Hyperspectral Imaging System for Precision Weed Control in Processing Tomato

Abstract

A precision weed control system was developed to autonomously detect, identify and map weed species in the seedline of directly-seeded processing tomatoes and to apply a precise and lethal spray to the weed foliage. The weed sensor, based upon a hyperspectral imaging subsystem equipped with a line imaging spectrograph, had a spectral range of 385 - 810 nm at a 1.6 nm resolution and a spatial resolution of 0.4 mm across the seedline. Hyperspectral field images were collected in a processing tomato field in real-time from a continuously moving cultivation sled. A multivariate Bayesian classifier was constructed off-line to identify the plant species of each pixel in the hyperspectral images. At the pixel level, cross-validation results demonstrated a significant improvement in classifier performance on field data when site-specific classifier training was conducted. A site-specific classifier correctly recognized 95% of tomato foliage and more than 84%, of four weed species (black nightshade (Solanum nigrum L.), lambsquarter (Chenopodium album L.), red-root pigweed (Amaranthus retroflexus L.), and purslane (Portulaca oleracea L.)). At the plant level, nearly all plant species were correctly identified. The hyperspectral imaging system was robust in the identification of partially occluded foliage and provided an accurate weed map suitable for species-specific targeted herbicide application.