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.
Each crop has their own weed problems. Therefore, to understand each problem, agronomists and weed scientists must be able to determine the weed abundance with the most precise method. There are several techniques to scouting, including visual counting for density or estimations for coverage of weeds. However, this technique depends by the evaluator subjectivity, performance, and training, causing errors and bias when estimating weeds abundance. This chapter introduces a methodology to process multispectral images, based on histograms of oriented gradients and support vector machines to detect weeds in lettuce crops. The method was validated by experts on weed science, and the statistical differences were calculated. There were no significant differences between expert analysis and the proposed method. Therefore, this method offers a way to analyze large areas of crops in less time and with greater precision.
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