Journal
AGRONOMY-BASEL
Volume 11, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/agronomy11091809
Keywords
weeds; artificial intelligence; hyperspectral; multi-spectral; weeds management
Categories
Funding
- Pest and Disease Monitoring Using Artificial Intelligent for Risk Management of Rice Under Climate Change under the Long-Term Research Grant Scheme (LRGS), Ministry of Higher Education, Malaysia [5545002]
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Weeds are unwanted plants that reduce crop yields and need to be controlled; integrating technologies like drones can better manage weed problems; future challenges can be overcome through remote sensing and machine learning approaches.
Weeds are unwanted plants that can reduce crop yields by competing for water, nutrients, light, space, and carbon dioxide, which need to be controlled to meet future food production requirements. The integration of drones, artificial intelligence, and various sensors, which include hyperspectral, multi-spectral, and RGB (red-green-blue), ensure the possibility of a better outcome in managing weed problems. Most of the major or minor challenges caused by weed infestation can be faced by implementing remote sensing systems in various agricultural tasks. It is a multi-disciplinary science that includes spectroscopy, optics, computer, photography, satellite launching, electronics, communication, and several other fields. Future challenges, including food security, sustainability, supply and demand, climate change, and herbicide resistance, can also be overcome by those technologies based on machine learning approaches. This review provides an overview of the potential and practical use of unmanned aerial vehicle and remote sensing techniques in weed management practices and discusses how they overcome future challenges.
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