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).

Drift from the use of handheld knapsack pesticide sprayers in Boyacá (Colombian Andes)

Offsite pesticide losses in tropical mountainous regions have been little studied. One example is measuring pesticide drift soil deposition, which can support pesticide risk assessment for surface water, soil, bystanders, off target plants and fauna. This is considered a serious gap, given the evidence of pesticide-related poisoning in those regions. Empirical data of drift deposition of a pesticide surrogate, Uranine tracer, within one of the highest potato producing regions in Colombia, characterized by small plots and mountain orography, is presented. High drift values encountered in our study reflect the actual spray conditions using handled knapsack sprayers. Comparison between measured and predicted drift values using three existing empirical equations showed important underestimation. However, after their optimization based on measured drift information, the equations showed a strong predictive power for this study area and the study conditions. The most suitable curve to assess mean relative drift was the IMAG calculator after optimization.

The development and contribution of surface charge by crop residues in two Malawian acid soils

The effects of maize and soya bean residues on the pH and charge of a loamy sand (Kawalazi) and a sandy clay loam (Naming'omba) from Malawi were measured to determine both the indirect effect of the residues on soil charge through the changes in pH, and the direct contribution of charge carried on the residue surfaces. The soils had pH values (10 mM CaCl2) of 4.3 and 5.0 and organic matter contents were 1.4% and 2.7%, respectively. The clay fractions were dominated by kaolinite and goethite, and mica was present in both samples. The soils were incubated for 28 days with maize (Zea mays) and soya bean (Glycine max) residues. The maximum addition of residue (12.0%) in the Kawalazi and Naming'omba soils increased the pH from 4.3 and 5.0 to 4.8 and 5.3 (maize) and to 9.0 and 8.8 (soya bean), respectively. Negative charge increased from 2.1 and 4.7 cmol(c) kg(-1) to 3.8 and 7.5 (maize) and to 5.3 and 9.3 cmol(c) kg(-1) (soya bean). Positive charge increased from 0.72 and 0.62 to 0.87 and 0.85 cmol(c) kg(-1) (maize) and to 0.75 and 0.68 (soya bean). The charge contribution by the residues was calculated by difference between the charge on a sample incubated with residue and the charge on a soil without residue limed to the same pH value. Up to 100 cmolc negative charge and 10 cmol(c) of positive charge per kg of residue were directly contributed to the soil-residue mixture, the amounts depending on the type of residue, the extent to which the residue was decomposed in the soil and the pH of the mixture. The Anderson and Sposito method [Soil Sci. Soc. Am. J. 55 (1991) 1569] was used to partition the permanent negative charge (holding Cs+) from variable negative charge (holding Li+). In the pH range 3.7-6.5 the maize residue contributed between 3 and 26 cmol(c) of variable charge per kg of residue in the Kawalazi soil and between 6 and 25 cmol(c) per kg of residue in the Naming'omba soil. For soya bean the values were between I and 28 and between 4 and 68 cmolc per kg of residue, respectively. At a given pH value, the charge tended to increase with time of incubation and for a given addition of residue, pH decreased during incubation. Addition of residues contributed no permanent negative charge and the charge on the soil measured by Cs adsorption was independent of pH change caused by the residue showing that the method is valid for soil-residue mixtures. With time there was a decrease in the amount of permanent charge probably due to masking as humic material become adsorbed on mineral surfaces. (C) 2003 Elsevier Science B.V. All rights reserved.

Modelling risk perception and trust in food safety information within the theory of planned behaviour

This study suggests a statistical strategy for explaining how food purchasing intentions are influenced by different levels of risk perception and trust in food safety information. The modelling process is based on Ajzen's Theory of Planned Behaviour and includes trust and risk perception as additional explanatory factors. Interaction and endogeneity across these determinants is explored through a system of simultaneous equations, while the SPARTA equation is estimated through an ordered probit model. Furthermore, parameters are allowed to vary as a function of socio-demographic variables. The application explores chicken purchasing intentions both in a standard situation and conditional to an hypothetical salmonella scare. Data were collected through a nationally representative UK wide survey of 533 UK respondents in face-to-face, in-home interviews. Empirical findings show that interactions exist among the determinants of planned behaviour and socio-demographic variables improve the model's performance. Attitudes emerge as the key determinant of intention to purchase chicken, while trust in food safety information provided by media reduces the likelihood to purchase. (C) 2006 Elsevier Ltd. All rights reserved.

Importance of mycotoxins in food and feed in India

Various food and feed samples including groundnut seed, maize, sorghum, soyabean cake, groundnut cake, cotton cake, poultry feed, buffalo milk, cow milk and milk powders were collected from farmers' fields, farmer's stores, oil millers storage, traders' storage, retail shops and supermarkets. More than 2000 samples were analysed by ELISA and most of the commodities, with the exception of sorghum seed, contained high levels of aflatoxin. Groundnut cake was one of the major cattle feed ingredients in the peri-urban area of Hyderabad (Andhra Pradesh, India) and >75% of the samples contained >100 µg/kg aflatoxin, leading to a high level of aflatoxin M1, in milk samples. Strategies to reduce aflatoxin levels (especially in groundnut) by management interventions at preharvest, harvest and storage, are discussed.

Conformational heterogeneity of tripeptides containing Boc-Leu-Aib as corner residues in the solid state

A critical analysis of single crystal X-ray diffraction studies on a series of terminally protected tripeptides containing a centrally positioned Aib (alpha-aminoisobutyric acid) residue has been reported. For the tripeptide series containing Boc-Ala-Aib as corner residues, all the reported peptides formed distorted type II beta-turn structures. Moreover, a series of Phe substituted analogues ( tripeptides with Boc-Phe-Aib) have also shown different beta-turn conformations. However, the Leu-modified analogues (tripeptides with Boc-Leu-Aib) disrupt the concept of beta-turn formation and adopt various conformations in the solid state. X-ray crystallography sheds some light on the conformational heterogeneity at atomic resolution. (c) 2007 Elsevier Ltd. All rights reserved.

The role of terminal tyrosine residues in the formation of tripeptide nanotubes: a crystallographic insight

Terminally protected acyclic tripeptides containing tyrosine residues at both termini self-assemble into nanotubes in crystals through various non-covalent interactions including intermolecular hydrogen bonds. The nanotube has an average internal diameter of 5 angstrom (0.5 nm) and the tubular ensemble is developed through the hydrogen-bonded phenolic-OH side chains of tyrosine (Tyr) residues [Org. Lett. 2004, 6, 4463]. We have synthesized and studied several tripeptides 3-6 to probe the role of tyrosine residues in nanotube structure formation. These peptides either have only one Tyr residue at N- or C-termini or they have one or two terminally located phenylalanine (Phe) residues. These tripeptides failed to form any kind of nanotubular structure in the solid state. Single crystal X-ray diffraction studies of these peptides 3-6 clearly demonstrate that substitution of any one of the terminal Tyr residues in the Boc-Tyr-X-Tyr-OMe (X=VaI or Ile) sequence disrupts the formation of the nanotubular structure indicating that the presence of two terminally located Tyr residues is vital for nanotube formation. (c) 2006 Elsevier Ltd. All rights reserved.

Kinetic models as a route to control acrylamide formation in food

Reports that heat processing of foods induces the formation of acrylamide heightened interest in the chemistry, biochemistry, and safety of this compound. Acrylamide-induced neurotoxicity, reproductive toxicity, genotoxicity, and carcinogenicity are potential human health risks based on animal studies. Because exposure of humans to acrylamide can come from both external sources and the diet, there exists a need to develop a better understanding of its formation and distribution in food and its role in human health. To contribute to this effort, experts from eight countries have presented data on the chemistry, analysis, metabolism, pharmacology, and toxicology of acrylamide. Specifically covered are the following aspects: exposure from the environment and the diet; biomarkers of exposure; risk assessment; epidemiology; mechanism of formation in food; biological alkylation of amino acids, peptides, proteins, and DNA by acrylamide and its epoxide metabolite glycidamide; neurotoxicity, reproductive toxicity, and carcinogenicity; protection against adverse effects; and possible approaches to reducing levels in food. Cross-fertilization of ideas among several disciplines in which an interest in acrylamide has developed, including food science, pharmacology, toxicology, and medicine, will provide a better understanding of the chemistry and biology of acrylamide in food, and can lead to the development of food processes to decrease the acrylamide content of the diet.

Analytical and compositional aspects of isoflavones in food and their biological effects

This paper provides an overview of analytical techniques used to determine isoflavones (IFs) in foods and biological fluids with main emphasis on sample preparation methods. Factors influencing the content of IFs in food including processing and natural variability are summarized and an insight into IF databases is given. Comparisons of dietary intake of IFs in Asian and Western populations, in special subgroups like vegetarians, vegans, and infants are made and our knowledge on their absorption, distribution, metabolism, and excretion by the human body is presented. The influences of the gut microflora, age, gender, background diet, food matrix, and the chemical nature of the IFs on the metabolism of IFs are described. Potential mechanisms by which IFs may exert their actions are reviewed, and genetic polymorphism as determinants of biological response to soy IFs is discussed. The effects of IFs on a range of health outcomes including atherosclerosis, breast, intestinal, and prostate cancers, menopausal symptoms, bone health, and cognition are reviewed on the basis of the available in vitro, in vivo animal and human data.

Neural network based control in food processing

A number of commonly encountered simple neural network types are discussed, with particular attention being paid to their applicability in automation and control when applied to food processing. In the first instance n-tuple networks are considered, these being particularly useful for high speed production checking operations. Subsequently backpropagation networks are discussed, these being useful both in a more familiar feedback control arrangement and also for such things as recipe prediction.