SBIR-STTR Award

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 II project seeks to further develop a novel computer vision based plant identification system for commercialization in agricultural weed control. This system will provide a cost competitive alternative to chemical herbicides, a global $20B market. 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's objective is to create 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. The project team will build a computer vision algorithm based on a hierarchical classifier. This classifier will utilize a field customized support vector machine (SVM) that uses point-of-interest rather than shape-based methods, a novel approach to visual object identification. The result of this research will be the creation of an algorithm integrated into an automated weeding system. The broader impact/commercial potential of this project is significant, as the development of an alternative to chemical intensive agricultural weed control will impact technological understanding, create commercial opportunity, and positively impact society. Technologically, the project will advance the fields of computer vision and machine learning through development of a real-time, automated plant identification system based on point-of-interest and SVMs. Commercially, the system will offer conventional farmers an effective and chemical-free method to eliminate weeds, and it will offer organic farmers the first truly precise organic weed control method. The addressable market for weed control in food production is estimated to be $4B in the U.S. 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