SBIR Phase I: Estimation of Detailed Plant Geometry for Robotic Farm Work

这个小企业创新研究（SBIR）第一阶段项目的更广泛影响将改善农业经济。对植物几何形状具有良好计算机感知的机器可以实施减少农药，肥料，水和燃料使用的农业实践。这将提高农场的效率，并为环境带来长期的好处。这个小型企业创新研究（SBIR）第一阶段项目将开发新的算法，用于从图像中估计植物结构。这是具有挑战性的，因为农业领域通常密集地挤满了植物，使得每个植物的部分被隐藏而不可见。现有的方法可以估计丢失的人造物体的几何形状从受阻的意见，但植物是相当困难的，由于其几何形状的复杂性，可变性和视觉模糊性。该项目的目标是达到以前在工厂成像中无法达到的细节和准确度。为了实现这一目标，该项目将使用现场数据收集，算法开发和现场测试的快速迭代。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will improve the economics of farming. Machines with good computer perception of plant geometry can implement farming practices that reduce the use of pesticides, fertilizer, water, and fuel. This will increase farm efficiency and provide long-term benefits to the environment.This Small Business Innovation Research (SBIR) Phase I project will develop new algorithms for estimation of plant structure from images. This is challenging because agricultural fields are often densely packed with plants, such that portions of each plant are hidden from view. Existing methods can estimate missing geometry of man-made objects from obstructed views, but plants are considerably more difficult, due to the complexity, variability, and visual ambiguity of their geometry. The project will aim to achieve levels of detail and accuracy not previously attained in plant imaging. To reach this goal, the project will use rapid iterations of field data collection, algorithm development, and field testing.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.