Machine vision algorithm for whiteflies ( Bemisia tabaci Genn.) scouting under greenhouse environment.

One of the main problems in greenhouse crop production is the presence of pests. Detection and classification of insects are priorities in integrated pest management (IPM). This document describes a machine vision system able to detect whiteflies ( Bemisia tabaci Genn.) in a greenhouse by sensing their presence using hunting traps. The extracted features corresponding to the eccentricity and area of the whiteflies projections allow to establish differences among pests and other insects on both the trap surfaces and dust generated artefacts. Because of whiteflies geometrical characteristics, it was possible to design an efficient (related to manual counting) machine vision algorithm to scout and count units of this pest within a greenhouse environment. These algorithm results show high correlation indexes for both, sticky screens ( R2 = 0.97) and plant leaf situations ( R2 = 1.0). The machine vision algorithm reduces the scouting time and the associated human error for IPM-related activities. [ABSTRACT FROM AUTHOR]