Combining novel monitoring tools and precision application technologies for integrated high-tech crop protection in the future (a discussion document).

The possibility of combining novel monitoring techniques and precision spraying for crop protection in the future is discussed. A generic model for an innovative crop protection system has been used as a framework. This system will be able to monitor the entire cropping system and identify the presence of relevant pests, diseases and weeds online, and will be location specific. The system will offer prevention, monitoring, interpretation and action which will be performed in a continuous way. The monitoring is divided into several parts. Planting material, seeds and soil should be monitored for prevention purposes before the growing period to avoid, for example, the introduction of disease into the field and to ensure optimal growth conditions. Data from previous growing seasons, such as the location of weeds and previous diseases, should also be included. During the growing season, the crop will be monitored at a macroscale level until a location that needs special attention is identified. If relevant, this area will be monitored more intensively at a microscale level. A decision engine will analyse the data and offer advice on how to control the detected diseases, pests and weeds, using precision spray techniques or alternative measures. The goal is to provide tools that are able to produce high-quality products with the minimal use of conventional plant protection products. This review describes the technologies that can be used or that need further development in order to achieve this goal.

[1]  H. Ramon,et al.  Early Disease Detection in Wheat Fields using Spectral Reflectance , 2003 .

[2]  F. M. Dewey,et al.  Development of a monoclonal antibody‐based immunodetection assay for Botrytis cinerea , 1992 .

[3]  J. C. van de Zande,et al.  A System for Adjusting the Spray Application to the Target Characteristics , 2008 .

[4]  Elaine Ward,et al.  Plant pathogen diagnostics : immunological and nucleic acid-based approaches , 2004 .

[5]  G. Johnson,et al.  Spatial and temporal stability of weed populations over five years , 2000, Weed Science.

[6]  G. Boiteau,et al.  Pneumatic Control of Agricultural Insect Pests , 2001 .

[7]  T. Been,et al.  Distribution Patterns and Sampling , 2006 .

[8]  F. Forcella,et al.  Modeling seedling emergence , 2000 .

[9]  P. Skottrup,et al.  Monoclonal antibodies for the detection of Puccinia striiformis urediniospores. , 2007, Mycological research.

[10]  A. Justesen,et al.  Quantification of Pyrenophora graminea in barley seed using real-time PCR , 2008, European Journal of Plant Pathology.

[11]  C. Zijlstra,et al.  A Multiplex Real-Time Polymerase Chain Reaction (TaqMan) Assay for the Simultaneous Detection of Meloidogyne chitwoodi and M. fallax. , 2006, Phytopathology.

[12]  R. O'Kennedy,et al.  Advances in biosensors for detection of pathogens in food and water , 2003 .

[13]  N. Boonham,et al.  On-Site DNA Extraction and Real-Time PCR for Detection of Phytophthora ramorum in the Field , 2005, Applied and Environmental Microbiology.

[14]  G. Polder Spectral imaging for measuring biochemicals in plant material , 2004 .

[15]  G. Wang,et al.  Specific and sensitive detection of Ralstonia solanacearum in soil with quantitative, real‐time PCR assays , 2009, Journal of applied microbiology.

[16]  B. Thomma,et al.  Real-time PCR for detection and quantification of fungal and oomycete tomato pathogens in plant and soil samples , 2006 .

[17]  R. Jansen,et al.  Release of lipoxygenase products and monoterpenes by tomato plants as an indicator of Botrytis cinerea-induced stress. , 2009, Plant biology.

[18]  R. Kennedy,et al.  A New Method To Monitor Airborne Inoculum of the Fungal Plant Pathogens Mycosphaerella brassicicola andBotrytis cinerea , 2000, Applied and Environmental Microbiology.

[19]  W. S. Lee,et al.  Robotic Weed Control System for Tomatoes , 2004, Precision Agriculture.

[20]  Kennedy,et al.  Production and immunodetection of ascospores of Mycosphaerella brassicicola: ringspot of vegetable crucifers , 1999 .

[21]  G. Basch,et al.  Weed emergence as influenced by soil moisture and air temperature , 2009, Journal of Pest Science.

[22]  N. Schaad,et al.  Real-Time Polymerase Chain Reaction for One-Hour On-Site Diagnosis of Pierce's Disease of Grape in Early Season Asymptomatic Vines. , 2002, Phytopathology.

[23]  J. West,et al.  Detection and quantification of airborne inoculum of Sclerotinia sclerotiorum using quantitative PCR , 2009 .

[24]  S. Christensen,et al.  Real‐time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley , 2003 .

[25]  C. Lévesque,et al.  Identification and Quantification of Pathogenic Pythium spp. from Soils in Eastern Washington Using Real-Time Polymerase Chain Reaction. , 2006, Phytopathology.

[26]  Marie-France Destain,et al.  Analysis of Soil and Crop Properties for Precision Agriculture for Winter Wheat , 2003 .

[27]  A. Rahman,et al.  Correlation between the soil seed bank and weed populations in maize fields , 2006 .

[28]  J. Recasens,et al.  Spatial Distribution and Temporal Stability of Prostrate Knotweed (Polygonum aviculare) and Corn Poppy (Papaver rhoeas) Seed Bank in a Cereal Field , 2009, Weed Science.

[29]  H. G. Diem,et al.  Comparative growth and symbiotic performance of four Acacia mangium provenances from Papua New Guinea in response to the supply of phosphorus at various concentrations , 2004, Biology and Fertility of Soils.

[30]  Alfred Stein,et al.  Are weed patches stable in location? Application of an explicitly two-dimensional methodology , 2007 .

[31]  Enrique Moltó,et al.  PM—Power and Machinery , 2000 .

[32]  A. Pavlou,et al.  Recognition of anaerobic bacterial isolates in vitro using electronic nose technology , 2002, Letters in applied microbiology.

[33]  R. Fischer,et al.  Immunodetection of Venturia inaequalis Ascospores with Phage Antibodies , 2007 .

[34]  Rew,et al.  A stochastic simulation model for evaluating the concept of patch spraying , 1998 .

[35]  Diana H. Wall,et al.  Non‐invasive techniques for investigating and modelling root‐feeding insects in managed and natural systems , 2007 .

[36]  T. Borregaard,et al.  Crop–weed Discrimination by Line Imaging Spectroscopy , 2000 .

[37]  I. Jamaux,et al.  Development of a polyclonal antibody‐based immunoassay for the early detection of Sclerotinia sclerotiorum in rapeseed petals , 1994 .

[38]  G. Schroth,et al.  A method of processing soil core samples for root studies by subsampling , 1994, Biology and Fertility of Soils.

[39]  I. Baldwin,et al.  Volatile signaling in plant-plant-herbivore interactions: what is real? , 2002, Current opinion in plant biology.

[40]  Esmaeil S. Nadimi,et al.  Site‐specific weed control technologies , 2009 .

[41]  R. Frederick,et al.  Advances in molecular-based diagnostics in meeting crop biosecurity and phytosanitary issues. , 2003, Annual review of phytopathology.

[42]  G. Raghavan,et al.  Ultraviolet irradiance to control dry rot and soft rot of potato in storage , 1997 .

[43]  M. Wenneker,et al.  Towards more target oriented crop protection , 2008 .

[44]  M. Chilvers,et al.  A Real-Time, Quantitative PCR Seed Assay for Botrytis spp. that Cause Neck Rot of Onion. , 2007, Plant disease.

[45]  P. Jensen Effect of light environment during soil disturbance on germination and emergence pattern of weeds , 1995 .

[46]  M. Virant-Doberlet,et al.  Vibrational communication in insects , 2004 .

[47]  Annemarie F Justesen,et al.  Rapid determination of Phytophthora infestans sporangia using a surface plasmon resonance immunosensor. , 2007, Journal of microbiological methods.

[48]  Ring T. Cardé,et al.  Insect Pheromone Research , 1997, Springer US.

[49]  David E. Clay,et al.  Spatial distribution, temporal stability, and yield loss estimates for annual grasses and common ragweed (Ambrosia artimisiifolia) in a corn/soybean production field over nine years , 2006, Weed Science.

[50]  H. T. Søgaard,et al.  Application Accuracy of a Machine Vision-controlled Robotic Micro-dosing System , 2007 .

[51]  C. Ballaré,et al.  Photostimulation of seed germination during soil tillage , 1994 .

[52]  Louis Longchamps,et al.  Discrimination of corn, grasses and dicot weeds by their UV-induced fluorescence spectral signature , 2010, Precision Agriculture.

[53]  A. Aharoni,et al.  Genetic Engineering of Terpenoid Metabolism Attracts Bodyguards to Arabidopsis , 2005, Science.

[54]  F. Dessaint,et al.  Nine years' soil seed bank and weed vegetation relationships in an arable field without weed control , 1997 .

[55]  R. Cardé,et al.  Insect Pheromone Research: New Directions , 1997 .

[56]  Peter Ertl,et al.  Microfluidic Systems for Pathogen Sensing: A Review , 2009, Sensors.

[57]  C. Kempenaar,et al.  MLHD online : manual for the herbicide dose calculation module , 2004 .

[58]  N. H. Spliid,et al.  Deposition of spray liquid on the soil below cereal crops after applications during the growing season , 2003 .

[59]  J. Byers,et al.  Potential of Mass Trapping for Long-Term Pest Management and Eradication of Invasive Species , 2006, Journal of economic entomology.

[60]  R. Gerhards,et al.  Practical experiences with a system for site‐specific weed control in arable crops using real‐time image analysis and GPS‐controlled patch spraying , 2006 .

[61]  M. Garbelotto,et al.  Detection and Quantification of Airborne Conidia of Fusarium circinatum, the Causal Agent of Pine Pitch Canker, from Two California Sites by Using a Real-Time PCR Approach Combined with a Simple Spore Trapping Method , 2004, Applied and Environmental Microbiology.

[62]  P. Skottrup,et al.  Towards on-site pathogen detection using antibody-based sensors. , 2008, Biosensors & bioelectronics.

[63]  D. Ehlert,et al.  Variable-rate fungicide spraying in cereals using a plant cover sensor , 2006, Precision Agriculture.

[64]  H. Jones,et al.  Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. , 2007, Journal of experimental botany.

[65]  J. González-Andújar,et al.  Predicting weed emergence in maize crops under two contrasting climatic conditions , 2009 .

[66]  H. Jalink,et al.  Correcting and matching time sequence images of plant leaves using Penalized Likelihood Warping and Robust Point Matching , 2007 .

[67]  David C. Slaughter,et al.  Multispectral Machine Vision Identification of Lettuce and Weed Seedlings for Automated Weed Control , 2008, Weed Technology.

[68]  J. A. Bokx,et al.  Aphid trapping in potato fields and transmission of potato virus YN. , 1985 .

[69]  Julian W. Gardner,et al.  A brief history of electronic noses , 1994 .

[70]  M. Garbelotto,et al.  Detection and quantification of Leptographium wageneri, the cause of black-stain root disease, from bark beetles (Coleoptera: Scolytidae) in Northern California using regular and real-time PCR , 2005 .

[71]  A. Hajek,et al.  A review of introductions of pathogens and nematodes for classical biological control of insects and mites , 2007 .

[72]  J. Byers,et al.  Potential of “Lure and Kill” in Long-Term Pest Management and Eradication of Invasive Species , 2009, Journal of economic entomology.

[73]  Benjamin J Hindson,et al.  APDS: the autonomous pathogen detection system. , 2005, Biosensors & bioelectronics.

[74]  David C. Slaughter,et al.  HERBICIDE MICRO-DOSING FOR WEED CONTROL IN FIELD-GROWN PROCESSING TOMATOES , 2004 .

[75]  I. M. Scotford,et al.  Combination of Spectral Reflectance and Ultrasonic Sensing to monitor the Growth of Winter Wheat , 2004 .

[76]  J. M. Blanco-Moreno,et al.  Spatial and temporal patterns of Lolium rigidum–Avena sterilis mixed populations in a cereal field , 2006 .

[77]  Christian Germain,et al.  Transformation of high resolution aerial images in vine vigour maps at intra-block scale by semi automatic image processing , 2007 .

[78]  Paul Leonard,et al.  Detection of fungal spores using a generic surface plasmon resonance immunoassay. , 2007, Biosensors & bioelectronics.

[79]  H. A. Mccartney,et al.  Sampling bioaerosols in plant pathology , 1997 .

[80]  R. Hall,et al.  Biosensor technologies for detecting microbiological foodborne hazards. , 2002, Microbes and infection.

[81]  W. Donald A degree-day model of Cirsium arvense shoot emergence from adventitious root buds in spring , 2000, Weed Science.

[82]  S. Welter,et al.  Pheromone mating disruption offers selective management options for key pests , 2005 .

[83]  Jean Emberlin,et al.  PCR to predict risk of airborne disease. , 2008, Trends in microbiology.

[84]  K. Gindro,et al.  Development of a TaqMan real-time PCR assay for quantification of airborne conidia of Botrytis squamosa and management of botrytis leaf blight of onion. , 2009, Phytopathology.

[85]  H. Ganzelmeier,et al.  The International (BCPC) spray classification system including a drift potential factor , 1998 .

[86]  T. Michailides,et al.  Quantification of airborne spores of Monilinia fructicola in stone fruit orchards of California using real-time PCR , 2007, European Journal of Plant Pathology.

[87]  J. De Baerdemaeker,et al.  Weed Detection Using Canopy Reflection , 2002, Precision Agriculture.

[88]  A. T. Nieuwenhuizen,et al.  Automated detection and control of volunteer potato plants , 2009 .