Title

Crop Insights: Data Visualization and Management for Farmers and their Orchards

Lead Author Major

Computer Science

Lead Author Status

Senior

Second Author Major

Computer Science

Second Author Status

Senior

Third Author Major

Computer Science

Third Author Status

Junior

Fourth Author Major

Computer Science

Fourth Author Status

Senior

Format

SOECS Senior Project Demonstration

Faculty Mentor Name

Shon Vick

Faculty Mentor Email

svick@pacific.edu

Faculty Mentor Department

Computer Science

Abstract/Artist Statement

Crop Insights is a collaborative project with an ECPE senior project team who are developing a rover that will use machine learning and computer vision to pollinate crops, specifically Kiwi trees, in its initial implementation. To effectively market this product when it is complete, a web application is needed to provide additional value and detailed information to clients, this is Crop Insights’ function.

Crop Insights aims to use the data that is gathered by the ECPE Kiwi Rover Team to provide useful information to the clients (Farmers) who can use to better manage their potential crop yield and their orchards through scheduling visitations (pollen application session) of the rover. Our web application will provide a way to view detailed data that would otherwise be extremely time-consuming or impossible to collect. Based on collected orchard data and analytical metrics provided to us by the ECPE team, via the pollination company they are working with for their portion of the project, we will create an algorithm that will estimate crop yield based on the conditions during a pollen application session. The web application will be developed with scalability and future expansion to other types of crops in mind.

Prior to the delivery of Crop Insights, we will showcase the web application to the ECPE team to verify that it provides the services that were defined as ‘must have’ features for the web application. We will also verify the accuracy of our algorithm, based on historical yield data for given orchards provided to us by the company.

Our presentation will cover the development process of our web application. We will discuss the motivation for the project in depth, as well as some design aspects of the project that we thought were the most important for its success in the market.

Location

School of Engineering & Computer Science

Start Date

5-5-2018 3:30 PM

End Date

5-5-2018 4:30 PM

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May 5th, 3:30 PM May 5th, 4:30 PM

Crop Insights: Data Visualization and Management for Farmers and their Orchards

School of Engineering & Computer Science

Crop Insights is a collaborative project with an ECPE senior project team who are developing a rover that will use machine learning and computer vision to pollinate crops, specifically Kiwi trees, in its initial implementation. To effectively market this product when it is complete, a web application is needed to provide additional value and detailed information to clients, this is Crop Insights’ function.

Crop Insights aims to use the data that is gathered by the ECPE Kiwi Rover Team to provide useful information to the clients (Farmers) who can use to better manage their potential crop yield and their orchards through scheduling visitations (pollen application session) of the rover. Our web application will provide a way to view detailed data that would otherwise be extremely time-consuming or impossible to collect. Based on collected orchard data and analytical metrics provided to us by the ECPE team, via the pollination company they are working with for their portion of the project, we will create an algorithm that will estimate crop yield based on the conditions during a pollen application session. The web application will be developed with scalability and future expansion to other types of crops in mind.

Prior to the delivery of Crop Insights, we will showcase the web application to the ECPE team to verify that it provides the services that were defined as ‘must have’ features for the web application. We will also verify the accuracy of our algorithm, based on historical yield data for given orchards provided to us by the company.

Our presentation will cover the development process of our web application. We will discuss the motivation for the project in depth, as well as some design aspects of the project that we thought were the most important for its success in the market.