Journal
WEED RESEARCH
Volume 62, Issue 2, Pages 123-133Publisher
WILEY
DOI: 10.1111/wre.12526
Keywords
Artificial Intelligence; patch spraying; precision farming; robotic weeding; sensor technologies; weed mapping
Categories
Funding
- German Federal Ministry of Food and Agriculture
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The developments of information and automation technologies have opened up new possibilities for weed management in agricultural fields. Site-specific weed management systems, utilizing sensor technologies and artificial intelligence, have shown ecological and economic benefits in controlling weeds. Challenges exist in terms of interoperability of sensing and controllers, but the adoption of standards could improve the implementation of these technologies. Further on-farm research is needed to integrate these technologies into agricultural practice. Overall, the potential impact of site-specific weed management in reducing pesticide use and increasing biodiversity is significant.
The developments of information and automation technologies have opened a new era for weed management to fit physical and chemical control treatments to the spatial and temporal heterogeneity of weed distributions in agricultural fields. This review describes the technologies of site-specific weed management (SSWM) systems, evaluates their ecological and economic benefits and gives a perspective for the implementation in practical farming. Sensor technologies including 3D cameras, multispectral imaging and Artificial Intelligence (AI) for weed classification and computer-based decision algorithms are described in combination with precise spraying and hoeing operations. Those treatments are targeted for patches of weeds or individual weed plants. Cameras can also guide inter-row hoes precisely in the centre between two crop rows at much higher driving speed. Camera-guided hoeing increased selectivity and weed control efficacy compared with manual steered hoeing. Robots combine those guiding systems with in-row hoeing or spot spraying systems that can selectively control individual weeds within crop rows. Results with patch spraying show at least 50% saving of herbicides in various crops without causing additional costs for weed control in the following years. A challenge with these technologies is the interoperability of sensing and controllers. Most of the current SSWM technologies use their own IT protocols that do not allow connecting different sensors and implements. Plug & play standards for linking detection, decision making and weeding would improve the adoption of new SSWM technologies and reduce operational costs. An important impact of SSWM is the potential contribution to the EU-Green Deal targets to reduce pesticide use and increase biodiversity. However, further on-farm research is needed for integrating those technologies into agricultural practice.
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