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Purpose <p>Site-specific weed management (SSWM) represents a promising solution for reducing herbicide use while maintaining effective weed control. To encourage the adoption of these techniques, SWIM (Smart Weed Impact Management), a decision support system for generating prescription maps based on RGB imagery, is proposed. Two options are available: a Simple Method (SM) for farmers with limited digital expertise and an Improved Method (IM) for more advanced applications.</p> Methods <p>SWIM operates through three sequential phases: (i) weed detection, aimed at estimating Weed Green Cover (WGC) by subtracting Maize Green Cover (MGC) from Total Green Cover (TGC) from aerial images acquired by drone; (ii) potential damage, aimed at determining the economic intervention threshold based on maize yield losses due to weed competition; (iii) prescription map generation, based on the creation of prescription maps for Patch Spraying or Variable Rate Applications (PSA and VRA). In SM, MGC estimation relies on fixed values of number of plants per unit area and cover of a single plant for the entire field obtained from calibration plots. These values are also partially used by IM, which additionally integrates information derived from the spatial variability of plant density. For maize yield loss assessment, SM uses predefined and/or literature-based parameters, whereas IM relies on site-specific data collected in previous years. </p> Results <p>SWIM showed that both methods provided accurate WGC estimates, with root mean square error values below 0.10 and concordance correlation coefficients above 0.91. However, IM outperformed SM in capturing spatial variability and in defining the economic intervention threshold, as SM tended to overestimate the EIT when applying the Goldsmith model. These differences led to two PSA maps with different potential herbicide savings (44% for SM and 28% for IM). </p> Conclusion <p>The ease of use and cost-effectiveness of SWIM may promote its adoption at farm level and contribute to a reduction in herbicide use in arable cropping systems.</p>

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SWIM (smart weed impact management): A decision support system (DSS) for the site-specific weed control in maize cultivation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月30日

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  • Leonardo Ercolini,
  • Nicola Grossi,
  • Davide Moroni,
  • Andrea Berton,
  • Massimo Martinelli,
  • Nicola Silvestri

摘要

Purpose

Site-specific weed management (SSWM) represents a promising solution for reducing herbicide use while maintaining effective weed control. To encourage the adoption of these techniques, SWIM (Smart Weed Impact Management), a decision support system for generating prescription maps based on RGB imagery, is proposed. Two options are available: a Simple Method (SM) for farmers with limited digital expertise and an Improved Method (IM) for more advanced applications.

Methods

SWIM operates through three sequential phases: (i) weed detection, aimed at estimating Weed Green Cover (WGC) by subtracting Maize Green Cover (MGC) from Total Green Cover (TGC) from aerial images acquired by drone; (ii) potential damage, aimed at determining the economic intervention threshold based on maize yield losses due to weed competition; (iii) prescription map generation, based on the creation of prescription maps for Patch Spraying or Variable Rate Applications (PSA and VRA). In SM, MGC estimation relies on fixed values of number of plants per unit area and cover of a single plant for the entire field obtained from calibration plots. These values are also partially used by IM, which additionally integrates information derived from the spatial variability of plant density. For maize yield loss assessment, SM uses predefined and/or literature-based parameters, whereas IM relies on site-specific data collected in previous years.

Results

SWIM showed that both methods provided accurate WGC estimates, with root mean square error values below 0.10 and concordance correlation coefficients above 0.91. However, IM outperformed SM in capturing spatial variability and in defining the economic intervention threshold, as SM tended to overestimate the EIT when applying the Goldsmith model. These differences led to two PSA maps with different potential herbicide savings (44% for SM and 28% for IM).

Conclusion

The ease of use and cost-effectiveness of SWIM may promote its adoption at farm level and contribute to a reduction in herbicide use in arable cropping systems.