Weeds are a persistent foe for farmers, competing for valuable resources and significantly reducing crop yields. Traditional weed control methods often involve applying herbicides uniformly across the entire field without considering weed pressure and distribution, leading to increasing concerns about environmental impact and the risk of evolution of herbicide resistance in weeds. This chapter explores the promising potential of unmanned aerial vehicles (UAVs) equipped with RGB cameras and multispectral/hyperspectral sensors to precisely identify and map weed infestations for site-specific weed management (SSWM). UAVs offer millimeter-level spatial resolution with three-dimensional stereo-vision capabilities, enabling better differentiation of weeds within crop fields. It allows farmers to apply herbicides precisely to affected areas, reducing waste and minimizing environmental impact. UAVs can navigate complex agricultural landscapes, identify weed-infested areas, and apply herbicides with high precision. Despite the advantages of UAVs over other remote sensing platforms, their adoption in crop fields requires significant technological advancements to overcome challenges such as high initial investment costs, complex image acquisition and processing, limited battery endurance, and lower payload capacity. Establishing standardized operating procedures is crucial for effective and environmentally responsible herbicide application using UAVs by reducing the risk of herbicide drift. These protocols should address critical factors like optimal spray height, droplet size, spray volume, wind speed, and suitable weather conditions. Farmers can deploy multiple drones simultaneously, covering larger areas more efficiently and effectively. The future will also witness the integration of UAV data with satellite imagery and ground-based sensors using the Internet of Things (IoT) and cloud processing to create a more comprehensive and accurate map of weed distribution and crop health.

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The Role of Unmanned Aerial Vehicles and Sensor Technology in Site-Specific Weed Management

  • S. Vijayakumar,
  • Bholuram Gurjar,
  • Muthukumar Bagavathiannan,
  • Virender Kumar

摘要

Weeds are a persistent foe for farmers, competing for valuable resources and significantly reducing crop yields. Traditional weed control methods often involve applying herbicides uniformly across the entire field without considering weed pressure and distribution, leading to increasing concerns about environmental impact and the risk of evolution of herbicide resistance in weeds. This chapter explores the promising potential of unmanned aerial vehicles (UAVs) equipped with RGB cameras and multispectral/hyperspectral sensors to precisely identify and map weed infestations for site-specific weed management (SSWM). UAVs offer millimeter-level spatial resolution with three-dimensional stereo-vision capabilities, enabling better differentiation of weeds within crop fields. It allows farmers to apply herbicides precisely to affected areas, reducing waste and minimizing environmental impact. UAVs can navigate complex agricultural landscapes, identify weed-infested areas, and apply herbicides with high precision. Despite the advantages of UAVs over other remote sensing platforms, their adoption in crop fields requires significant technological advancements to overcome challenges such as high initial investment costs, complex image acquisition and processing, limited battery endurance, and lower payload capacity. Establishing standardized operating procedures is crucial for effective and environmentally responsible herbicide application using UAVs by reducing the risk of herbicide drift. These protocols should address critical factors like optimal spray height, droplet size, spray volume, wind speed, and suitable weather conditions. Farmers can deploy multiple drones simultaneously, covering larger areas more efficiently and effectively. The future will also witness the integration of UAV data with satellite imagery and ground-based sensors using the Internet of Things (IoT) and cloud processing to create a more comprehensive and accurate map of weed distribution and crop health.