Browsing by Subject "Bispektralkamera"

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Effects of weeds on yield and determination of economic thresholds for site-specific weed control using sensor technology
(2014) Keller, Martina; Gerhards, Roland
Weeds can cause high yield losses. Knowledge about the weeds occurring, their distribution within fields and their effects on the crop yield is important to achieve effective weed control. The critical period for weed control (CPWC) and the economic threshold (ET) are important key concepts and management tools in weed control. While the former helps to time weed control in crops of low competitiveness, the latter provides a decision aid to determine whether weed control is necessary. This decision is generally taken at the field level. Weeds have been found to be distributed heterogeneously within fields. Site-specific weed control (SSWC) addresses this sub-field variation by determining weed distribution as input, by taking control decisions in the decision component and by providing control measures as output at high spatial resolution. Sensor systems for automated weed recognition were identified as prerequisite for SSWC since costs for scouting are too high. While experiences with SSWC using sensor data as input are still scarce, studies showed that considerable herbicide savings could be achieved with SSWC. ETs can serve as thresholds for the decision component in SSWC systems. However, the commonly used ETs were suggested decades ago and have not been updated to changing conditions since. The same is the case for the CPWC in maize in Germany. In addition, the approaches to determine the CPWC are usually not based on economic considerations, which are highly relevant to farmers. Thus, the objectives of this thesis are: 1. To test different models and to provide a straightforward approach to integrate economical aspects in the concept of the CPWC for two weed control strategies: Herbicide based (Germany) and hoeing based (Benin); 2. To determine the effect of weeds on yield and to calculate ETs under current conditions which can be used for SSWC; 3. To evaluate the use of bi-spectral cameras and shape-based classification algorithms for weed detection in SSWC; and 4. To determine changes in weed frequencies, herbicide use and yield over the last 20 years in southwestern Germany. Datasets in maize from Germany and Benin served as input for the CPWC analyses. The log-logistic model was found to provide a similar fit as the commonly used models but its parameters are biologically meaningful. For Germany, analyses using a full cost model revealed that farmers should aim at applying herbicides early before the 4-leaf stage of maize. In Benin, where weed control is mainly done by hoeing, analyses showed that one well- timed weeding operation around the 10-leaf stage could already be cost-effective. A second weeding operation at a later stage would assure profit. The precision experimental design (PED) was employed to determine the effect of weeds, soil properties and herbicides on crop yield in three winter wheat trials. In this design, large field trials’ geodata of weed distribution, herbicide application, soil properties and yield are used to model the effects of the former three on yield. Galium aparine, other broadleaved weeds and Alopecurus myosuroides reduced yield by 17.5, 1.2 and 12.4 kg ha-1 plant-1 m2 determined by weed counts. The determined thresholds for SSWC with independently applied herbicides were 4, 48 and 12 plants m-2, respectively. Bi-spectral camera based weed–yield estimates were difficult to interpret showing that this technology still needs to be improved. However, large weed patches were correctly identified. ETs derived of field trials’ data carried out at several sites over 13 years in the framework of the ’Gemeinschaftsversuche Baden-Württemberg’ were 9.2-9.8 and 4.5-8.9 % absolute weed coverage for winter wheat and winter barley and 3.7% to 5.5% relative weed coverage for maize. Overall, the weed frequencies in winter cereals were found to be more stable than the weed frequencies in maize during the observation period. In maize, a frequency increase of thermophilic species was found. Trends of considerable yield increases of 0.16, 0.08 and 0.2 t ha-1 for winter wheat, winter barely and maize, respectively, were estimated if weeds were successfully controlled. In order to evaluate the use of bi-spectral cameras and shapebased classification algorithms for weed detection in SSWC, herbicides were applied site-specifically using weed densities determined by bi-spectral camera technology in a winter wheat and maize field. Threshold values were employed for decision taking. Using this approach herbicide savings between 58 and 83 % could be achieved. Such reductions in herbicide use would meet the demand of society to minimize the release of plant protection products in the environment. Misclassification occurred if weeds overlapped with crop plants and crop leaf tips were frequently misclassified as grass weeds. Improvements in equipment, especially between the interfaces of camera, classification algorithms, decision component and sprayer are advisable for further trials. In conclusion, the derived ETs can be easily implemented in a straightforward SSWC system or can serve as decision aid for farmers in winter wheat and winter barley. Further model testing and adjusting would be necessary. For maize, the use of ETs at the field level is not suggested by this study, however the need for early weed control is clearly demonstrated. Bi-spectral camera technology combined with classification algorithms to detect weeds is promising for research use and for SSWC, but still requires some technical improvements.
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.