SBIR-STTR Award

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to improve the current spraying practices in agriculture. Inefficient crop spraying causes drift which can lead to crop damage or unwanted pesticide use in neighboring non-target crop areas. It may contaminate nearby bodies of water or be an issue for human health. Our company would solve this through the implementation of a closed loop sensor system which would remotely determine the efficiency of sprayer machines and the spray pattern of the material continuously and in real time. Farmers will benefit through reduced costs of wasted materials resulting from over-spraying, while feeling safe against pest and fungus infestation resulting from under-spraying. The initial intended customers are large orchards, vineyards and tree farms. Those farmers generally use airblast sprayers which are highly inefficient and waste about 45% of the material. Those chemicals either hit the ground and contaminate it or pollute the air. An owner of a large farm that implements our proposed device will save a significant amount on chemical costs and also avoids the consequences due to drift of chemicals onto neighboring farms, public roads, or non-agricultural areas. This SBIR Phase I project proposes to introduce a new technology to the agricultural sector. This technology has been previously built for complex and sophisticated applications in mostly academia and government sectors. We look to provide a new functionality while reducing the size and cost of the technology. It would be used as a new and improved method of calibration, replacing a tedious and often ignored current process. Furthermore, the sensor would be integrated as a closed loop system for autonomous sprayer adjustments. A complete software solution would accompany the hardware in order to make it approachable to the non-scientific community. This project will prove the potential of our technology as a competitive player in the rapidly growing ag-tech field.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This SBIR Phase II project will deploy an innovative technology used in academia into a cost-effective solution for better precision and efficiency in agricultural practices. The envisioned product is a tool for ordinary farmers to increase their production and reduce unnecessary and unpredictable costs, reducing negative impacts on the environment and leading to higher quality food on the table. By converting once complicated, slow, inaccurate practices of insect identification to automated and highly accurate and in real time, this technology will be a part of the automation and data driven industrialization wave transforming the agricultural sector. The quick reaction to pest infestation will allow farmers to better target their response, ultimately making farming more efficient and sustainable. Effective pest control in agriculture is imperative for growers to prevent major crop loss. Certain insect pests are responsible for such significant crop damage which provokes growers to invest in expensive and time-consuming measures to minimize pest effects. The reaction of the grower to the pest is time-sensitive; the timing of the treatment application will directly determine its effectiveness. The proposed technology is an innovative ground-based sensor system that detects plurality of airborne pests in real-time along with their geolocation information. It has capabilities for differentiation and identification of different insect species detected in flight. The instantaneous knowledge of the location of the pest allows for a targeted spray rather than spraying an entire field. Targeted sprays reduce unnecessary spraying and promote more sustainable pest treatment practices. The low-cost sensor that is adequately accurate finally places growers a few steps ahead of the problem, leading them to make wiser treatment decisions such as choosing the time of day to avoid harming beneficial pests and for mitigating drift. The sensor replaces and completely automates the current methods for monitoring insect activity in the crop field, with much better efficiency. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.