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Use of multispectral images to evaluate the efficacy of pre-emergent herbicides in peas under greenhouse conditions

First stage image processing, peas cluster identification. Photo: O. García; Process: A. Puerto


In Colombia, peas are the second most important legume after the bean, and weeds are the main biotic factor that limits production, causing losses of up to 100%. Manual control can represent up to 40% of the labor force. The critical period in the crop-weed competition is the first third of the crop cycle; therefore, pre-emergent herbicide applications are a cost-effective way to control weeds. Common variables for assessing weed-control efficacy include, weed density (individuals/area), which is precise but time consuming, and weed coverage (%), which is faster but very subjective. Therefore, pre-emergence herbicides and a weed-control evaluation method that standardizes, facilitates, and provides greater precision are needed for peas cultivation and experimentation. Five pre-emergent herbicides (linuron, S-metolachlor, metribuzine, oxifluorfen and pendimetalin) were evaluated at two doses in a greenhouse pea crop. Also, two methods (quantification process of multispectral images and conventional human visual) for assessing weed coverage and control efficacy were compared. The best herbicide treatment for the dry grain yield was metribuzine (2.36 t ha-1). Furthermore, the effectiveness of the weed control was 88% at 36 days after sowing, which is optimal. Finally, there was agreement between the weed assessment methods (human vs. machine). The intraclass correlation coefficient was over 0.95, which validates the use of machine quantification for weed coverage.


Pisum sativum L., Chemical weed control, Herbicide efficacy tests, Optical sensor, Spectral reflectance, Image analysis



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