外文摘要：Weed harrowing is commonly used to manage weeds in organic farming but is also applied in conventional farming to replace herbicides. Due to its whole-field application, weed harrowing after crop emergence has relatively poor selectivity and may cause crop damage. Weediness generally varies within a field. Therefore, there is a potential to improve the selectivity and consider the within-field variation in weediness. This paper describes a decision model for precision post-emergence weed harrowing in cereals based on experimental data in spring barley and nonlinear regression analysis. The model predicts the optimal weed harrowing intensity in terms of the tine angle of the harrow for a given weediness (in terms of percentage weed cover), a given draft force of tines, and the biological weed damage threshold (in terms of percentage weed cover). Weed cover was measured with near-ground RGB images analyzed with a machine vision algorithm based on deep learning techniques. The draft force of tines was estimated with an electronic load cell. The proposed model is the first that uses a weed damage threshold in addition to site-specific values of weed cover and soil hardness to predict the site-specific optimal weed harrow tine angle. Future field trials should validate the suggested model.
外文关键词：precision agriculture;site-specific weed management (SSWM);digital farming;flex-tine;integrated pest management (IPM);integrated weed management (IWM);mechanical weeding;variable rate application (VAR)
作者：Andreasen, Christian;Berge, Therese W;Urdal, Frode;Torp, Torfinn
作者单位：Univ Copenhagen;Norwegian Inst Bioecon Res NIBIO;Adigo AS
期刊名称：AGRONOMY-BASEL
期刊影响因子：0.0
出版年份：2024
出版刊次：14(1)
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台