Digital agriculture improves data yield, yet increases the need for analytics and algorithms
Digital agriculture makes data collection easier, but analytics are necessary to improve decision-making
While new facilities and technologies have made data collection easier, they have also created a new challenge for agriculture: Once you have the data, what can you actually do with it?
One pass of Purdue’s PhenoRover, a modified sprayer, over 20 acres of farmland generates three terabytes of data—thousands of images and hundreds of thousands of measurements. That data is rich with useful information for research and industry, but extracting the value still requires tedious human labor. Researchers must weed out faulty images or bad measurements and digitally stitch multiple images together to reconstruct the field. Data must also be validated by checking thousands of manual measurements against those collected using newer automated methods. Even a seemingly simple task, such as counting the number of plants in a single row, can take hours as high-resolution photos meet low-throughput methods.
As such, the next frontier for smarter agriculture is developing algorithms that automate these processes, automatically analyzing images and aggregating measurements to produce useful output.
The TERRA project has made promising early steps in this area, writing software that can automatically stitch images, identify rows of plants, categorize them by plot, and visualize comparisons within and between plant types.
The future holds even more sophisticated techniques, says Melba Crawford, associate dean of engineering for research; professor of agronomy, civil engineering, and electrical and computer engineering; and Purdue Chair of Excellence in Earth Observation. Many of these will adapt innovative methods from other fields, such as the deep learning and computer vision approaches used by the tech industry to comb visual data for facial recognition and in self-driving cars.
“We have made excellent progress,” Crawford says. “The most recent results in plant and leaf counting from our electrical engineering group using deep learning have been very promising.”
Once extracted, this phenotype data will merge with the flood of data from the world of plant “omics”—the genes and proteins controlling plant development and function. Marshall Porterfield, a professor of agricultural and biological engineering whose past work includes research on growing plants on the International Space Station, sees a future where these data streams combine to make the vision of digital agriculture a reality.
“Access to the molecular level of what’s happening in the crops and the plants themselves at a systems biology level can allow us to develop targets for selection so that crops perform better in general,” Porterfield says. “It’s like precision medicine, where they hope to someday take a look at your genome and determine which drugs work best for you. Why not look at a crop or field situation, the soil, the nutrients, the sunlight or water availability, and fine-tune a crop variety to match that application?”
And while a hyperspectral image might be far too complex for practical application, software can be written that translates that information into simple recommendations. Eventually, a dashboard can be designed as a kind of digital advisor for farmers, aggregating and repackaging the streams of data collected from their crops into real-time feedback.
“From the data point of view, analytics is key. How do you create particular algorithms that give actionable information to, for example, turn on a fertilizer sprayer?” asks Janice Zdankus, vice president of quality for Hewlett Packard Enterprise. “If you look at the value of taking localized data on a farm level and analyzing it to say when to turn on a nozzle at the right amount at the right time, that’s very valuable to a farmer.”
From the local farm to global food security, the big data harvest holds great promise for the future of agriculture, both supplementing thousands of years of acquired knowledge and revealing new perspectives on farming. In the College of Agriculture, ultramodern facilities and a strong interdisciplinary focus create an environment where these new technological capabilities can be aimed at the largest challenges.
“If we’re going to feed 10 billion people in the next 30 to 40 years, we want to be more efficient in the production of food and make predictions about what’s economically the most sustainable way of producing food, all while having the least impact on the environment,” says Mitch Tuinstra, professor of plant breeding and genetics; Wickersham Chair of Excellence in Agricultural Research; and scientific director of the Institute for Plant Sciences. “That’s what digital agriculture is: using big data so we can make better decisions that impact agricultural sustainability and productivity.”