Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).

Strategies for the use of PIMEX and other video exposure monitoring methods. : Final report from sub-project “implementation strategies” within the HERIVIS project

Video exposure monitoring (VEM) is a group of methods used for occupational hygiene studies. The method is based on a combined use of video recordings with measurements taken with real-time monitoring instruments. A commonly used name for VEM is PIMEX. Since PIMEX initially was invented in the mid 1980’s have the method been implemented and developed in a number of countries. With the aim to give an updated picture of how VEM methods are used and to investigate needs for further development have a number of workshops been organised in Finland, UK, the Netherlands, Germany and Austria. Field studies have also been made with the aim to study to what extent the PIMEX method can improve workers motivation to actively take part in actions aimed at workplace improvements.The results from the workshops illustrates clearly that there is an impressive amount of experiences and ideas for the use of VEM within the network of the groups participating in the workshops. The sharing of these experiences between the groups, as well as dissemination of it to wider groups is, however, limited. The field studies made together with a number of welders indicate that their motivation to take part in workplace improvements is improved after the PIMEX intervention. The results are however not totally conclusive and further studies focusing on motivation are called for.It is recommended that strategies for VEM, for interventions in single workplaces, as well as for exposure categorisation and production of training material are further developed. It is also recommended to conduct a research project with the intention of evaluating the effects of the use of VEM as well as to disseminate knowledge about the potential of VEM to occupational hygiene experts and others who may benefit from its use.

Passive crosslaminated timber buildings : Final report Cerbof-project no. 76

In this project, Stora Enso’s newly developed building system has been further developed to allow building to the Swedish passive house standard for the Swedish climate. The building system is based on a building framework of CLT (Cross laminated timber) boards. The concept has been tested on a small test building. The experience gained from this test building has also been used for planning a larger building (two storeys with the option of a third storey) with passive house standard with this building system. The main conclusions from the project are:  It is possible to build airtight buildings with this technique without using traditional vapour barriers. Initial measurements show that this can be done without reaching critical humidity levels in the walls and roof, at least where wood fibre insulation is used, as this has a greater capacity for storing and evening out the moisture than mineral wool. However, the test building has so far not been exposed to internal generation of moisture (added moisture from showers, food preparation etc.). This needs to be investigated and this will be done during the winter 2013-14.  A new fixing method for doors and windows has been tested without traditional fibre filling between them and the CLT panel. The door or window is pressed directly on to the CLT panel instead, with an expandable sealing strip between them. This has been proved to be successful.  The air tightness between the CLT panels is achieved with expandable sealing strips between the panels. The position of the sealing strips is important, both for the air tightness itself and to allow rational assembly.  Recurrent air tightness measurements show that the air tightness decreased somewhat during the first six months, but not to such an extent that the passive house criteria were not fulfilled. The reason for the decreased air tightness is not clear, but can be due to small movements in the CLT construction and also to the sealing strips being affected by changing outdoor temperatures.  Long term measurements (at least two years) have to be carried out before more reliable conclusions can be drawn regarding the long term effect of the construction on air tightness and humidity in the walls.  An economic analysis comparing using a concrete frame or the studied CLT frame for a three storey building shows that it is probably more expensive to build with CLT. For buildings higher than three floors, the CLT frame has economic advantages, mainly because of the shorter building time compared to using concrete for the frame. In this analysis, no considerations have been taken to differences in the influence on the environment or the global climate between the two construction methods.

Quality management for vital forest tree seedlings : Final report, Project no: P33854-1

The work concerns development of a prototype molecular tests to identify vitality status of conifer seedlings. The work is done by NSure, Holland, Dalarna University and SUAS. In case for spruce, a successful validation experiment has been performed to validate the identified frost tolerance and vitality genes. Multiple indicators were identified that can be used to either reinforce the existing ColdnSure test, but also for development of a vitality test. The identified frost tolerance and vitality genes for pine still need to be validated. NSure together with Dalarna University aim to perform a validation next season. Multiple LN indicators were identified in spruce that can be used to determine the effectiveness of a LN treatment, but they are not yet validated. In spruce and pine hardly any scientific research is performed to study the effect of a LN treatment, particularly not at molecular level. Therefore NSure together with Dalarna Research Station want to apply for a project. Within this project, we would be able to develop the tests further.