SBIR Phase I: Precision Farming Operating System for Personal Unmanned Aerial Vehicles with Intelligent Field Adaptive Data Collection Protocol

The broader impact/commercial potential of this project lies in the accessibility and accuracy of the proposed system. By eliminating the hurdle of using Unmanned Aerial System (UAS) for farmers, agricultural field scouting becomes more accessible and will lead to wider adoption, which will lead to significant new market for UAS in agriculture. This system will provide significant savings to crop-growers by allowing targeted application of chemicals. More than $8 billion dollars are spent on herbicide and pesticides in US annually, and the high quality data from the proposed system will lead to a more efficient use of resources in agriculture, which will provide significant savings to farmers, while at the same time, help reduce the release of chemicals and other pollutants into the environment. Wide spread usage of this system will also allow greater data accumulation from a wider geospatial, higher temporal spectrum that is desperately needed for the agricultural Big Data efforts aimed at further advancements in the precision farming decision support systems. This Small Business Innovation Research (SBIR) Phase I project will examine the feasibility of developing a system that will have near real time agricultural data collection/processing capabilities using Unmanned Aerial Systems (UAS) and tablet computers. The lack of a high-resolution data collection system is a great stumbling block to current precision agriculture technology. Existing methods of data collection are time consuming and yield low-quality data. Current aerial data collection methods (include some that utilize UAS) preclude on-site image processing, and delays the delivery of time-sensitive field situation reports. The proposed system will test novel data collection and data pre-processing methods that eliminate computationally expensive algorithms thereby allowing on-site data processing using tablet pcs. This goal will be reached by utilizing over a decade worth of accumulated crop data to develop the optimum data collection processes and data processing algorithms. The end result will be a prototype system that will provide high-quality agricultural data that meet the farmers? requirement in spatial and temporal resolutions.