This Small Business Innovation Research (SBIR) Phase I project seeks to understand the fundamental visual cues and characteristics of plants found in agricultural facilities for the purpose of rapid automated identification of plant species. The human eye, coupled with the brain?s processing power , can readily distinguish between different plant species. This capability was one of the basic needs for humans to become an agrarian society (farming requires weeding), which helped start enormous social advancement. Similarly, to bring automated systems to the next generation of capability, computer vision must interact with the natural world with greater fidelity. Today?s computer vision has ability to detect a ?splotch? of vegetation versus no vegetation. This project will advance computer vision by developing the equipment and software algorithms necessary to automatically distinguish plant types. The project team will build a computer vision algorithm based on a field customized support vector machine (SVM) that can automatically and reliably identify a known crop versus a foreign plant (i.e. weed) for use in a larger system for automated weeding. By creating the ability for computers to distinguish between plant types, we will enable food to be grown with reduced amounts of chemical herbicides.
The broader impact/ commercial potential of this project is to increase the competitiveness of vegetable farms, particularly organic ones, while improving human health and the environment. Today, organic farms represent 5% of the U.S. agricultural economy and are growing at a pace to double organic acreage every 4 years. A key feature of organic farming is the lack of herbicides. Consequently, organic farms are normally weeded by hand. Weed control represents approximately 50% of operating costs for organic farms, compared to less than 10% for conventional ones. With an estimated $700M spent annually on weeding organic farms, there is a substantial commercial opportunity to create a system that can weed farms automatically. This project will develop a system that uses a computer system towed behind a tractor to automatically detect and eliminate weeds at early plant stages. The system can be developed and deployed at less than 1/5 the life-cycle costs of hand weeding. The technology is also applicable to conventional crop thinning where it can significantly reduce the amount of herbicides used. Additionally this technology has a profound health and sustainability benefits by eliminating human exposure to chemical herbicides through food and avoids herbicides leaching into the soil.
This Small Business Innovation Research (SBIR) Phase I project was successful in verifying the feasibility of real-time, high-precision visual identification of individual plants to enable the development of an automated thinning and weeding system. The efforts built upon prior work in computer vision-based plant identification to provide novel detection capability with characteristics suitable for commercialization. Existing computer vision based approaches can segment a "splotch" of green vegetation from a brown background but are unable to provide the selectivity and precision necessary for mechanized, automated weeding. This project successfully created computer vision and machine learning based software algorithms that match the capability of the human eye and brain to quickly and reliably classify plants into crops and weeds in real-time. Upon commercialization, the proposed technology will provide an alternative to chemical intensive agricultural weed control. Commercially, the system under development by Blue River Technology will provide a cost competitive alternative to chemical herbicides, a global $25B market. In addition, it will offer organic farmers the first truly precise organic weed control method. The system’s ability to eliminate the use of chemical herbicides has a profound societal effect. U.S. farmers apply over 250M pounds of herbicide annually on corn and soybeans alone, with many unintended and detrimental side effects. Chemical concentrations in rivers, lakes and groundwater are rising, and the prevalence of herbicide resistant weeds is growing exponentially. An alternative to these chemicals limits society’s exposure while protecting environmental integrity.
