Plant Science Initiative @ NC State University

In my role at Oracle, I get to work across many industries on some very interesting problems. One that I have been involved with recently is the collaboration between North Carolina (NC) State University and Oracle with NC State’s Plant Science Initiative.

In particular, we’ve been working with the College of Agriculture and Life Sciences (CALS) to launch a big data project that focuses on sweet potatoes. The goal is to help geneticists, plant scientists, farmers and industry partners in the sweet potato industry to develop better varieties of sweet potatoes, as well as speed up the pace with which research is commercialized. The big question is can we use the power of Big Data, Machine Learning, and Cloud computing to reduce the time it takes to develop and commercialize a new variety of sweet potato crop from 10 years to three or four years?

One of the well-known secrets to driving innovation is scaling and speeding up experimentation cycles. In addition, reducing the friction associated with collaborative research and development can help bring research to market more quickly.

My team is helping the CALS group to develop engagement models that facilitate interdisciplinary collaboration using the Oracle Cloud. Consider geneticists, plant science researchers, farmers, packers, and distributors of sweet potato being able to contribute their data and insights to optimize different aspects of the sweet potato production – sweet potato from the genetic sequence to the dinner plate.

I am extremely excited by the potential impact open collaboration between various stakeholders can mean for the sweet potato and precision agriculture industry.

More details at cals.ncsu.edu

Precision Agriculture needs Scalable Automation

If you have ever looked out an airplane window as it flies over land, chances are that you see some spectacular landscapes, sprawling cities or a quilted patchwork of farms. Over the centuries science, machines and better land management practices have increased agricultural outputs dramatically, allowing farmers to manage and cultivate ever larger swaths of lands. The era of Big Data and Artificial Intelligence is pushing these productivity gains even further with Precision Agriculture. For example, using satellite images to evaluate the health of crops to direct farming decisions or predict the likely yields we can expect during harvest time.

Earlier this year, my team at Oracle (chiefly Venu Mantha, Marta Maclean & Ashok Holla) worked with a large agricultural customer to help them shift towards a more data-driven agricultural approach that would maximize their yields and reduce waste. We explored a variety of technologies, from field sensors streaming measurements over Internet-of-Things (IoT) networks to the geospatial fusion of a variety of historical and real-time data. The idea is to support farmers with contextually relevant data, allowing them to make better decisions. Apart from people and process challenges associated with such a dramatic business transformation for an ancient sector, the major technological obstacle for realizing the potential of precision agriculture is, in fact, scalable automation.

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Let us take a concrete example. A key aspect of the proposal was the use of aerial or satellite imagery to assess the health of the crop. Acquiring satellite or aerial imagery on demand is significantly easier compared to what it was just a few years ago with the growing number of vendors in the market and falling cost of acquisition (e.g. Digital Globe,  Free Data Sources). Now that we can get imagery in high-resolution (i.e. down to level of an individual tree), that is multi-spectral (i.e. color and infrared bands) and covers large expanses of land (i.e. 100 acres or more) the challenge has shifted to a well-recognized one in the world of Big Data – How do you sift through the large volumes of data to extract meaningful and actionable insights quickly?

If one image covers 100 acres and takes a Geographic Information System (GIS) specialist an hour to review manually, handling images for over 100,000 acres would mean a team of 40 GIS specialists working without a break for about 25 hours to go through the full batch of images. Clearly throwing more people at the problem is not going to work. Not only is it slow and error-prone, but finding enough specialists with domain knowledge would be a challenge.

The answer is to automate the image analysis pipelines and distributed computing to parallelize and speed up the analysis. Oracle’s Big Data Spatial & Graph (BDSG) is particularly well suited for partitioning, analyzing and stitching back large image blocks using the map-reduce framework of Hadoop. It understands common GIS and image file formats and gives the developer Java bindings to the OpenCV image processing library as part of its multimedia analytics capabilities. You can either split up a large image (raster or vector) and analyze each chunk in parallel or analyze each image in parallel. You can write your own image processing algorithms or compose one using the fundamental image processing algorithms available in OpenCV.

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The challenge for the customer, however, was coming up with an algorithm that could correct the image misalignment that naturally creeps in during image acquisition or image stitching process. A misaligned image would require a GIS specialist to open and manually adjust the image using tools like ArcGIS from ESRI. Analyzing a misaligned image would lead to incorrect results and can lead to bad decisions.

This is where the BDSG product engineering team (Siva Ravada, Juan Carlos Reyes & Zazhil ha Herena) and I stepped in to design and develop a solution to automate the image alignment and analysis processes. We have a patent application around the solution that can be used in a variety of domains beyond farming – Think of urban planning, defense, law & enforcement, and even traffic reports.

Just the manual alignment of images would take a GIS expert 3-8 mins per image. With our solution, the entire alignment and analysis process takes less than 90 seconds and can handle 100s of images in parallel. Instead of a team of 40 GIS experts working without a break for 25 hours, we can now analyze imagery covering 100,000 acres in about 15 minutes. 

The key lesson here is that although we can access interesting sensors and data sources to inform us and guide Precision Agriculture, successful technological solutions require scalable automation that minimizes the human effort, not add to it. The adoption of these solutions in practice further depends on the maturity of the organization in embracing change.

Want to know more or talk about how the Oracle Big Data & Analytics team can help your business objectives? Connect with me on LinkedIn and follow me on Twitter

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