Coupling Waveguide-Based Micro-Sensors and Spectral Multivariate Analysis to Improve Spray Deposit Characterization in Agriculture

The leaf coverage surface is a key measurement of the spraying process to maximize spray efficiency. To determine leaf coverage surface, the development of optical micro-sensors that, coupled with a multivariate spectral analysis, will be able to measure the volume of the droplets deposited on their surface is proposed. Rib optical waveguides based on Ge-Se-Te chalcogenide films were manufactured and their light transmission was studied as a response to the deposition of demineralized water droplets on their surface. The measurements were performed using a dedicated spectrophotometric bench to record the transmission spectra at the output of the waveguides, before (reference) and after drop deposition, in the wavelength range between 1200 and 2000 nm. The presence of a hollow at 1450 nm in the relative transmission spectra has been recorded. This corresponds to the first overtone of the O–H stretching vibration in water. This result tends to show that the optical intensity decrease observed after droplet deposition is partly due to absorption by water of the light energy carried by the guided mode evanescent field. The probe based on Ge-Se-Te rib optical waveguides is thus sensitive throughout the whole range of volumes studied, i.e., from 0.1 to 2.5 μL. Principal Component Analysis and Partial Least Square as multivariate techniques then allowed the analysis of the statistics of the measurements and the predictive character of the transmission spectra. It confirmed the sensitivity of the measurement system to the water absorption, and the predictive model allowed the prediction of droplet volumes on an independent set of measurements, with a correlation of 66.5% and a precision of 0.39 μL.

[1]  André R. S. Marçal,et al.  Image Processing of Artificial Targets for Automatic Evaluation of Spray Quality , 2008 .

[2]  Caroline Vigreux,et al.  Channel waveguides based on thermally co-evaporated Te–Ge–Se films for infrared integrated optics , 2013 .

[3]  E. Hilz,et al.  Spray drift review: The extent to which a formulation can contribute to spray drift reduction , 2013 .

[4]  K. Helming,et al.  Rebound effects in agricultural land and soil management: Review and analytical framework , 2019, Journal of Cleaner Production.

[5]  R. Barnes,et al.  Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra , 1989 .

[6]  Juan David Sesquile,et al.  Assessment of pesticide application quality with a manual sprayer in spinach , 2016 .

[7]  Singh,et al.  Advance breeding and biotechnological approaches for crop improvement: A review , 2019 .

[8]  A. Pradel,et al.  Chalcogenide circuits for the realization of CO2 micro-sensors operating at 4.23 µm , 2016, International Conference on Transparent Optical Networks.

[9]  Caroline Vigreux,et al.  Chalcogenide rib waveguides for the characterization of spray deposits , 2018, Optical Materials.

[10]  D. Markovich,et al.  Interferometric technique for measurement of droplet diameter , 2011 .

[11]  A. Birch,et al.  Networking of integrated pest management: A powerful approach to address common challenges in agriculture , 2016 .

[12]  P. Balsari,et al.  Toward a new method to classify the airblast sprayers according to their potential drift reduction: comparison of direct and new indirect measurement methods. , 2019, Pest management science.

[13]  K. A. Huntington,et al.  The use of a water sensitive dye for the detection and assessment of small spray droplets , 1970 .

[14]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[15]  R. C. Derksen,et al.  Visual and Image System Measurement of Spray Deposits Using Water-Sensitive Paper , 2003 .

[16]  H. Runhaar,et al.  Assessment of policy instruments for pesticide use reduction in Europe; Learning from a systematic literature review , 2019, Crop Protection.

[17]  R. Zengerle,et al.  Non-contact optical sensor to detect free flying droplets in the nanolitre range , 2010 .

[18]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[19]  Caroline Vigreux,et al.  Wide-range transmitting chalcogenide films and development of micro-components for infrared integrated optics applications , 2014 .

[20]  M. Forina,et al.  Multivariate calibration. , 2007, Journal of chromatography. A.

[21]  S. Wold Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[22]  Emanuele Cerruto,et al.  A model to estimate the spray deposit by simulated water sensitive papers , 2019, Crop Protection.

[23]  C. Nansen,et al.  Optimizing pesticide spray coverage using a novel web and smartphone tool, SnapCard , 2015, Agronomy for Sustainable Development.

[24]  Joe D. Luck,et al.  Development and Preliminary Evaluation of a Spray Deposition Sensing System for Improving Pesticide Application , 2015, Sensors.

[25]  A. Miranda-Fuentes,et al.  Assessing the influence of air speed and liquid flow rate on the droplet size and homogeneity in pneumatic spraying. , 2018, Pest management science.

[26]  Jian Wang,et al.  An Intelligent Vision Based Sensing Approach for Spraying Droplets Deposition Detection , 2019, Italian National Conference on Sensors.

[27]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[28]  Emilio Gil,et al.  Spray distribution evaluation of different settings of a hand-held-trolley sprayer used in greenhouse tomato crops. , 2016, Pest management science.