Proof of concept of automated mapping of weeds in arable fields

The project investigated whether it would be possible to remove the main technical hindrance to precision application of herbicides to arable crops in the UK, namely creating geo-referenced weed maps for each field. The ultimate goal is an information system so that agronomists and farmers can plan precision weed control and create spraying maps. The project focussed on black-grass in wheat, but research was also carried out on barley and beans and on wild-oats, barren brome, rye-grass, cleavers and thistles which form stable patches in arable fields. Farmers may also make special efforts to control them. Using cameras mounted on farm machinery, the project explored the feasibility of automating the process of mapping black-grass in fields. Geo-referenced images were captured from June to December 2009, using sprayers, a tractor, combine harvesters and on foot. Cameras were mounted on the sprayer boom, on windows or on top of tractor and combine cabs and images were captured with a range of vibration levels and at speeds up to 20 km h-1. For acceptability to farmers, it was important that every image containing black-grass was classified as containing black-grass; false negatives are highly undesirable. The software algorithms recorded no false negatives in sample images analysed to date, although some black-grass heads were unclassified and there were also false positives. The density of black-grass heads per unit area estimated by machine vision increased as a linear function of the actual density with a mean detection rate of 47% of black-grass heads in sample images at T3 within a density range of 13 to 1230 heads m-2. A final part of the project was to create geo-referenced weed maps using software written in previous HGCA-funded projects and two examples show that geo-location by machine vision compares well with manually-mapped weed patches. The consortium therefore demonstrated for the first time the feasibility of using a GPS-linked computer-controlled camera system mounted on farm machinery (tractor, sprayer or combine) to geo-reference black-grass in winter wheat between black-grass head emergence and seed shedding.

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).

Farm-level economic performance of genetically modified cotton in Maharashtra, India

A study of the commercial growing of Bacillus flutringiensis (Bt) cotton in India, compares the performance of over 9,000 Bt and non-Bt cotton farm plots in Maharashtra over the 2002 and 2003 seasons. Results show that since their commercial release in 2002, Bt cotton varieties have had a significant positive impact on average yields and on the economic performance of cotton growers. Regional variation showed that, in a very few areas, not all farmers had benefited from increased performance of Bt varieties.

Genetically modified insect resistance in cotton: some farm level economic impacts in India

The paper explores the impact of insect-resistant Bacillus thuringiensis (Bt) cotton on costs and returns over the first two seasons of its commercial release in three sub-regions of Maharashtra State, India. It is the first such research conducted in India based on farmers' own practices rather than trial plots. Data were collected for a total of 7793 cotton plots in 2002 and 1577 plots in 2003. Results suggest that while the cost of cotton seed was much higher for farmers growing Bt cotton relative to those growing non-Bt cotton, the costs of bollworm spray were much lower. While Bt plots had greater costs (seed plus insecticide) than non-Bt plots, the yields and revenue from Bt plots were much higher than those of non-Bt plots (some 39% and 63% higher in 2002 and 2003, respectively). Overall, the gross margins of Bt plots were some 43% (2002) and 73% (2003) higher than those of non-Bt plots, although there was some variation between the three sub-regions of the state. The results suggest that Bt cotton has provided substantial benefits for farmers in India over the 2 years, but there are questions as to whether these benefits are sustainable. (c) 2004 Elsevier Ltd. All rights reserved.

The 2002 US Farm Bill: what's in it for CAP reform?

The 2002 U.S. Farm Bill (the Farm Security and Rural Investment Act or FSRIA) provides considerably more government subsidies for U.S. agriculture than Congress envisaged when it passed the preceding 1996–2002 FAIR Act. We review the FAIR record, showing how government subsidies increased greatly beyond those originally scheduled. For FSRIA, we outline key commodity, trade, and conservation and environmental provisions. We expect that the commodity programmes will: (a) encourage production when the market calls for less; (b) significantly increase subsidies over FAIR baseline subsidies; (c) press against current WTO and possible Doha Round support limits; and (d) aggravate trading partners. Finally, we suggest two lessons from the U.S. policy experience that might benefit those working on CAP and WTO reform. First, past research shows that farm programmes have little to do with the economic health of rural communities. Second, programme transparency, and especially public disclosure of the level of payments going to individual farmers, by name, influences the farm policy debate. Personalized data show what economists have long maintained—that the bulk of programme benefits go to a relatively few, large, producers—but do so in a way that captures the public and policy-makers' attention

Farming systems methodology for efficient resource management at the farm level: a review from an Indian perspective

Farming systems research is a multi-disciplinary holistic approach to solve the problems of small farms. Small and marginal farmers are the core of the Indian rural economy Constituting 0.80 of the total farming community but possessing only 0.36 of the total operational land. The declining trend of per capita land availability poses a serious challenge to the sustainability and profitability of farming. Under such conditions, it is appropriate to integrate land-based enterprises such as dairy, fishery, poultry, duckery, apiary, field and horticultural cropping within the farm, with the objective of generating adequate income and employment for these small and marginal farmers Under a set of farm constraints and varying levels of resource availability and Opportunity. The integration of different farm enterprises can be achieved with the help of a linear programming model. For the current review, integrated farming systems models were developed, by Way Of illustration, for the marginal, small, medium and large farms of eastern India using linear programming. Risk analyses were carried out for different levels of income and enterprise combinations. The fishery enterprise was shown to be less risk-prone whereas the crop enterprise involved greater risk. In general, the degree of risk increased with the increasing level of income. With increase in farm income and risk level, the resource use efficiency increased. Medium and large farms proved to be more profitable than small and marginal farms with higher level of resource use efficiency and return per Indian rupee (Rs) invested. Among the different enterprises of integrated farming systems, a chain of interaction and resource flow was observed. In order to make fanning profitable and improve resource use efficiency at the farm level, the synergy among interacting components of farming systems should be exploited. In the process of technology generation, transfer and other developmental efforts at the farm level (contrary to the discipline and commodity-based approaches which have a tendency to be piecemeal and in isolation), it is desirable to place a whole-farm scenario before the farmers to enhance their farm income, thereby motivating them towards more efficient and sustainable fanning.

Farm-level economic performance of genetically modified cotton in Maharashtra, India

A study of the commercial growing of Bacillus flutringiensis (Bt) cotton in India, compares the performance of over 9,000 Bt and non-Bt cotton farm plots in Maharashtra over the 2002 and 2003 seasons. Results show that since their commercial release in 2002, Bt cotton varieties have had a significant positive impact on average yields and on the economic performance of cotton growers. Regional variation showed that, in a very few areas, not all farmers had benefited from increased performance of Bt varieties.

Farm costs associated with premovement testing for bovine tuberculosis

Sixty cattle farmers in England were questioned about the costs associated with premovement testing for bovine tuberculosis (TB). On average, the farmers had premovement tested 2-45 times in the previous 12 months, but the majority had tested only once. An average of 28.6 animals were tested on each occasion, but there were wide variations. The average farm labour costs were (sic)4.00 per animal tested, veterinary costs were (sic)4.33 and other costs were (sic)0.51, giving a total cost of (sic)8.84, but there were wide variations between farms, and many incurred costs of more than (sic)20 per animal. A majority of the farmers also cited disruption to the farm business or missed market opportunities as costs, but few could estimate their financial cost. Most of the farmers thought that premovement testing was a cost burden on their business, and over half thought It was not an effective policy to control bovine TB.

Estimating the benefits of farm animal welfare legislation using the contingent valuation method

This paper presents the method and findings of a contingent valuation (CV) study that aimed to elicit United Kingdom citizens' willingness to pay to support legislation to phase out the use of battery cages for egg production in the European Union (EU). The method takes account of various biases associated with the CV technique, including 'warm glow', 'part-whole' and sample response biases. Estimated mean willingness to pay to support the legislation is used to estimate the annual benefit of the legislation to UK citizens. This is compared with the estimated annual costs of the legislation over a 12-year period, which allows for readjustment by the UK egg industry. The analysis shows that the estimated benefits of the legislation outweigh the costs. The study demonstrates that CV is a potentially useful technique for assessing the likely benefits associated with proposed legislation. However, estimates of CV studies must be treated with caution. It is important that they are derived from carefully designed surveys and that the willingness to pay estimation method allows for various biases. (C) 2003 Elsevier Science B.V. All rights reserved.