates than flying drones early in the season when plants are still small.

Given the variability and complexity of the data collected from multiple sources (yield monitors and multiple imagery sources), analyses can be challenging. “Both drone and satellite data are messy in their raw format,” Shajahan explained. “For example, clouds and shadows can get in the way or the time-series imageries might not align well. And, since we are bringing together all of these data, it can be complicated to standardize the various data layers.”

In 2022, Srinivasan also became part of the NMSP team as an Atkinson postdoctoral fellow. Her work within NMSP builds on what Shajahan started and aims to evaluate use of drone and satellite imagery to conduct on-farm research using a newly developed single-strip spatial research protocol. Srinivasan co-presented on the work on November 29 at the 2023 Northeast Certified Crop Adviser and Field Crop Dealer Meeting held in Syracuse, New York. In her presentation to crop consultants, Srinvasagan shared updates on the development of a web-based tool for implementing and analyzing single-strip trials on farms, using the Single-strip Spatial Evaluation Approach or SSEA.

“Incorporating a web-based SSEA tool into their toolkit, farmers and agricultural consultants can harness a powerful decision-support system,” Srinivasan said. “This tool will enable them to independently evaluate treatment effects and facilitates informed management decisions.”

Collaborating closely with Rahul Goel, a graduate student in Electrical and Computer Engineering at Cornell, Srinivasan and her colleagues are in the process of developing this user-friendly resource. “Its availability holds the potential to inspire greater adoption of on-farm experimentation among farmers,” she said.

The precision ag projects of NMSP attract students from a diversity of majors, including computer science, info science, biometry and statistics and data science, agricultural sciences, animal science and engineering. Shajahan especially enjoys working with them as part of his role within academia.

“Our core focus is on nurturing students to be critical thinkers and training them with advanced agricultural technologies,” said Shajahan, who joined the University of Illinois this past fall. Along with the student interaction, he also appreciates the chance to foster collaboration with other faculty and to deliver practical solutions that meet stakeholder’s needs, he added.

The most rewarding part for both Shajahan and Srinivasan is the partnerships NMSP builds with farmers on field-based research. Merging remote sensing imagery from farms with data on weather, topography and landforms, the researchers work to build a streamlined process to implement machine-learning applications and research tools that farmers might one day use to gather insights into crop management for their fields.

Their shared vision is to develop a protocol or application where farmers can choose a field, and the data from all sources are automatically retrieved and wrangled to a suitable format to train a machine-learning model in the back end and evaluate data from on-farm experiments. This will enable farmers to make informed, data-driven decisions on their farms and manage resources—adapting to weather extremes, increasing profits and ensuring sustainability.

“I take great pride in serving the agricultural community,” Srinivasan said. “I’m dedicated to generating impactful research and tools that equip farmers with scientifically substantiated insights for their decision-making processes.

“Not every profession affords the opportunity to witness the immediate and tangible impact of one's work,” she continued. “NMSP's collaborative model, focused on close engagement with farmers to address their specific needs, continually reminds me of the meaningful contribution I make to the community. This sense of purpose and collaboration fuels my drive to do even more for the agricultural sector.”

Caroline Stamm ’24 is an animal science major and student writer for the Cornell CALS Department of Animal Science.

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