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.
In this paper, some morphological transformations are used to detect the background in images characterized by poor lighting. Lately, contrast image enhancement has been carried out by the application of two operators based on the Weber's law notion. The first operator employs information from block analysis, while the second transformation utilizes the opening by reconstruction, which is employed to define the multibackground notion. The objective of contrast operators consists in normalizing the grey level of the input image with the purpose of avoiding abrupt changes in intensity among the different regions. Finally, the performance of the proposed operators is illustrated through the processing of images with different backgrounds, the majority of them with poor lighting conditions.
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