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In this paper, an electro-mechanical integrated weed management system was designed, constructed, and tested. This laboratory-scale solution integrated elements of machine vision, controls, and mechanical actuation to selectively remove weeds from within a crop row. The device was validated in a controlled environment using corn crops. Various crop conditions were considered to ensure the robustness of the design. Though some design aspects should be reworked for improved results, the device can effectively be used to facilitate small-scale research for automated weeding strategies.

A generalized Visual Fast Count (GVFC) method of moments estimator is proposed for estimating epifaunal species abundance from underwater video survey transects. This formalises and provides a statistical framework for previous ad‐hoc Visual Fast Count methods. For a single transect, we derive the expected value of the naive GVFC estimator and use this to create the method of moments estimator, which has reduced bias. A maximum likelihood estimator for multiple transects is derived. For illustration, our methods are applied to a series of video trawls at Folkestone Pomerania in the Dover Straits, UK. Although our methods have been developed for marine applications, they could also be applied to some terrestrial transect surveys.