FASTCON: Food, Ag & Supply Chain in Tech Conference

Data-driven decisions are paramount to thriving and growing in today’s business, government, and service environments. In this session, the Grande Cheese Company shares how to leverage vast resources of publicly available data from governments, non-governmental organizations, and commercial sources to augment and enhance understanding; leading to better business decisions. Topics include: - Sources and access methods to publicly available data sets, reliability and biases in data, and opportunities to use public data both tactically and strategically. Data use demonstrations using public data from North/South America, Europe, and Oceania will focus on proper organization, presentation, and visualization of data and methods to source and structure data within to make comparative analysis and glean understanding. A use case in collaboration with the Department of Applied Economics at the University of Minnesota-Twin Cities focusing on agricultural data will be featured.

As a leading supplier in the institutional food service industry, Land O’Lakes provides various processed cheese products via annual competitive contracts. Each bid requires a custom price, set individually per contract, trading off win probabilities (low prices) and profit margins (high prices).

To provide accurate estimates of win probabilities, the Land O’Lakes Data Science team set out to build a classification model, predicting likelihood of winning a bid at a given price, but training such a model is challenging due to limited data and overfitting. In this presentation we will share two innovative techniques in data augmentation and de-overfitting, which enable a classification model reliable enough to optimize prices and maximize expected profit.

In the area of inspection the combination of machine vision and machine learning is changing the way inspection in done in agriculture - globally. However, for some companies it is not clear how to leverage this technology. Even companies with a data scientist team, are looking for specific machine learning models, and practical implementation strategies that drive value for their specific crops and at different places in the supply chain where they can add value or manage risk.

What is the role of corporate Data Scientist in creating a sustainable future? With his background in paleontology, environmental science, and mass extinctions, Kristopher Purens will show how corporate scientists can use geospatial analytics to help companies find sustainable solutions—while also helping the organization become more efficient. Considering the rapid increase in the number of Earth's observation satellites in the past decade, geospatial analysis is entering a new era where big data tools are necessary to analyze and create value from this data. From classic use cases of crop monitoring, new satellites sensor types are empowering organizations to understand the carbon cycle and pollutant emissions, see through clouds, and more.

As the logistics and supply chain industry continues to mature, so does the increased desire to integrate new and exciting technologies. Although it is easy to understand the benefits of leveraging these technologies, individuals tend to overlook the challenges associated with integration and adoption. Here, we present the lifecycle of integrating new dynamic systems into established supply chains and the challenges and rewards associated with them.

While technology can help farmers produce more while consuming less resources, also crucial to sustainability are tried-and-true conservation practices and geographic features leveraged to preserve waterways and prevent erosion.  In this session, we explore a proposal and proof of concept demonstrating how neural networks may be used to detect sustainable practices in agricultural fields.  From annotation to model training and image segmentation, learn how Mask RCNN was used to find sustainable features in four Iowa counties.

Developing Deep Learning solutions to improve quality and yield of wheat harvester using computer vision technology.