Browsing by Subject "Site-specific weed control"

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An image analysis and classification system for automatic weed species identification in different crops for precision weed management
(2010) Weis, Martin; Gerhards, Roland
A system for the automatic weed detection in arable fields was developed in this thesis. With the resulting maps, weeds in fields can be controlled on a sub-field level, according to their abundance. The system contributes to the emerging field of Precision Farming technologies. Precision Farming technologies have been developed during the last two decades to refine the agricultural management practise. The goal of Precision Farming is to vary treatments within fields, according to the local situation. These techniques lead to an optimisation of the management practice, thereby saving resources, increasing the farmers outcome, reducing the overall management costs and the environmental impact. A successful introduction of Precision Farming involves the development of application equipment capable of varying treatments and sensor technology to measure the spatial heterogeneity of important growth factors. Such systems are able to record, store and use large amounts of data gathered by the sensors. Decision components are needed to transform the measurements into practical management decisions. Since the treatments are varied spatially, positional data, usually measured using GPS technology, has to be processed. The located measurements lead to a delineation of management zones within a field and are represented by geo-data and can be visualised in maps. The improved, detailed knowledge of the situation within the field leads to new and extended scopes of applications and allows to document the management practices more precisely. In this work, parts of Precision Farming technology were developed for site-specific weed management. Five selected publications are presented, covering the technological prerequisites and details of the developed system.
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.
Teilschlagspezifische Unkrautbekämpfung durch raumbezogene Bildverarbeitung im Offline- und (Online-) Verfahren (TURBO)
(2006) Oebel, Horst; Gerhards, Roland
Georeferenced application maps (TURBO) is presented. The system was applied and analysed on agricultural fields from 2004 to 2005. The results can be summarized as followed: For online image acquisition bi-spectral cameras were developed which took homogeneous grey scale pictures with a strong contrast using a combination of two spectral channels in the near infrared and the visible spectrum. Three bi-spectral cameras were mounted in front of a prototype carrier vehicle. Using an automatic control of the exposure time, well focused pictures of weeds in cereals, maize, sugar beets, peas and oil seed rape were taken at a speed up to 10 km/h and stored together with their GPS coordinates. Under changing light conditions, bi-spectral images were free of faults. Stones, mulch and soil were not illustrated. The picture quality showed a clear improvement compared to current image analysis technologies using colour and infrared cameras in plant production. The geometric resolution of the cameras was sufficient for creating application maps. With a size of 0.014 m² per picture weed seedlings were representatively assessed. The dense grid of 3.500 sampling points per hectare allowed an efficient detection of weed distribution within agricultural fields. The procedure of shape analysis allowed precise identification of weed species in a speed of 20 images per second. The classification rate of unclassified plants using Fuzzy Logic or the principle of minimum distance was between 73 % (malt barley) and 85 % (oil seed rape). The calculation of discrimination functions to separate crops and weed classes by shape parameters allowed a better classification of unknown plants and increased the classification rate to 88.4 % (sugar beets) and 94 % (malt barley). Characteristic shape features of 45 weed species in the growth stages BBCH 10 to BBCH 14 were stored in a database and the classification of weed species in malt barley, maize and sugar beets was studied using discrimination analysis. In growth stage BBCH 10 weed species could be differentiated on average by 70 %. Crops were accurately differentiated from broadleaved weeds and grass weeds. Joining weeds species (BBCH 10) in the classes broadleaved weed species, grass weeds, Galium aparine and crop resulted in correct classification of 83 % in malt barley to 96 % in maize. With manual, GIS-based and image analysis sampling methods treatment maps for three weed species classes were created for site-specific weed control in cereals, sugar beet, maize, oil seed rape and peas on a total of 138 ha. Economic weed threshold were used as a decision rule for chemical weed control. Herbicides were only applied when the economic weed threshold was exceeded. Above the economic weed threshold the herbicide dosage was varied from 70 % to 100 % depending on the density of weed species. Herbicide application was performed with a newly developed multiple sprayer. The sprayer integrates three conventional sprayers on one machine including three separated hydraulic circuits, boom section control (3 m), dGPS for real time location and a central control unit. During application the on-board computer loading a georeferenced application maps was linked to the spray control system for precise application of up to three different herbicide mixtures. Herbicide savings using site-specific weed control depended on the cultivated crop, weed species composition and weed infestation levels. On average 47 % of herbicides for grass weeds and 35 % for broad-leaved weeds were saved. Herbicide use to control Galium aparine and Cirsium arvense was reduced by 71 %. The efficacy of site-specific weed control was documented by manual weed sampling before and after post emergent herbicide treatments. It ranged from 71.8 % to 98.8 %. Weed infestation level did not increase in the following crops. First results with yield mapping of experimental fields revealed that site-specific weed control did not cause yield reduction. On contrary, in cereals higher yields were observed at locations where no herbicides were applied. However, further studies are needed to prove this hypothesis. The economic evaluation of site-specific weed control over two years on practical farm sites showed that site-specific weed control was profitable. The average savings for herbicides were 27.61 ?/ha. This resulted in an average profit of 11.35 ?/ha using the system for site-specific weed control.