Precision agriculture

 
 

Overview

 
Precision agriculture or site specific management consider agricultural activities in variety of scales smaller than the field scale all the way down to specific plants. It can be defined as implementing the right treatment at the right time and only where needed. Therefore, remote sensing by its versatile spectral, spatial and temporal resolutions can provide tools for monitoring agricultural lands. Applying remote sensing tools for precision agriculture aims can be beneficial economically as well as environmentally. Here are several topics that were and are studied in our lab:

The yield of dryland wheat is limited by rainfall, therefore, a decision support system (DSS) for improving economic profit and reducing environmental impact was developed. The quick and simplified DSS was developed and it results in the ability to decide whether to apply nitrogen to grain harvest, no additional fertilization and leave for grain harvest or harvest hay (Bonfil et al. 2004). The conditions during the heading stage are reflected in final grain quality and yield. Therefore, in order to apply this DSS on the effective growth stage for analysis the normalized heading index (NHI) was developed (Pimstein et al. 2009).
 
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Nitrogen (N) is an essential element in plant growth and productivity, and N fertilizer is therefore of prime importance in cultivated crops. The amount and timing of N application has economic and environmental implications and is consequently considered to be an important issue in precision agriculture. The majority of the known N spectral indices are indirectly related to N by using chlorophyll sensitive bands. The 1510 nm band is directly related to N content since it is an overtone of the connection between N and hydrogen atoms. It was concluded that combining direct and indirect relation to N content results in better assessment of N content of above ground potato plants (Herrmann et al. 2010).
 
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The importance of potassium (K) and phosphorus (P) contents to wheat yield and grain quality, and the very little experience that has been gained on nutritional monitoring of other than nitrogen using remotely sensed technologies, a study was undertaken to explore the possibility of identifying these mineral stresses using spectral data. Based on the current experiments and observations it can be concluded that monitoring K and P contents of wheat crops can be performed using remotely sensed data. However, the level of accuracy and the type of information that can be retrieved depend on the type of sensor and algorithm to be used, as well as on the kind of monitoring that is required to be implemented (Pimstein et al. 2011).
 
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Leaf Area Index (LAI) is an important variable that governs canopy processes. LAI assessment by the band formations of Vegetation and Environmental New micro Spacecraft (VENμS) and Sentinel-2 was explored for potato and wheat fields. Both satellites contain red-edge bands. Since the red-edge inflection point (REIP) is linearly related to LAI values it was concluded that it can assess LAI better than NDVI. Therefore the importance for the future satellites mentioned above (Herrmann et al. 2011).
 
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Weed control is commonly performed by applying selective herbicides homogeneously over entire agricultural fields. However, applying herbicide only where needed could have economic and environmental benefits. Spectral separation between crops and weed has resulted in almost perfect classification results. Spectral separation based on canopy has resulted in similar results (Shapira et al. 2013). Applying hyperspectral imaging on ground level has resulted in spatial separation. The red-edge band was the most important region for the separation models (Herrmann et all.2013).
 
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Spider Two spotted spider mites (TSSM; Tetranychus urticae Koch) cause significant damage to crops and yields, in the field as well as in greenhouses. By feeding, TSSM destroy chloroplast-containing cells; this damage can be spectrally detected in the reflectance of the visible and near-infrared regions. Several spectral indices were calculated and it was resulted that spectral data can separate between early damage and less indices can separate between damage levels (Herrmann et all. 2012). These data now days are analyzed for continuous spectra and preliminary results show ability to separate damage levels by continuous spectra.
 
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Papers

 
 
 
 
 
French, A., Hunsaker, D., Bounoua, L., Karnieli, A., Luckett, W. and Strand, R. 2018. Remote sensing of evapotranspiration over the central Arizona irrigation & drainage district, USA. Agronomy. 8. doi:10.3390/agronomy8120278
 
 
 
 

 
 
 
 
 
Pincovici, S., Cochavi, A., Karnieli, A., Ephrath, J., and Rachmilevitch, S. 2018. Source-sink relations of sunflower plants as affected by a parasite modifies carbon allocations and leaf traits. Plant Science. 271, 100-107. https://doi.org/10.1016/j.plantsci.2018.03.022.
 
 
 
 

 
 
 
 
 
Matzrafi, M. Herrmann, I., Nansen, N., Kliper, T., Zait, Y., Ignat, T., Siso, D., Rubin, R., Karnieli, A., Eizenberg, H. 2017. Hyperspectral technologies for assessing seed germination and trifloxysulfuron-methyl response in Amaranthus palmeri (Palmer amaranth). Frontiers in Plant Science, section Agroecology and Land Use Systems, 8 (article 474) doi: 10.3389/fpls.2017.00474.
 
 
 
 

 
 
 
 
 
Rapaport, T., Hochberg, U., Cochavi, A., Karnieli, A. and Rachmilevitch, S. 2017. The potential of the spectral ‘water balance index’ (WABI) for crop irrigation scheduling. New Phytologist. doi: 10.1111/nph.14718.
 
 
 
 

 
 
 
 
 
Cochavi, A., Rapaport, R., Gendler, T., Karnieli, A., Eizenberg, H., Rachmilevitch, S., and Ephrath, J.E. 2017. Recognition of Orobanche cumana below-ground parasitism through physiological and hyper spectral measurements in sunflower (Helianthus annuus L.). Frontiers in Plant Science, 8 (article 909) doi: 10.3389/fpls.2017.00909.
 
 
 
 

 
2015
 
 
 
 
 
Rapaport T., Hochberg, U., Shoshany, M., Karnieli, A. and Rachmilevitch, S. 2015. Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for the assessment of grapevine water status. ISPRS Journal of Photogrammetry and Remote Sensing. 109, 88-97.
 
 
 
 

 
2014
 
 
 
 
 
Nguy-Robertson, A.L., Peng, Y., Gitelson, A., Arkebauer, T.J., Pimstein, A., Herrmann, I., Karnieli, A., Rundquist, D.C. and Bonfil, D.J. 2014. Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm. Agricultural and Forest Meteorology. 192-193, 140-148.
 
 
 
 

 
 
 
 
 
Rapaport, T., Hochberg, U., Rachmilevitch, S. and Karnieli, A. 2014. The effect of differential growth rates across plants on spectral predictions of physiological parameters. PLOS ONE. E 9, e88930.doi:10.1371/journal.pone.0088930.
 
 
 
 

 
2013
 
 
 
 
 
Herrmann, I., Shapira, U., Kinast, S., Karnieli, A. and Bonfil, D.J. 2013. Ground-level hyperspectral imagery for detecting weeds in wheat fields. Precision Agriculture. DOI 10.1007/s11119-013-9321-x.
 
 
 
 

 
 
 
 
 
Shapira, U., Herrmann, I., Karnieli, A., Bonfil, D.J. 2013. Field spectroscopy for weed detection in wheat and chickpea fields. International Journal of Remote Sensing, 34, 6094-6108.
 
 
 
 

 
2012
 
 
 
 
 
Herrmann, I., Berenstein, M., Sade, A., Karnieli, A., Bonfil, D.J. and Weintraub, P.G. 2012. Spectral monitoring of two-spotted spider mite damage to pepper leaves. Remote Sensing Letters. 3, 277–283.
 
 
 
 

 
2011
 
 
 
 
 
Herrmann, I., Pimstein, A., Karnieli, A., Cohen, Y., Alchanatis, V. and Bonfil, D.J. 2011. LAI assessment of wheat and potato crops by VENµS and Sentinel-2 bands. Remote Sensing of the Environment. 115, 2141–2151.
 
 
 
 

 
 
 
 
 
Pimstein, A., Karnieli, A., Bansal, S.K. and Bonfil, D.J. 2011. Exploring remotely sensed technologies for monitoring wheat potassium and phosphorus using field spectroscopy. Field Crops Research. 121, 125-135.
 
 
 
 

 
 
 
 
 
Cohen, Y., Alchanatis, V., Zusman Y., Dar, Z., Bonfil, D.J., Karnieli, A.., Zilberman, A., Moulin, A., Ostrovsky, V., Levi, A.., Brikman, R. and Shenker, M. 2010. Leaf nitrogen estimation in potato based on spectral data and on simulated bands of the VENµS satellite. Precision Agriculture, 11, 520-537.
 
 
 
 

 
 
 
 
 
Herrmann, I., Karnieli, A., Bonfil, D.J., Cohen, Y. and Alchanatis, V. 2010. SWIR-based spectral indices for assessing nitrogen content in potato fields. International Journal of Remote Sensing, 31, 5127-5143.
 
 
 
 

 
 
 
 
 
Legarrea, S., Karnieli, A., Fereres1, A. and Weintraub, P.G. 2010. Comparison of UV-absorbing nets in pepper crops: Spectral properties, effects on plants and pest control. Photochemistry and Photobiology. 86, 324–330.
 
 
 
 

 
 
 
 
 
Pimstein, A., Eitel, J., Long, D., Mufradid, I., Karnieli, A. and Bonfil, D.J. 2009. Spectral index to monitor the head-emergence of wheat in semi-arid conditions. Field Crops Research, 111, 218-225.
 
 
 
 

 
 
 
 
 
Pimstein, A., Karnieli, A., Bonfil, D.J. 2007. Wheat and maize monitoring based on ground spectral measurements and multivariate data analysis. Journal of Applied Remote Sensing. 1, 013530.
 
 
 
 

 
 
 
 
 
Bonfil, D.J., Karnieli, A., Raz, M., Mufradi. I, Asido, S., Egozi, H., Hoffman, A. and Schmilovitch, Z. 2005. Rapid assessing of water and nitrogen status in wheat flag leaves. Journal of Food, Agriculture & Environment, 3, 207-212.
 
 
 
 

 
 
 
 
 
Bonfil, D.J., Karnieli, A., Raz, M., Mufradi, I., Asido, S., Egozi, H., Hoffman, A. and Schmilovitch, Z. 2004. Decision support system for improving wheat grain quality in the Mediterranean area of Israel. Field Crops Research, 89, 153-163.
 
 
 
 

 
 
 
 
 
Ben-Dor, E., Goldshalager, N., Braun, O., Kindel, B., Goetz, A.F.H., Bonfil, D., Agassi, M., Margalit, N., Binayminy, Y. and Karnieli, A. 2004. Monitoring of infiltration rate in semiarid soils using airborne hyperspectral technology. International Journal of Remote Sensing, 25, 2607-2624.
 
 
 
 

 
 
 
 
 
Ben-Dor, E., Patkin, K., Banin, A. and Karnieli, A. 2002. Mapping of several soil properties using DAIS-7915 hyperspectral scanner data: A case study over clayey soils in Israel. International Journal of Remote Sensing, 23, 1043-1062.