Browsing by Subject "Weed mapping"

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Advances in site-specific weed management in agriculture: A review
(2022) Gerhards, Roland; Andújar Sanchez, Dionisio; Hamouz, Pavel; Peteinatos, Gerassimos G.; Christensen, Svend; Fernandez‐Quintanilla, Cesar
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.
Investigations on site-specific weed management for a decision support system for patch spraying
(2012) Gutjahr, Christoph; Gerhards, Roland
During the past five years, powerful sensor technologies have been developed which are capable of classifying weed species in digital images based on shape features and which allow assessing weed seedling distributions automatically in arable crops. Classification algorithms have been computed based on shape features to differentiate between the most abundant weed species in winter wheat, winter barley, maize and sugar beet. Those cameras were used in combination with GPS and GIS-technologies to create weed distribution maps or they can be mounted in front of a sprayer to detect and spray weed patches in real-time. It has been shown in previous studies that patch spraying, based on weed distribution maps and simple decision rules for herbicide application significantly reduces the amount of herbicides needed. Therefore, site-specific weed management practices have economic and ecological benefits by reducing the amount of herbicides applied. It has further been shown that populations of Galium aparine and Alopecurus myosuroides did not significantly change in location and size when site-specific weed control methods were applied over a period of 8 years. However, precise decision rules for site-specific weed management are still lacking. The objectives of this study were to derive and verify decision rules for site-specific weed management in winter annuals grains and maize. This study includes three work packages: In the first work package, weed species were grouped into three classes based on their competitive ability and sensitivity to herbicides. The first group contained annual grasses, the second group annual dicotyledons and the third group perennial weed species. Weed distribution maps were created for all groups of weed species in winter wheat, winter barley, maize and sugar beets. It was then analysed at which locations in the field weed control measures were warranted and which herbicides and combinations of herbicides were required. Weed control measures were realized with a multiple tank sprayer and spatial and temporal stability of weed patches was assessed in the following year. In the second work package, a so-called Precision Experimental Design using Precision Farming technologies and Geographic Information System, was applied in maize, winter barley and winter wheat to determine the effects of each weed species group, soil variability and herbicide application on grain yield separately. Data of these experiments were used to calculate yield loss functions for individual weed species. In the third work package, the structure of a decision support system for site-specific weed control was created including yield loss function and dose-response curves for the most relevant weed species in winter wheat and maize. The results of the three work package can be summarized as followed: All weed species and weed classes were distributed heterogeneously within the fields with densities ranging from 0 to more than 200 plants m-2. Patch spraying resulted in 30-40% herbicide saving when a tank mixtures of all herbicides needed was applied. Savings of 77% were achieved when a three tank sprayer was used to apply each herbicide at different locations. For the Precision Experimental Design, a linear mixed model with spatial correlation structure has been modified and fitted to the data. It was found that competition of E. crus-galli resulted in significant yield losses of 0.027 t ha-1 plant m-2 in maize and G. aparine in 0.034 t ha-1 plant m-2 yield loss in winter wheat. However, herbicides against grasses and annual dicotyledons also reduced grain yield by approximately 0.3 t ha-1, which again underlines the necessity to save herbicides at location where no or only few weed species are present. ?HPS Online? describes a possible structure of a decision support system for patch spraying. The combination of yield loss functions for the most abundant weed species/group of species with dose response curves for the most relevant herbicides to control these species allowed determining the most economic weed control strategy at each location in the field. It is recommended to include weather conditions or historical data of the fields, if available, such as maps of perennial weed species to optimize weed control decisions. In conclusion of the results, precision weed management offers a great potential for herbicide savings in arable crops. It requires the combination of automatic sensor technology for weed detection, a decision support system for weed control and application technology to vary the herbicide mixture in real-time.