Abstract
Precision agriculture is highly dependent on the collection of high quality ground truth data to validate the algorithms used in prescription maps. However, the process of collecting ground truth data is labor-intensive and costly. One solution to increasing the collection of ground truth data is by recruiting citizen scientists through a crowdsourcing platform. In this study, a crowdsourcing platform application was built using a human-centered design process. The primary goals were to gauge users’ perceptions of the platform, evaluate how well the system satisfies their needs, and observe whether the classification rate of lambsquarters by the users would match that of an expert. Previous work demonstrated a need for ground truth data on lambsquarters in the D.C., Maryland, Virginia (DMV) area. Previous social interviews revealed users who would want a citizen science platform to expand their skills and give them access to educational resources. Using a human-centered design protocol, design iterations of a mobile application were created in Kinvey Studio. The application, Mission LQ, taught people how to classify certain characteristics of lambsquarters in the DMV and allowed them to submit ground truth data. The final design of Mission LQ received a median system usability scale (SUS) score of 80.13, which indicates a good design. The classification rate of lambsquarters was 72%, which is comparable to expert classification. This demonstrates that a crowdsourcing mobile application can be used to collect high quality ground truth data for use in precision agriculture.
Highlights
Farming today has grown increasingly dependent on precision agriculture to increase yields and minimize environmental impact; more than 50% of farms in the United States use precision agriculture (PA) in their fields [1,2,3,4]
Identifying the Need for Human-Centered Design Based on previous research, we have identified a need for a human-centered crowdsourcing application to aid in ground truthing for the design of classification machine learning algorithms for precision agriculture
One participant was from Ward 1, four participants were from Ward 4, two participants were from Virginia, and seven participants were from Maryland
Summary
Farming today has grown increasingly dependent on precision agriculture to increase yields and minimize environmental impact; more than 50% of farms in the United States use precision agriculture (PA) in their fields [1,2,3,4]. In order to develop the PA prescription maps that farmers depend on, large amounts of data on soil types, weather, and planting needs to be collected [5]. To design highly accurate PA algorithms to create these maps, millions of data points from thousands of farms are needed [6,7,8]. The collection of this data can be divided into two types: remote and ground truth. The third stage, prescription, is to produce a prescription map from the classified data This map illustrates how much of an input should be applied throughout the field. PA is usually applied to irrigation on a weekly or daily basis [18]
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