TY - JOUR

T1 - Advances in site-specific weed management in agriculture - A review

AU - Gerhards, Roland

AU - Andújar Sanchez, Dionisio

AU - Hamouz, Pavel

AU - Peteinatos, Gerassimos G.

AU - Christensen, Svend

AU - Fernandez-Quintanilla, Cesar

N1 - Publisher Copyright: © 2022 The Authors. Weed Research published by John Wiley & Sons Ltd on behalf of European Weed Research Society.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Artificial Intelligence

KW - patch spraying

KW - precision farming

KW - robotic weeding

KW - sensor technologies

KW - weed mapping

U2 - 10.1111/wre.12526

DO - 10.1111/wre.12526

M3 - Review

AN - SCOPUS:85126048516

VL - 62

SP - 123

EP - 133

JO - Weed Research

JF - Weed Research

SN - 0043-1737

IS - 2

ER -