SBIR Phase II: Use of Machine Learning Techniques for Robust Crop and Weed Detection in Agricultural Fields

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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.