Aiming at decision making in plant disease protection and phenotyping by the use of optical sensors

被引:38
作者
Kuska, M. T. [1 ]
Mahlein, A. -K. [1 ,2 ]
机构
[1] Rheinische Friedrich Wilhelms Univ Bonn, Inst Crop Sci & Resource Conservat INRES, Plant Dis & Plant Protect, Nussallee 9, D-53115 Bonn, Germany
[2] Inst Sugar Beet Res IfZ, Holtenser Landstr 77, D-37079 Gottingen, Germany
关键词
Hyperspectral imaging; Precision agriculture; Plant phenotyping; PRECISION; PHENOMICS; DYNAMICS; PATTERNS; INDEXES;
D O I
10.1007/s10658-018-1464-1
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Protection of crops against plant diseases is crucial in crop production. Agricultural practice and scientific research is confronted with new challenges. Environmentally friendly and sustainable solutions are increasingly demanded. Therefore, the precise detection of primary infection sites and disease dynamics is fundamental to make a decision for a subsequent management practice. In this context, optical sensors can provide an accurate and objective detection of plant diseases. This has awoken the interest and expectation from the public, farmers, and companies for sophisticated optical sensors in agriculture, providing promising solutions. Nevertheless, the application of optical sensors in a practical context in the field is still challenging, and sophisticated data analysis methods have to be developed. In general, the entire system pipeline, consisting of the type of sensor, the platform carrying the sensor, and the decision making process by data analysis has to be tailored to the specific problem. Here, we briefly recount the possibilities and challenges using optical sensors in research and practice for plant disease protection. Optical sensor-based approaches are considered as a key element in plant phenotyping. This overview addresses mainly hyperspectral imaging as it determines several plant parameters that represent the basis for more specific sensors in the future.
引用
收藏
页码:987 / 992
页数:6
相关论文
共 39 条
  • [1] Impact of primary infection site of Fusarium species on head blight development in wheat ears evaluated by IR-thermography
    Al Masri, A.
    Hau, B.
    Dehne, H. -W.
    Mahlein, A. -K.
    Oerke, E. -C.
    [J]. EUROPEAN JOURNAL OF PLANT PATHOLOGY, 2017, 147 (04) : 855 - 868
  • [2] Development and evaluation of a field-based high-throughput phenotyping platform
    Andrade-Sanchez, Pedro
    Gore, Michael A.
    Heun, John T.
    Thorp, Kelly R.
    Carmo-Silva, A. Elizabete
    French, Andrew N.
    Salvucci, Michael E.
    White, Jeffrey W.
    [J]. FUNCTIONAL PLANT BIOLOGY, 2014, 41 (01) : 68 - 79
  • [3] Non-invasive Presymptomatic Detection of Cercospora beticola Infection and Identification of Early Metabolic Responses in Sugar Beet
    Arens, Nadja
    Backhaus, Andreas
    Doell, Stefanie
    Fischer, Sandra
    Seiffert, Udo
    Mock, Hans-Peter
    [J]. FRONTIERS IN PLANT SCIENCE, 2016, 7
  • [4] The global spread of crop pests and pathogens
    Bebber, Daniel P.
    Holmes, Timothy
    Gurr, Sarah J.
    [J]. GLOBAL ECOLOGY AND BIOGEOGRAPHY, 2014, 23 (12): : 1398 - 1407
  • [5] Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection
    Behmann, Jan
    Acebron, Kelvin
    Emin, Dzhaner
    Bennertz, Simon
    Matsubara, Shizue
    Thomas, Stefan
    Bohnenkamp, David
    Kuska, Matheus T.
    Jussila, Jouni
    Salo, Harri
    Mahlein, Anne-Katrin
    Rascher, Uwe
    [J]. SENSORS, 2018, 18 (02)
  • [6] Generation and application of hyperspectral 3D plant models: methods and challenges
    Behmann, Jan
    Mahlein, Anne-Katrin
    Paulus, Stefan
    Dupuis, Jan
    Kuhlmann, Heiner
    Oerke, Erich-Christian
    Pluemer, Lutz
    [J]. MACHINE VISION AND APPLICATIONS, 2016, 27 (05) : 611 - 624
  • [7] A review of advanced machine learning methods for the detection of biotic stress in precision crop protection
    Behmann, Jan
    Mahlein, Anne-Katrin
    Rumpf, Till
    Roemer, Christoph
    Pluemer, Lutz
    [J]. PRECISION AGRICULTURE, 2015, 16 (03) : 239 - 260
  • [8] Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging
    Bock, C. H.
    Poole, G. H.
    Parker, P. E.
    Gottwald, T. R.
    [J]. CRITICAL REVIEWS IN PLANT SCIENCES, 2010, 29 (02) : 59 - 107
  • [9] Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data:: Non-parametric statistical approaches and physiological implications
    Delalieux, Stephanie
    van Aardt, Jan
    Keulemans, Wannes
    Schrevens, Eddie
    Coppin, Pol
    [J]. EUROPEAN JOURNAL OF AGRONOMY, 2007, 27 (01) : 130 - 143
  • [10] Future Scenarios for Plant Phenotyping
    Fiorani, Fabio
    Schurr, Ulrich
    [J]. ANNUAL REVIEW OF PLANT BIOLOGY, VOL 64, 2013, 64 : 267 - 291