Multispectral machine vision identification of lettuce and weed seedlings for automated weed control

摘要

Multispectral images of leaf reflectance in the visible and near infrared region from 384 to 810 nm were used to establish the feasibility of developing a site-specific classifier to distinguish lettuce plants from weeds in California direct-seeded lettuce fields. An average crop vs. weed classification accuracy of 90.3% was obtained in a study of over 7,000 individual spectra representing 150 plants. The classifier utilized reflectance values from a small spatial area (3 mm diameter) of the leaf in order to allow the method to be robust to occlusion and to eliminate the need to identify leaf boundaries for shape-based machine vision recognition. Reflectance spectra were collected in the field using equipment suitable for real-time operation as a weed sensor in an autonomous system for automated weed control. Nomenclature-Lettuce, Lactuca sativa L. 'Capitata' and 'Crispa'.