Inspiration

While talking to an Agronomist, we identified a serious lack of remote communication options between Agronomists and Farmers. Currently, the Agronomist has to travel out to the farm for disease inspection and diagnosis on crops. Due to the large range of Agronomists, they waste a large amount of time traveling. Thus, Leaf, a uniquely designed method of communication specifically for Agronomists and Farmers, was born.

What it does

Leaf is a method of remote communication between Agronomists and Farmers to reduce the amount of time spent traveling by Agronomists, improving the efficiency of Agronomists. Leaf focuses on image communication supplemented by data designed specifically to support crop inspection and diagnosis.

How we built it

Leaf is built as a iOS app, Android app, Universial Windows Application, and a web app. Microsoft Xamarin Forms was used to build the iOS, Android and UWP app cross-platform. Xamarin offers the ability to write one code base and use most of it for three different platforms. HTML5 and Javascript was used to build the web app. Our backend, which allows all four platforms to communicate with each other is built using Node.js and offers a RESTful API to each platform.

Challenges we ran into

At one point in the project, a 10k ohm resistor was required and the Raspberry Pi only accepted digital inputs, so the solution was to find an Arduino to take the analog input. The Arduino does not have Wifi or Bluetooth so the data had to be ported to the Raspberry Pi before we could transmit it to the app. Another challenge was integrating all of the different components of Leaf together. The pictures in the app were not uploading to the database properly.

Accomplishments that we're proud of

We are proud of creating a cross platform mobile app in addition to a functional web app. We can also feed the images we receive from the farmers into a secondary software that uses image analysis to identify the likeliness of disease.

What we learned

We learned how to collect sensor data and route it to a server to be served up on a web app.

What's next for Leaf

We hope to see Leaf implemented on a wider scale with connections to Agronomists as well as Pathologists who can diagnose a wider variety of diseases. Incorporating more data collecting sensors would give the Agronomists and farmers a more accurate reading of the conditions in the field.

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