Remote sensing technology and its applications in management of plant pests and diseases
Subject Areas : Plant Pests
Azam Taheri Shahrestani
1
*
,
Somayeh Sefidgar Shakolaie
2
1 - Laboratory Expert, Faculty of Agriculture, University of Guilan, Guilan, Iran
2 - Laboratory Expert, Faculty of Agriculture, University of Guilan, Guilan, Iran
Keywords: Remote sensing, plant disease, pest, sensor, wavelength,
Abstract :
Given the increasing population growth, reducing agricultural losses caused by pests and plant diseases has great importance. In this regard, using appropriate preventive methods is an effective step in timely protection of agricultural products and also reducing the use of chemical pesticides. In recent years, advances in remote sensing techniques have made this science play a very important role in plant pest and disease management. Remote sensing can help identifying, diagnosing, and controlling plant pests and diseases, as well as stress caused by water or nutrient deficiencies. By combining remote sensing data and agricultural knowledge, it is possible to prevent the impact of a disease or pest on crops by providing early warning and taking appropriate measures at an early stage. This technology plays an important role in the management of plant pests and diseases by providing accurate and efficient data, and helps in early identification of infected areas, continuous monitoring of plant health, and reducing the use of harmful chemical pesticides. This article discusses the importance of remote sensing in controlling plant pests and diseases and examples of remote sensing for their monitoring.
زرقاني، ا.، فرآشیانی، م ا. و امینی، س. 1401. روش¬های مختلف پایش آفات و بیماري‌ها در عرصه‌هاي جنگلی و مرتعی. طبیعت ایران 7 (1): 33-44.
صادقی، و. و طریقی، ج. 1396. کاربرد فناوری سنجش از دور زمینی، هوایی و ماهواره¬ای در صنعت کشاورزی. پنجمین کنفرانس ملی و اولین کنفرانس بین¬المللی کشاورزی ارگانیک و مرسوم، اردبیل.
کشتکار، ح. و نعمت اللهی، م. 1396. کاربرد سنجش از دور در علوم طبیعی. انتشارات نونگارش نوین. تهران. 250 صفحه.
شریعتی نیا، ل. و کشتکار، ح. 1402. كاربرد سنجش از دور در پایش آفات و بیماری‌های گیاهي. نشريه دانشجويي زيست سپهر 16 (2): 18-31.
Abdel, W.H., Aboelghar, M., Ali, A.M. and Yones, M. 2017. Spectral and molecular studies on gray mold in strawberry. Asian Journal of Plant Pathology 11(4): 167-173.
Abd El-Ghany, N.M., Abd El-Aziz, S.E. and Marei, S.S. 2020. A review: application of remote sensing as a promising strategy for insect pests and diseases management. Environmental Science and Pollution Research 27: 33503-33515. https://doi.org/10.1007/s11356-020-09517-2.
Aboelghar, M. and Abdel Wahab, H. 2013. Spectral footprint of Botrytis cinerea, a novel way for fungal characterization. Advances in Bioscience and Biotechnology 4: 374-382. doi:10.4236/abb.2013.43050.
Albetis, J., Duthoit, S., Guttler, F., Jacquin, A., Goulard, M., Poilvé, H., Féret, J.B. and Dedieu, G. 2017. Detection of Flavescence dorée grapevine disease using Unmanned Aerial Vehicle (UAV) multispectral imagery. Remote Sensing 9(4): 308. https://doi.org/10.3390/rs9040308.
Aggarwal, S. 2004. Principles of remote sensing. Proceedings of Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, India. Pp. 23–38.
Apan, A., Held, A., Phinn, S. and Markley, J. 2004. Detecting sugarcane ‘orange rust’ disease using EO-1Hyperion hyperspectral imagery. International Journal of Remote Sensing 25: 489-498.
Bellvert, J., Zarco-Tejada, P.J., Girona, J. and Fereres, E. 2014. Mapping crop water stress index in a ‘Pinot-noir’vineyard: Comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precision Agriculture 15(4): 361-376. https://doi.org/10.1007/s11119-013-9334-5.
Berdugo, C.A., Zito, R., Paulus, S. and Mahlein, A.K. 2014. Fusion of sensor data for the detection and differentiation of plant diseases in cucumber. Plant Pathology 63: 1344-1356. https://doi.org/10.1111/ppa.12219.
Blackburn, G.A. and Steele, C.M. 1999. Towards the remote sensing of matorral vegetation physiology: relationships between spectral reflectance, pigment and biophysical characteristics of semi-arid bush land canopies. Remote Sensing of Environment 70: 278–292.
Bock, C.H., Parker, P.E, Cook, A.Z. and Gottwald, T.R. 2008. Visual rating and the use of image analysis for assessing different symptoms of citrus canker on grapefruit leaves. Plant Disease 92: 530-541. https://doi.org/10.1094/pdis-92-4-0530.
Bravo, C., Moshou, D., West, J., McCartney, A. and Ramon, H. 2003. Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering 84: 137-145. https://doi.org/10.1016/S1537-5110(02)00269-6.
Burling, K., Hunsche, M. and Noga, G. 2011. Use of blue-green and chlorophyll fluorescence measurements for differentiation between nitrogen deficiency and pathogen infection in wheat. Journal of Plant Physiology 168: 1641-1648.
Calderón, R., Navas-Cortés, J.A., Lucena, C. and Zarco-Tejada, P.J. 2013. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing of Environment 139: 231-245.
Calderón, R., Navas-Cortés, J.A. and Zarco-Tejada, P.J. 2015. Early detection and quantification of Verticillium wilt in olive using hyperspectral and thermal imagery over large areas. Remote Sensing 7(5): 5584-5610. https://doi.org/10.3390/rs70505584.
Camargo, A. and Smith, J.S. 2009. Image pattern classification for the identification of disease causing agents in plants. Computers and Electronics in Agriculture 66: 121-125.
Chaerle, L., Lenk, S., Hagenbeek, D., Buschmann, C. and Straeten, D.V.D. 2004. Multicolor fluorescence imaging for early detection of the hypersensitive reaction to tobacco mosaic virus. Journal of Plant Physiology 164: 253-262.
Chaerle, L., Hagenbeek, D., De Bruyne, E. and Van der Straeten, D. 2007. Chlorophyll fluorescence imaging for disease-resistance screening of sugar beet. Plant Cell, Tissue and Organ culture 91: 97-106.
Corti, M., Gallina, P.M., Cavalli, D. and Cabassi, G. 2017. Hyperspectral imaging of spinach canopy under combined water and nitrogen stress to estimate biomass, water, and nitrogen content. Biosystems Engineering 158: 38-50.
Deleon, L., Brewer, M.J., Esquivel, I.L. and Halcomb, J. 2017. Use of a geographic information system to produce pest monitoring maps for south Texas cotton and sorghum land managers. Crop Protection 101: 50-57. https://doi.org/10.1016/j.cropro.2017.07.016.
Gamal, E., Khdery, G., Morsy, A., El-Sayed, M., Hashim, A. and Saleh, H. 2020. Using GIS based modelling to aid conservation of two endangered plant species (Ebenus Armitagei and Periploca Angustifolia) at Wadi Al-Afreet, Egypt. Remote Sensing Applications: Society and Environment 19: 100336.
Gitelson, A.A., Merzlyak, M.N. and Chivkunova, O.B. 2001. Optical properties and nondestructive estimation of anthocyanin content in plants leaves. Photochemistry and Photobiology 74: 38-45.
Gogoi, N.K., Deka, B. and Bora, L.C. 2018. Remote sensing and its use in detection and monitoring plant diseases: a review. Agricultural Reviews 39(4): 307-313. doi:10.18805/ag.R-1835.
Hatfield, J.L. and Pinter, P.J. 1993. Remote sensing for crop protection. Crop Protection 12: 403–413.
Herrmann, I., Berenstein, M., Paz-Kagan, T., Sade, A. and Karnieli, A. 2017. Spectral assessment of two-spotted spider mite damage levels in the leaves of greenhouse-grown pepper and bean. Biosystems Engineering 157: 72-85. https://doi.org/10.1016/j.biosystemseng.2017.02.008.
Hillnhutter, C., Mahlein, A.K., Sikora, R.A. and Oerke, E.C. 2011. Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields. Field Crops Research 122: 70–77. https://doi.org/10.1016/j.fcr.2011.02.007.
Iost Filho, F.H., Heldens, W.B., Kong, Z. and de Lange, E.S. 2020. Drones: innovative technology for use in precision pest management. Journal of Economic Entomology 113(1): 1-25. https://doi.org/10.1093/jee/toz268.
Jiang, J.A., Tseng, C.L., Lu, F.M., Yang, E.C., Wu, Z.S. and Chen, C.P. 2008. A GSM-based remote wireless automatic monitoring system for field information: a case study for ecological monitoring of oriental fruit fly, Bactrocera dorsalis (Hendel). Computer and Electronics in Agriculture 62: 243-259. https://doi.org/10.1016/j.compag.2008.01.005.
Khdery, G., Frag, E. and Arafat, S. 2019. Natural vegetation cover discrimination using hyperspectral data in Wadi Hagul, Egypt. Egyptian Journal of Remote Sensing and Space Science 22: 253-262.
Konanz, S., Kocsanyi, L. and Buschmann, C. 2014. Advanced multi-color fluorescence imaging system for detection of biotic and abiotic stresses in leaves. Agriculture 4: 79-95.
Kuska, M., Wahabzada, M., Leucker, M., Dehne, H. W., Kersting, K., Oerke, E.C., Steiner, U. and Mahlein, A. K. 2015. Hyperspectral phenotyping on microscopic scale – towards automated characterization of plant-pathogen interactions. Plant Methods 11: 28. https://doi.org/10.1186/s13007-015-0073-7.
Kuska, M.T. and Mahlein, A.K. 2018. Aiming at decision making in plant disease protection and phenotyping by the use of optical sensors. European Journal of Plant Pathology 152(4): 987-992.
López-López, M., Calderón, R., González-Dugo, V., Zarco-Tejada, P. and Fereres, E. 2016. Early detection and quantification of almond red leaf blotch using high-resolution hyperspectral and thermal imagery. Remote Sensing 8(4): 276. https://doi.org/10.3390/rs8040276.
Mahlein, A.K. 2016. Plant disease detection by imaging sensors-Parrels and specific demands for precision agriculture and plant phenotyping. Plant Disease 100: 241-251. https://doi.org/10.1094/pdis-03-15-0340-fe.
Mahlein, A.K., Steiner, U., Dehne, H.W. and Oerke, E.C. 2010. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agriculture 11: 413-431.
Mahlein, A.K., Steiner, U., Hillnhütter, C., Dehne, H.W. and Oerke, E.C. 2012. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet disease. Plant Methods 8: 3. https://doi.org/10.1186/1746-4811-8-3.
Martin, D.E. and Latheef, M.A. 2017. Remote sensing evaluation of two-spotted spider mite damage on greenhouse cotton. Journal of Visualized Experiments 122: e54314.
Moghimi, A., Pourreza, A., Zuniga-Ramirez, G., Williams, L.E. and Fidelibus, M.W. 2020. A novel machine learning approach to estimate grapevine leaf nitrogen concentration using aerial multispectral imagery. Remote Sensing 12(21): 3515. https://doi.org/10.3390/rs12213515.
Moran, M.S., Inoue, Y. and Barnes, E.M. 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment 61: 319-346.
Moshou, D., Bravo, C., West, J., Wahlen, S., McCartney, A. and Ramon, H. 2004. Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture 44: 173-188.
Neumann, M., Hallau, L., Klatt, B., Kersting, K. and Bauckhagem C. 2014. Erosion band features for cell phone image based plant disease classification. Proceeding of the 22nd International Conference on Pattern Recognition (ICPR), Stockholm, Sweden. pp: 3315-3320.
Oerke, E.C., Frohling, P. and Steiner, U. 2011. Thermographic assessment of scab disease on apple leaves. Precision Agriculture 12: 699-715.
Omidi, R., Moghimi, A., Pourreza, A., El-Hadedy, M. and Salah Eddin, A. 2020. Ensemble hyperspectral band selection for detecting nitrogen status in grape leaves. 19th IEEE International Conference on Machine Learning and Application (ICMLA). http://dx.doi.org/10.1109/ICMLA51294. 2020.00054.
Osco, L.P., Ramos, A.P.M., Moriya, É.A.S., de Souza, M., Junior, J.M., Matsubara, E.T., Imai, N.N. and Creste, J.E. 2019a. Improvement of leaf nitrogen content inference in Valencia-orange trees applying spectral analysis algorithms in UAV mounted-sensor images. International Journal of Applied Earth Observation and Geoinformation 83: 101907. https://doi.org/10.1016/j.jag.2019.101907.
Osco, L.P., Marques Ramos, A.P., Roberto Pereira, D., Akemi Saito Moriya, É., Nobuhiro Imai, N., Takashi Matsubara, E., Estrabis, N., de Souza, M., Marcato Junior, J., Gonçalves, W. N., Li, J., Liesenberg, V. and Creste, J.E. 2019b. Predicting canopy nitrogen content in citrus-trees using random forest algorithm associated to spectral vegetation indices from UAV-Imagery. Remote Sensing 11(24): 2925. https://doi.org/10.3390/rs11242925.
Piou, C. and Prévost, E. 2013. Contrasting effects of climate change in continental vs. oceanic environments on population persistence and microevolution of Atlantic salmon. Global Change Biology Bioenergy 19: 711-723. https://doi.org/10.1111/gcb.12085.
Polder, G., van der Heijden, G.W.A.M., van Doorn, J. and Baltissen, T.A.H.M.C. 2014. Automatic detection of tulip breaking virus (TBV) in tulip fields using machine vision. Bio systems Engineering 117: 35-42. https://doi.org/10.1016/j.biosystemseng.2013.05.010.
Prabhakar, M., Prasad, Y.G., Thirupathi, M., Sreedevi, G., Dharajothi, B. and Venkateswarlu, B. 2011. Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae). Computers and Electronics in Agriculture 79: 189-198. https://doi.org/10.1016/j.compag.2011.09.012.
Rousseau, C., Belin, E., Bove, E., Rousseau, D., Fabre, F., Berruyer, R., Guillaumes, J., Manceau, C., Jaques, M. A. and Boureau, T. 2013. High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis. Plant Methods 9: 17. https://doi.org/10.1186/1746-4811-9-17.
Rumpf, T., Mahlein, A.K., Steiner, U., Oerke, E.C., Dehne, H.W. and Plümer, L. 2010. Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and Electronics in Agriculture 74: 91-99. https://doi.org/10.1016/j.compag.2010.06.009.
Sahoo, R.N., Ray, S.S. and Manjunath, K.R. 2015. Hyperspectral remote sensing of agriculture. Current Science 108: 848-859.
Sankaran, S., Maja, J., Buchanon, S. and Ehsani, R. 2013. Huanglongbing (citrus greening) detection using visible, near infrared and thermal imaging techniques. Sensors 13 (2): 2117-2130. https://doi.org/10.3390/s130202117.
Strickland, R.M., Ess, D.R. and Parsons, S.D. 1998. Precision farming and precision pest management: the power of new crop production technologies. Journal of Nematology 30(4): 431-435.
Sugiura, R., Tsuda, S., Tamiya, S., Itoh, A., Nishiwaki, K., Murakami, N., Shibuya, Y., Hirafuji, M. and Nuske, S. 2016. Field phenotyping system for the assessment of potato late blight resistance using RGB imagery from an unmanned aerial vehicle. Biosystems Engineering 148: 1-10. https://doi.org/10.1016/j.biosystemseng.2016.04.010.
Wang, X., Zhang, M., Zhu, J. and Geng, S. 2008. Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural network (ANN). International Journal of Remote Sensing 29: 1693-1706. http://dx.doi.org/10.1080/01431160701281007.
West, S.J., Bravo, C., Oberti, R., Moshou, D., Ramon, H. and McCartney, H.A. 2010. Detection of fungal diseases optically and pathogen inoculum by air sampling. Pp. 135-150. In: Oerke, E.C., Gerhards, R., Menz, G. and Sikora, R.A. (eds.). Precision crop protection-the challenge and use of heterogeneity. Springer, Dordrecht.
Wijekoon, C.P., Goodwin, P.H. and Hsiang, T. 2008. Quantifying fungal infection of plant leaves by digital image analysis using Scion Image software. Journal of Microbiological Methods 27: 94-101. http://dx.doi.org/10.1016/j.mimet.2008.03.008.
Yones, M.S., Aboelghar, M., Khdery, G.A., Dahi, H.F. and Sowilem, M. 2019a. Spectral signature for detecting pest infestation of some cultivated plants in the northern west coast of Egypt. Egyptian Academic Journal of Biological Science 12: 73-38.
Yones, M.S., Aboelghar, M., Khdery, G.A., Farag, E., Ali, A.M., Salem, N.H. and Ma’mon, S. 2019b. Spectral measurements for monitoring of sugar beet infestation and its relation with production. Asian Journal of Agriculture and Biology 7(3): 386-395.
Yones, M.S., Khdery, G.A., Dahi, H.F., Farg, E., Arafat, S.M. and Gamil, W.E. 2019c. Early detection of pink boll worm Pectinophora gossypiella (Saunders) using remote sensing technologies. Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 111491C (21 October).
Zarate-Valdez, J.L., Muhammad, S., Saa, S., Lampinen, B.D. and Brown, P.H. 2015. Light interception, leaf nitrogen and yield prediction in almonds: a case study. European Journal of Agronomy 66: 1-7. https://doi.org/10.1016/j.eja.2015.02.004.
Zhang, X., Li, P. and Jiang, Z. 2016. Evaluation of spectral indices for assessing tomato leaf chlorophyll content. Precision Agriculture 17(2): 225-243.