We wanted to use the given data sets to maximize farm efficiency and productivity, however realized making any analysis on a farm’s current state and its future is impossible without up to date and accurate data. So we created a tool that would provide for better local comparison and more frequent data sharing.

What it does

The app logs field data as an agronomist or grower inputs it and gives notifications for warnings or major updates. Example being a diseased crop in a neighboring field.

How we built it

Before we started we sat and talked with Ab inBev agronomists to find out what solutions or information would be most beneficial and feasible to both growers and agronomists. Once we had a direction we sat down with agronomists again for information on specific details and then planned out a platform. We used node.js, mongodb, and express.js for backend database development. We are using swift to create an iOS app for a user interface that takes user input and also used d3js to create a data visualization web interface.

Challenges we ran into

Time. For the most part we knew how to implement our solutions, however they were time intensive and it became more and more apparent that we would only be able to implement a small part of our vision in the time period. Moreover, we performed a significant amount of customer surveying and scrum planning which became frustratingly useless since we would not get far enough to take advantage of most of it or even a fair amount of the provided data resources.

Accomplishments that we're proud of

We built a framework for easier, faster, more frequent, and more useful data sharing. Should this all be fully implemented it would significantly improve the data that both growers and agronomists readily have access to. Meaning that better and more current insights, suggestions, and warnings would be more readily available to our customers to aid them in producing more and higher quality yield.

What we learned

We learned a lot about barley and agronomics. We also gained experience in implementing large data sets to solve real world problems. Lastly, we learned talking to the end user is extremely important, helping us realize some of our favorite ideas were not truly feasible or came with other dependencies which we had little to no control over.

What's next for BarleyNet

BarleyNet could easily grow in many different directions to become an all in one tool for creating thriving agronomic industries. Expansion into grower agronomist messaging, photo sharing, a notification system, farm accounting, and integration of more data sets could truly make BarleyNet the all in one tool for agriculture optimization. We think what is especially unique about BarleyNet is that it can give more relevant data to specific cases based on any data set or combination of data sets meaning we could in the future give to individual fields highly accurate suggestions, warnings, and predictive modeling, based on prior or similar data sets.

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