Digital plant protection
DOI:
https://doi.org/10.29114/ajtuv.vol9.iss2.338Keywords:
plant protection, digitalization, monitoring, identification, forecastingAbstract
Over the last twenty years, the process of digitization has increasingly entered agriculture, including plant protection. This study summarizes research on various aspects of digital plant protection, mainly in relation to diseases and insect pests. It examines the possibilities of digitization in terms of forecasting, identification, monitoring and application of plant protection products, comparing them with classical methods used in phytopathology and entomology. Аttention is paid to Integrated Pest Management, Decision Support Systems, forecasting models, remote sensing systems and Artificial Intelligence as well as their features.
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Abbas, A., Zhang, Z., Zheng, H., Alami, M.M., Alrefaei, A.F., Abbas, Q., Naqvi, S.A.H., Rao, M.J., Mosa, W.F.A., Abbas, Q., et al. (2023). Drones in Plant Disease Assessment, Efficient Moni-toring, and Detection: A Way Forward to Smart Agriculture. Agronomy,13, 1524.
Abd El-Ghany, N.M., Abd El-Aziz, S.E., Marei, S.S. (2020). A review: Application of remote sens-ing as a promising strategy for insect pests and diseases management. Environ. Sci. Pollut. Res., 27, 33503–33515.
Abu-khalaf, N., and Salman, M. (2014) Visible / Near infrared (VIS / NIR) spectroscopy and multi-variate data analysis (MVDA) for identification and quantification of olive leaf spot (OLS) disease. Palestine Technical University Research Journal: 2 (1): 1--8. https://digitalcommons.aaru.edu.jo/ptuk/vol2/iss1/1
Arshad, F., Deliorman, M., Sukumar, P., Qasaimeh, M.A., Olarve, J.S.; Santos, G.N.; Bansal, V., Ahmed, M.U. (2023) Recent Developments and Applications of Nanomaterial-Based Lab-on-a-Chip Devices for Sustainable Agri-Food Industries. Trends Food Sci. Technol., 136, 145–158.
Atanasov, A., Mihova, G. and Mihaylov, R. (2022). Applicability and efficiency of remote sensing of agricultural areas. Bulgarian Journal of Agricultural Science, 28 (5), 933–943.
Banerjee, S., Mondal, A.C. (2023). An Intelligent Approach to Reducing Plant Disease and Enhanc-ing Productivity Using Machine Learning. Int. J. Recent Innov. Trends Comput. Commun., 11, 250–262.
Bannerman, J. A., Costamagna, A. C., McCornack, B. P. & Ragsdale, D. W. (2015). Comparison of relative bias, precision, and efficiency of sampling methods for natural enemies of soybean aphid (Hemiptera: Aphididae). J. Econ. Entomol., 108, 1381–1397.
Batz, P., Will, T., Thiel, S., Ziesche, T.M., and Joachim, C. (2023). From identification to forecast-ing: the potential of image recognition and artificial intelligence for aphid pest monitoring. Front. Plant Sci., 14. https://doi.org/10.3389/fpls.2023.1150748
Bauriegel, E. and Herppich, W. B. (2014). Hyperspectral and Chlorophyll Fluorescence Imaging for Early Detection of Plant Diseases, with Special Reference to Fusarium spec. Infections on Wheat, Agriculture, 4, 32-57, https://doi.org/10.3390/agriculture4010032
Binns, M. R. & Nyrop, J. P. (1992). Sampling insect populations for the purpose of IPM decision making. Annu. Rev. Entomol. 37, 427–453. https://doi.org/10.1146/annurev.ento.37.1.427
Bregaglioa, S., Saviana, F., Raparellia, E., Morellia, D., Epifania, R., Pietrangelib, F., Nigroc, C., Bugianid, R., Pinie, S., Culattif, P., Tognettig, D., Spannah, F., Gerardii, M., Delilloj, I., Ba-joccoa, S., Fanchinia, D., Filaa, G., Ginaldia, F., Manici, L. M. (2022). A public decision support system for the assessment of plant disease infection risk shared by Italian regions. Journal of Environmental Management, 317, 115365. https://doi.org/10.1016/j.jenvman.2022.115365
Brydegaard, M. & Jansson, S. (2018). Advances in entomological laser radar. IET Int. Radar Conf. https://doi.org/10.1049/joe.2019.0598
Burkholder, W. E. & Ma, M. (1985). Pheromones for monitoring and control of stored-product in-sects. Annu. Rev. Entomol., 30, 257–272.
Cabrera-Bosquet, L., Molero, G., Stellacci, A. M., Araus, J. L., Bort, J., Nogues, S. (2011). NDVI as a Potential Tool for Predicting Biomass, Plant Nitrogen Content and Growth in Wheat Genotypes Subjected to Different Water and Nitrogen Conditions. Cereal Research Communications, 39(1), 147-159.
Camino, C., Calderón, R., Parnell, S., Dierkes, H., Chemin, Y., Román-Écija, M., Montes-Borrego, M., Landa, B. B., Navas-Cortes, J. A., Zarco-Tejada, P. J., and Beck, P. S. A. (2021). Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits. Remote Sens. Environ., 260, 112420.
Campbell, C. L., and Madden, L. V. (1990). Introduction to Plant Disease Epidemiology. John Wiley & Sons, New York.
Chen, C., Li, Y., Tai, C., Chen, Y., Huang, Y., (2022). Pest incidence forecasting based on Internet of Things and Long Short-Term Memory Network. Applied Soft Computing, 124, 108895. https://doi.org/10.1016/j.asoc.2022.108895
Cunniffe, N.J., Koskella, B., Metcalf, E., C.J., Parnell, S., Gottwald, T.R., Gilligan, C.A. (2015). Thirteen challenges in modelling plant diseases. Epidemics, 10, 6–10. https://doi.org/10.1016/j.epidem.2014.06.002
Dalal, P. K., Singh, J. K. (2017). Role of modeling in insect pest and disease management. Journal of Entomology and Zoology Studies, 5(5), 1773-1777.
Davies, W.J., Shen, J. (2020). Reducing the environmental footprint of food and farming with agri-culture green development. Front Agric Sci Eng, 7(1), 1–4. https://doi.org/10.15302/J-FASE-201931
Deguine, J. P., Aubertot, J. N., Flor, R.J., Lescourret, F., Wyckhuys, K.A.G., Ratnadass, A. (2021). Integrated pest management: good intentions, hard realities. A review. Agron. Sustain. Dev. 41, 38. https://doi.org/10.1007/s13593-021-00689-w.
Dong, A., Wang, Z., Huang, J., Song, B., Hao, G. (2021). Bioinformatic tools support decision-making in plant disease management. Trends in Plant Science, 26 (9), 953-967.
Drake, V. A., Hatty, S., Symons, C. & Wang, H. (2020). Insect monitoring radar: Maximizing per-formance and utility. Remote Sens., 12, 596.
Duffy, C., Fealy, R., and Fealy, R. M. (2017). An improved simulation model to describe the tem-perature-dependent population dynamics of the grain aphid, Sitobion avenae. Ecol. Modell., 354, 140–171. https://doi.org/10.1016/j.ecolmodel.2017.03.011
Eaton, F.D., McLaughlin, S.A., Hines, J.R. (1995). A new frequency-modulated continuous wave radar for studying planetary boundary layer morphology. Radio Sci., 30, 75–88.
Fang, Y., and Ramasamy, R. P. (2015) Current and prospective methods for plant disease detection. Biosensors, 5(3), 537--561.
Fristrup, K. M., Shaw, J. A. & Tauc, M. J. (2017). Development of a wing-beat-modulation scan-ning lidar system for insect studies. Lidar Remote Sens. Environ. Monit., 15. https://doi.org/10.1117/12.2274656
Ganeva, D., Filchev, L., Roumenina, E., Dragov, R., Nedyalkova, S. and Bozhanova, V. (2024). Winter Durum Wheat Disease Severity Detection with Field Spectroscopy in Phenotyping Experiment at Leaf and Canopy Level., Remote Sens, 16, 1762. https://doi.org/10.3390/rs16101762
Gent, D. H., Mahaffee, W. F., McRoberts, N., and W. F. Pfender. (2013). The use and role of predic-tive systems in disease management. Annual review of phytopathology, 51, 267–289.
González-Rodríguez, V. E., Izquierdo-Bueno, I. and Garrido, C. (2024). Artificial Intelligence: A Promising Tool for Application in Phytopathology. Horticulturae, 10, 197. https://doi.org/10.3390/horticulturae10030197.
Görlich, F., Marks, E., Mahlein, A.K., König, K., Lottes, P., Stachniss, C. (2021) UAV-based classification of Cercospora leaf spo using RGB imgaes. Drones, 5(2), 34. https:// doi. org/ 10. 3390/ drone s5020034
Gullino, M. L., and Bonants, P. J. M. (2014) Detection and Diagnostics of Plant Pathogens. Springer: Berlin, Germany.
Harrington, R., and Hulle, M. (2017). “16 monitoring and forecasting,” in Aphids as crop pests. Eds. H. F. van Emden and R. Harrington (Wallingford Oxfordshire UK, Boston MA: CABI), 362–381.
Heidarian, D., R., Jarroudi, E. M., Kouadio, L., Meersmans, J., Beyer, M. (2020). Monitoring Wheat Leaf Rust and Stripe Rust in Winter Wheat Using High-Resolution UAV-Based Red-Green-Blue Imagery. Remote Sens., 12, 3696.
Hobbs, S. (1991). A radar signal processor for biological applications. Meas. Sci. Technol., 2, 415.
Hu, H., Wang, N., Liao, J., Tovar-Lopez, F.J. (2023). Recent Progress in Micro- and Nanotechnolo-gy-Enabled Sensors for Biomedical and Environmental Challenges. Sensors, 23, 5406.
Hughes, G. (2017). The evidential basis of decision making in plant disease management. Annu. Rev. Phytopathol., 55, 41-59.
Jansson, S., Malmqvist, E. & Mlacha, Y. (2021). Real-time dispersal of malaria vectors in rural Afri-ca monitored with lidar. Plos one. 16(3), e0247803. https://doi.org/10.1371/journal.pone.024780
Kaivosoja, J., Hautsalo, J., Heikkinen, J., Hiltunen, L., Ruuttunen, P., Näsi, R., Niemeläinen, O., Lemsalu, M., Honkavaara, E., Salonen, J. (2021). Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases. Remote Sens., 13, 1238. https://doi.org/10.3390/rs13071238
Khaled, A. Y., Abd Aziz, S., Bejo, S. K., Nawi, N. M., Seman, I. A. & Onwude, D. I. (2017). Early Detection of Diseases in Plant Tissue Using Spectroscopy – Applications and Limitations, Applied Spectroscopy Reviews, http://dx.doi.org/10.1080/05704928.2017.1352510
Klerkx, L., Jakku, E., and Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS Wagen. J. Life Sci., 90-91, 1-16.
Knierim, A., Kernecker, M., Erdle, K., Wurbs, A. (2019). Smart farming technology innovations – Insights and reflections from the German Smart-AKIS hub. NJAS - Wageningen Journal of Life Sciences, 90–91(1), 1–10. https://doi.org/10.1016/j.njas.2019.100314
Kogan, M. (1998). Integrated pest management: historical perspectives and contemporary develop-ments. Annual Rev. Entomology, 43, 243–270.
Koleva, M., Yankova, P., Plamenov, D., Naskova, P. (2024). Digitalization In Plant Production And Plant Protection In Bulgaria – Current Status And Future Goals. Annals of the University of Craiova - Agriculture, Montanology, Cadastre Series, 54 (1), 170-181
Kuska, M. T., Heim, R.H. J., Geedicke, I., Gold, K. M., Brugger, A., Paulus, S. (2022). Digital plant pathology: a foundation and guide to modern agriculture. Journal of Plant Diseases and Pro-tection, 129, 457–468.
Lajoie-O’Malley, A., Bronson, K., van der Burg, S., and Klerkx, L. (2020). The future(s) of digital agriculture and sustainable food systems: An analysis of high-level policy documents. Eco-syst. Serv., 45, 101183.
Lechenet, M., Dessaint, F., Py, G., Makowski, D., and Munier-Jolain, N. (2017). Reducing pesticide use while preserving crop productivity and profitability on arable farms. Nat. Plants, 3, 17008. https://doi.org/10.1038/nplants.2017.8
Liu, J. G. & Mason, Ph. J. (2009). Essential Image Processing for GIS and Remote Sensing. Wiley-Blackwell. p. 4. ISBN 978-0-470-51032-2.
López-López, M., Calderón, R., González-Dugo, V., Zarco-Tejada, P. J. and Fereres, E. (2016). Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyper-spectral and Thermal Imagery, Remote Sens., 8, 276. https://doi.org/10.3390/rs8040276
Lucas, J.A., Hawkins, N.J., Fraaije, B.A. (2015). The Evolution of Fungicide Resistance. Adv. Appl. Microbiol., 90, 29–92.
Magarey, R. D., Travis, J. W., Russo, J. M., Seem, R. C., and Magarey P. A. (2002). Decision sup-port systems: quenching the thirst. Plant Disease, 86, 4–14.
Mahlein, A. K. (2016). Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis., 100, 241–51.
Mahlein, A., Barbedo, J.G.A., Chiang, Kuo-Szu, DelPonte, E.M., Bock, C.H. (2024). From Detec-tion to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Mod-ern Plant Disease Management. Phytopathology, 114, 1733-1741. https://doi.org/10.1094/PHYTO-01-24-0009-PER.
Mankin, R. W., Hagstrum, D. W., Smith, M. T., Roda, A. L. & Kairo, M. T. K. (2011). Perspective and promise: a century of insect acoustic detection and monitoring. Am. Entomol., 57(1), 30–44.
Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., Villa, P., Stroppiana, D., Boschetti, M., Goulart, L. R., Davis, C. E., and Dandekar, A. M. (2015). Advanced methods of plant disease detection. A review. Agron. Sustain. Dev., 35 (1), 1-25.
Metcalf, J.I. (1975). Microstructure of Radar Echo Layers in the Clear Atmosphere. J. Atmos. Sci., 32, 362.
Moore, A., Miller, J. R., Tabashnik, B. E. & Gage, S. H. (1986). Automated identification of flying insects by analysis of wingbeat frequencies. J. Econ. Entomol., 79, 1703–1706.
Morgan, D., Walters, K. F. A., Oakley, J. N., and A. Lane. (2000). An Internet-based decision-support system for insect pests of rape. EPPO Bulletin, 30, 155–158.
Noskov, A., Bendix, J., and Friess, N. (2021). A review of insect monitoring approaches with special reference to radar techniques. A Review of Insect Monitoring Approaches with Special Ref-erence to Radar Techniques Sensors, 21(4), 1474. https://doi.org/10.3390/s21041474
Nwauzoma, A.B. (2016). A review of geographic information systems and digital imaging in plant pathology application. African Journal of Agricultural Research, 11(42), 4172-4180, https://doi.org/10.5897/AJAR2016.11641
Ojiambo, P. S., Yuen, J., van den Bosch, F., and Madden, L. V. 2017. Epidemiology: Past, present, and future impacts on understanding disease dynamics and improving plant disease man-agement–A summary of focus issue articles. Phytopathology, 107, 1092-1094.
Paulus, S. (2019). Measuring crops in 3D: Using geometry for plant phenotyping. Plant Methods, 15, 103.
Perry, J.N., Woiwod, I.P., Hanski, I. (1993). Using Response-Surface Methodology to Detect Chaos in Ecological Time Series. Oikos, 68, 329–339.
Prasad, Y. G., and Prabhakar, M. (2012). “Pest monitoring and forecasting,” in Integrated pest man-agement: principles and practice. Ed. D. P. Abrol (Wallingford, CABI), 41–57.
Purnhagen, K. P., Clemens, S., Eriksson, D., Fresco, L. O., Tosun, J., Qaim, M., Visser, R. G. F., Weber, A. P. M., Wesseler, J. H. H., and Zilberman, D. (2021). Europe’s Farm to Fork strat-egy and its commitment to biotechnology and organic farming: Conflicting or complemen-tary goals? Trends Plant Sci., 26, 600-606.
Reed, S. C., Williams, C. M. & Chadwick, L. E. (1942). Frequency of wing-beat as a character for separating species races and geographic varieties of Drosophila. Genetics, 27, 349.
Ren, Y.; Huang, W., Ye, H., Zhou, X., Ma, H., Dong, Y., Shi, Y., Geng, Y., Huang, Y., Jiao, Q., et al. (2021). Quantitative Identification of Yellow Rust in Winter Wheat with a New Spectral Index: Development and Validation Using Simulated and Experimental Data. Int. J. Appl. Earth Obs. Geoinf., 102, 102384.
Ristaino, J. B., Anderson, P. K., Bebber, D. P., Brauman, K. A., Cunniffe, N. J., Fedoroff, N. V., Finegold, C., Garrett, K. A., Gilligan, C. A., Jones, C. M., Martin, M. D., MacDonald, G. K., Neenan, P., Records, A., Schmale, D. G., Tateosian, L., and Wie, Q. (2021). The persistent threat of emerging plant disease pandemics to global food security. Proc. Natl. Acad. Sci. U.S.A., 118: e2022239118.
Rossi, V., Sperandio, G., Caffi, T., Simonetto, A., and Gilioli, G. (2019). Critical success factors for the adoption of decision tools in IPM. Agronomy, 9, 710.
Rydhmer, K., Bick, E., Still, L., Strand, A., Luciano, R., Helmreich, S., Beck, B. D., Grønne, C., Malmros, L., Poulsen, K., Elbæk, F., Brydegaard, M., Lemmich, J. & Nikolajsen, T. (2022). Automating insect monitoring using unsupervised near-infrared sensors. Scientific Reports, 12, 2603. https://doi.org/10.1038/s41598-022-06439-6
Savary, S., Willocquet, L., Pethybridge, S.J., Esker, P., McRoberts, N., Nelson, A. (2019). The global burden of pathogens and pests on major food crops. Nat. Ecol. Evol., 3, 430–439. https:// doi. org/ 10. 1038/ s41559-018-0793-y
Sawant, S. B., Raju, R. S., Behera, J. (2023). Artificial Intelligence Revolutionizes Plant Pathology: Unleashing the Power of Technology for Crop Protection. Biotica Research Today, 5(6), 405-406.
Shtienberg, D. (2013). Will decision-support systems be widely used for the management of plant diseases? Annual Rev. Phytopathology, 51, 1–16.
Singh, A., Ganapathysubramanian, B., Singh, A.K., Sarkar, S. (2016). Machine Learning for High-Throughput Stress Phenotyping in Plants. Trends Plant Sci., 21, 110–124.
Singh, A.K., Ganapathysubramanian, B., Sarkar, S., Singh, A. (2018). Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives. Trends Plant Sci., 23, 883–898.
Thomas, S., Kuska, M.T., Bohnenkamp, D., Brugger, A., Alisaac, E., Wahabzada, M., Behmann, J., Mahlein, A.K. (2018) Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective. J. Plant Dis. Prot., 125, 5–20. https://doi.org/10.1007/s41348-017-0124-6
Trapman, M. C. 2016. Validation of the RIMpro decision support system for apple sawfly (Hop-locampa testudinea) with field observation in The Netherlands, Belgium, Denmark and Aus-tria 2010-2015. Ecofruit.17th International Conference on Organic Fruit-Growing: Proceed-ings, 15-17 February 2016, Hohenheim, Germany, pp. 69–76.
Travis, J. W., and Latin, R. X. (1991). Development, implementation and adoption of expert sys-tems. Annu. Rev. Phytopathol. 29, 343-360.
Tummapudi, S., Sadhu, S.S., Simhadri, S.N., Damarla, S.N.T., Bhukya, M. (2023). Deep Learning Based Weed Detection and Elimination in Agriculture. In Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2023— Proceedings, Lalitpur, Nepal, 26–28 April 2023; Institute of Electrical and Electronics Engineers Inc.: New York, NY, USA, 2023; pp. 147–151
Qin, J., Monje, O., Nugent, M.R., Finn, J.R., O’Rourke, A.E., Wilson, K.D., Fritsche, R.F., Baek, I., Chan, D.E., Kim, M.S. (2023). A Hyperspectral Plant Health Monitoring System for Space Crop Production. Front. Plant Sci., 14, 1133505.
Wallhead, M., and Zhu, H. (2017). Decision Support Systems for Plant Disease and Insect Manage-ment in Commercial Nurseries in the Midwest: A Perspective Review. J. Environ. Hort., 35(2), 84–92.
Wang, S., Peng, Q., Zhang, W. and He, X. (2022). Development and Application of an Intelligent Plant Protection Monitoring System. Agronomy, 12, 1046. https://doi.org/10.3390/agronomy12051046.
Willets, K. A., and Van Duyne, R. P. (2007) Localized surface plasmon resonance spectroscopy and sensing. Annu. Rev. Phys. Chem., 58, 267--97.
Zhao, L., Walkowiak, S., Fernando, W.G.D. (2023). Artificial Intelligence: A Promising Tool in Ex-ploring the Phytomicrobiome in Managing Disease and Promoting Plant Health. Plants, 12, 1852.
Zhang, J., Pu, R., Yuan, L., Huang, W., Nie, C., and Yang, G. (2014). Integrating remotely sensed and meteorological observations to forecast wheat powdery mildew at a regional scale. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 4328-4339
Zhu, H., Rosetta, R., Reding, M. E., Zondag, R. H., Ranger, C. M., Canas, L., Fulcher, A., Derksen, R. C., Ozkan, H. E., and Krause, C. R. (2017). Validation of laser-guided variable-rate sprayer for managing insects in ornamental nurseries. Transactions ASABE, 60(2), 337-345.
Zolkin, A. L., Burda, A. G., Avdeev, Y. M. and Fakhertdinova, D. I. (2021). The main areas of application of information and digital technologies in the agro-industrial complex. IOP Conf. Ser.: Earth Environ. Sci., 677, 032092
Abd El-Ghany, N.M., Abd El-Aziz, S.E., Marei, S.S. (2020). A review: Application of remote sens-ing as a promising strategy for insect pests and diseases management. Environ. Sci. Pollut. Res., 27, 33503–33515.
Abu-khalaf, N., and Salman, M. (2014) Visible / Near infrared (VIS / NIR) spectroscopy and multi-variate data analysis (MVDA) for identification and quantification of olive leaf spot (OLS) disease. Palestine Technical University Research Journal: 2 (1): 1--8. https://digitalcommons.aaru.edu.jo/ptuk/vol2/iss1/1
Arshad, F., Deliorman, M., Sukumar, P., Qasaimeh, M.A., Olarve, J.S.; Santos, G.N.; Bansal, V., Ahmed, M.U. (2023) Recent Developments and Applications of Nanomaterial-Based Lab-on-a-Chip Devices for Sustainable Agri-Food Industries. Trends Food Sci. Technol., 136, 145–158.
Atanasov, A., Mihova, G. and Mihaylov, R. (2022). Applicability and efficiency of remote sensing of agricultural areas. Bulgarian Journal of Agricultural Science, 28 (5), 933–943.
Banerjee, S., Mondal, A.C. (2023). An Intelligent Approach to Reducing Plant Disease and Enhanc-ing Productivity Using Machine Learning. Int. J. Recent Innov. Trends Comput. Commun., 11, 250–262.
Bannerman, J. A., Costamagna, A. C., McCornack, B. P. & Ragsdale, D. W. (2015). Comparison of relative bias, precision, and efficiency of sampling methods for natural enemies of soybean aphid (Hemiptera: Aphididae). J. Econ. Entomol., 108, 1381–1397.
Batz, P., Will, T., Thiel, S., Ziesche, T.M., and Joachim, C. (2023). From identification to forecast-ing: the potential of image recognition and artificial intelligence for aphid pest monitoring. Front. Plant Sci., 14. https://doi.org/10.3389/fpls.2023.1150748
Bauriegel, E. and Herppich, W. B. (2014). Hyperspectral and Chlorophyll Fluorescence Imaging for Early Detection of Plant Diseases, with Special Reference to Fusarium spec. Infections on Wheat, Agriculture, 4, 32-57, https://doi.org/10.3390/agriculture4010032
Binns, M. R. & Nyrop, J. P. (1992). Sampling insect populations for the purpose of IPM decision making. Annu. Rev. Entomol. 37, 427–453. https://doi.org/10.1146/annurev.ento.37.1.427
Bregaglioa, S., Saviana, F., Raparellia, E., Morellia, D., Epifania, R., Pietrangelib, F., Nigroc, C., Bugianid, R., Pinie, S., Culattif, P., Tognettig, D., Spannah, F., Gerardii, M., Delilloj, I., Ba-joccoa, S., Fanchinia, D., Filaa, G., Ginaldia, F., Manici, L. M. (2022). A public decision support system for the assessment of plant disease infection risk shared by Italian regions. Journal of Environmental Management, 317, 115365. https://doi.org/10.1016/j.jenvman.2022.115365
Brydegaard, M. & Jansson, S. (2018). Advances in entomological laser radar. IET Int. Radar Conf. https://doi.org/10.1049/joe.2019.0598
Burkholder, W. E. & Ma, M. (1985). Pheromones for monitoring and control of stored-product in-sects. Annu. Rev. Entomol., 30, 257–272.
Cabrera-Bosquet, L., Molero, G., Stellacci, A. M., Araus, J. L., Bort, J., Nogues, S. (2011). NDVI as a Potential Tool for Predicting Biomass, Plant Nitrogen Content and Growth in Wheat Genotypes Subjected to Different Water and Nitrogen Conditions. Cereal Research Communications, 39(1), 147-159.
Camino, C., Calderón, R., Parnell, S., Dierkes, H., Chemin, Y., Román-Écija, M., Montes-Borrego, M., Landa, B. B., Navas-Cortes, J. A., Zarco-Tejada, P. J., and Beck, P. S. A. (2021). Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits. Remote Sens. Environ., 260, 112420.
Campbell, C. L., and Madden, L. V. (1990). Introduction to Plant Disease Epidemiology. John Wiley & Sons, New York.
Chen, C., Li, Y., Tai, C., Chen, Y., Huang, Y., (2022). Pest incidence forecasting based on Internet of Things and Long Short-Term Memory Network. Applied Soft Computing, 124, 108895. https://doi.org/10.1016/j.asoc.2022.108895
Cunniffe, N.J., Koskella, B., Metcalf, E., C.J., Parnell, S., Gottwald, T.R., Gilligan, C.A. (2015). Thirteen challenges in modelling plant diseases. Epidemics, 10, 6–10. https://doi.org/10.1016/j.epidem.2014.06.002
Dalal, P. K., Singh, J. K. (2017). Role of modeling in insect pest and disease management. Journal of Entomology and Zoology Studies, 5(5), 1773-1777.
Davies, W.J., Shen, J. (2020). Reducing the environmental footprint of food and farming with agri-culture green development. Front Agric Sci Eng, 7(1), 1–4. https://doi.org/10.15302/J-FASE-201931
Deguine, J. P., Aubertot, J. N., Flor, R.J., Lescourret, F., Wyckhuys, K.A.G., Ratnadass, A. (2021). Integrated pest management: good intentions, hard realities. A review. Agron. Sustain. Dev. 41, 38. https://doi.org/10.1007/s13593-021-00689-w.
Dong, A., Wang, Z., Huang, J., Song, B., Hao, G. (2021). Bioinformatic tools support decision-making in plant disease management. Trends in Plant Science, 26 (9), 953-967.
Drake, V. A., Hatty, S., Symons, C. & Wang, H. (2020). Insect monitoring radar: Maximizing per-formance and utility. Remote Sens., 12, 596.
Duffy, C., Fealy, R., and Fealy, R. M. (2017). An improved simulation model to describe the tem-perature-dependent population dynamics of the grain aphid, Sitobion avenae. Ecol. Modell., 354, 140–171. https://doi.org/10.1016/j.ecolmodel.2017.03.011
Eaton, F.D., McLaughlin, S.A., Hines, J.R. (1995). A new frequency-modulated continuous wave radar for studying planetary boundary layer morphology. Radio Sci., 30, 75–88.
Fang, Y., and Ramasamy, R. P. (2015) Current and prospective methods for plant disease detection. Biosensors, 5(3), 537--561.
Fristrup, K. M., Shaw, J. A. & Tauc, M. J. (2017). Development of a wing-beat-modulation scan-ning lidar system for insect studies. Lidar Remote Sens. Environ. Monit., 15. https://doi.org/10.1117/12.2274656
Ganeva, D., Filchev, L., Roumenina, E., Dragov, R., Nedyalkova, S. and Bozhanova, V. (2024). Winter Durum Wheat Disease Severity Detection with Field Spectroscopy in Phenotyping Experiment at Leaf and Canopy Level., Remote Sens, 16, 1762. https://doi.org/10.3390/rs16101762
Gent, D. H., Mahaffee, W. F., McRoberts, N., and W. F. Pfender. (2013). The use and role of predic-tive systems in disease management. Annual review of phytopathology, 51, 267–289.
González-Rodríguez, V. E., Izquierdo-Bueno, I. and Garrido, C. (2024). Artificial Intelligence: A Promising Tool for Application in Phytopathology. Horticulturae, 10, 197. https://doi.org/10.3390/horticulturae10030197.
Görlich, F., Marks, E., Mahlein, A.K., König, K., Lottes, P., Stachniss, C. (2021) UAV-based classification of Cercospora leaf spo using RGB imgaes. Drones, 5(2), 34. https:// doi. org/ 10. 3390/ drone s5020034
Gullino, M. L., and Bonants, P. J. M. (2014) Detection and Diagnostics of Plant Pathogens. Springer: Berlin, Germany.
Harrington, R., and Hulle, M. (2017). “16 monitoring and forecasting,” in Aphids as crop pests. Eds. H. F. van Emden and R. Harrington (Wallingford Oxfordshire UK, Boston MA: CABI), 362–381.
Heidarian, D., R., Jarroudi, E. M., Kouadio, L., Meersmans, J., Beyer, M. (2020). Monitoring Wheat Leaf Rust and Stripe Rust in Winter Wheat Using High-Resolution UAV-Based Red-Green-Blue Imagery. Remote Sens., 12, 3696.
Hobbs, S. (1991). A radar signal processor for biological applications. Meas. Sci. Technol., 2, 415.
Hu, H., Wang, N., Liao, J., Tovar-Lopez, F.J. (2023). Recent Progress in Micro- and Nanotechnolo-gy-Enabled Sensors for Biomedical and Environmental Challenges. Sensors, 23, 5406.
Hughes, G. (2017). The evidential basis of decision making in plant disease management. Annu. Rev. Phytopathol., 55, 41-59.
Jansson, S., Malmqvist, E. & Mlacha, Y. (2021). Real-time dispersal of malaria vectors in rural Afri-ca monitored with lidar. Plos one. 16(3), e0247803. https://doi.org/10.1371/journal.pone.024780
Kaivosoja, J., Hautsalo, J., Heikkinen, J., Hiltunen, L., Ruuttunen, P., Näsi, R., Niemeläinen, O., Lemsalu, M., Honkavaara, E., Salonen, J. (2021). Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases. Remote Sens., 13, 1238. https://doi.org/10.3390/rs13071238
Khaled, A. Y., Abd Aziz, S., Bejo, S. K., Nawi, N. M., Seman, I. A. & Onwude, D. I. (2017). Early Detection of Diseases in Plant Tissue Using Spectroscopy – Applications and Limitations, Applied Spectroscopy Reviews, http://dx.doi.org/10.1080/05704928.2017.1352510
Klerkx, L., Jakku, E., and Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS Wagen. J. Life Sci., 90-91, 1-16.
Knierim, A., Kernecker, M., Erdle, K., Wurbs, A. (2019). Smart farming technology innovations – Insights and reflections from the German Smart-AKIS hub. NJAS - Wageningen Journal of Life Sciences, 90–91(1), 1–10. https://doi.org/10.1016/j.njas.2019.100314
Kogan, M. (1998). Integrated pest management: historical perspectives and contemporary develop-ments. Annual Rev. Entomology, 43, 243–270.
Koleva, M., Yankova, P., Plamenov, D., Naskova, P. (2024). Digitalization In Plant Production And Plant Protection In Bulgaria – Current Status And Future Goals. Annals of the University of Craiova - Agriculture, Montanology, Cadastre Series, 54 (1), 170-181
Kuska, M. T., Heim, R.H. J., Geedicke, I., Gold, K. M., Brugger, A., Paulus, S. (2022). Digital plant pathology: a foundation and guide to modern agriculture. Journal of Plant Diseases and Pro-tection, 129, 457–468.
Lajoie-O’Malley, A., Bronson, K., van der Burg, S., and Klerkx, L. (2020). The future(s) of digital agriculture and sustainable food systems: An analysis of high-level policy documents. Eco-syst. Serv., 45, 101183.
Lechenet, M., Dessaint, F., Py, G., Makowski, D., and Munier-Jolain, N. (2017). Reducing pesticide use while preserving crop productivity and profitability on arable farms. Nat. Plants, 3, 17008. https://doi.org/10.1038/nplants.2017.8
Liu, J. G. & Mason, Ph. J. (2009). Essential Image Processing for GIS and Remote Sensing. Wiley-Blackwell. p. 4. ISBN 978-0-470-51032-2.
López-López, M., Calderón, R., González-Dugo, V., Zarco-Tejada, P. J. and Fereres, E. (2016). Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyper-spectral and Thermal Imagery, Remote Sens., 8, 276. https://doi.org/10.3390/rs8040276
Lucas, J.A., Hawkins, N.J., Fraaije, B.A. (2015). The Evolution of Fungicide Resistance. Adv. Appl. Microbiol., 90, 29–92.
Magarey, R. D., Travis, J. W., Russo, J. M., Seem, R. C., and Magarey P. A. (2002). Decision sup-port systems: quenching the thirst. Plant Disease, 86, 4–14.
Mahlein, A. K. (2016). Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis., 100, 241–51.
Mahlein, A., Barbedo, J.G.A., Chiang, Kuo-Szu, DelPonte, E.M., Bock, C.H. (2024). From Detec-tion to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Mod-ern Plant Disease Management. Phytopathology, 114, 1733-1741. https://doi.org/10.1094/PHYTO-01-24-0009-PER.
Mankin, R. W., Hagstrum, D. W., Smith, M. T., Roda, A. L. & Kairo, M. T. K. (2011). Perspective and promise: a century of insect acoustic detection and monitoring. Am. Entomol., 57(1), 30–44.
Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., Villa, P., Stroppiana, D., Boschetti, M., Goulart, L. R., Davis, C. E., and Dandekar, A. M. (2015). Advanced methods of plant disease detection. A review. Agron. Sustain. Dev., 35 (1), 1-25.
Metcalf, J.I. (1975). Microstructure of Radar Echo Layers in the Clear Atmosphere. J. Atmos. Sci., 32, 362.
Moore, A., Miller, J. R., Tabashnik, B. E. & Gage, S. H. (1986). Automated identification of flying insects by analysis of wingbeat frequencies. J. Econ. Entomol., 79, 1703–1706.
Morgan, D., Walters, K. F. A., Oakley, J. N., and A. Lane. (2000). An Internet-based decision-support system for insect pests of rape. EPPO Bulletin, 30, 155–158.
Noskov, A., Bendix, J., and Friess, N. (2021). A review of insect monitoring approaches with special reference to radar techniques. A Review of Insect Monitoring Approaches with Special Ref-erence to Radar Techniques Sensors, 21(4), 1474. https://doi.org/10.3390/s21041474
Nwauzoma, A.B. (2016). A review of geographic information systems and digital imaging in plant pathology application. African Journal of Agricultural Research, 11(42), 4172-4180, https://doi.org/10.5897/AJAR2016.11641
Ojiambo, P. S., Yuen, J., van den Bosch, F., and Madden, L. V. 2017. Epidemiology: Past, present, and future impacts on understanding disease dynamics and improving plant disease man-agement–A summary of focus issue articles. Phytopathology, 107, 1092-1094.
Paulus, S. (2019). Measuring crops in 3D: Using geometry for plant phenotyping. Plant Methods, 15, 103.
Perry, J.N., Woiwod, I.P., Hanski, I. (1993). Using Response-Surface Methodology to Detect Chaos in Ecological Time Series. Oikos, 68, 329–339.
Prasad, Y. G., and Prabhakar, M. (2012). “Pest monitoring and forecasting,” in Integrated pest man-agement: principles and practice. Ed. D. P. Abrol (Wallingford, CABI), 41–57.
Purnhagen, K. P., Clemens, S., Eriksson, D., Fresco, L. O., Tosun, J., Qaim, M., Visser, R. G. F., Weber, A. P. M., Wesseler, J. H. H., and Zilberman, D. (2021). Europe’s Farm to Fork strat-egy and its commitment to biotechnology and organic farming: Conflicting or complemen-tary goals? Trends Plant Sci., 26, 600-606.
Reed, S. C., Williams, C. M. & Chadwick, L. E. (1942). Frequency of wing-beat as a character for separating species races and geographic varieties of Drosophila. Genetics, 27, 349.
Ren, Y.; Huang, W., Ye, H., Zhou, X., Ma, H., Dong, Y., Shi, Y., Geng, Y., Huang, Y., Jiao, Q., et al. (2021). Quantitative Identification of Yellow Rust in Winter Wheat with a New Spectral Index: Development and Validation Using Simulated and Experimental Data. Int. J. Appl. Earth Obs. Geoinf., 102, 102384.
Ristaino, J. B., Anderson, P. K., Bebber, D. P., Brauman, K. A., Cunniffe, N. J., Fedoroff, N. V., Finegold, C., Garrett, K. A., Gilligan, C. A., Jones, C. M., Martin, M. D., MacDonald, G. K., Neenan, P., Records, A., Schmale, D. G., Tateosian, L., and Wie, Q. (2021). The persistent threat of emerging plant disease pandemics to global food security. Proc. Natl. Acad. Sci. U.S.A., 118: e2022239118.
Rossi, V., Sperandio, G., Caffi, T., Simonetto, A., and Gilioli, G. (2019). Critical success factors for the adoption of decision tools in IPM. Agronomy, 9, 710.
Rydhmer, K., Bick, E., Still, L., Strand, A., Luciano, R., Helmreich, S., Beck, B. D., Grønne, C., Malmros, L., Poulsen, K., Elbæk, F., Brydegaard, M., Lemmich, J. & Nikolajsen, T. (2022). Automating insect monitoring using unsupervised near-infrared sensors. Scientific Reports, 12, 2603. https://doi.org/10.1038/s41598-022-06439-6
Savary, S., Willocquet, L., Pethybridge, S.J., Esker, P., McRoberts, N., Nelson, A. (2019). The global burden of pathogens and pests on major food crops. Nat. Ecol. Evol., 3, 430–439. https:// doi. org/ 10. 1038/ s41559-018-0793-y
Sawant, S. B., Raju, R. S., Behera, J. (2023). Artificial Intelligence Revolutionizes Plant Pathology: Unleashing the Power of Technology for Crop Protection. Biotica Research Today, 5(6), 405-406.
Shtienberg, D. (2013). Will decision-support systems be widely used for the management of plant diseases? Annual Rev. Phytopathology, 51, 1–16.
Singh, A., Ganapathysubramanian, B., Singh, A.K., Sarkar, S. (2016). Machine Learning for High-Throughput Stress Phenotyping in Plants. Trends Plant Sci., 21, 110–124.
Singh, A.K., Ganapathysubramanian, B., Sarkar, S., Singh, A. (2018). Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives. Trends Plant Sci., 23, 883–898.
Thomas, S., Kuska, M.T., Bohnenkamp, D., Brugger, A., Alisaac, E., Wahabzada, M., Behmann, J., Mahlein, A.K. (2018) Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective. J. Plant Dis. Prot., 125, 5–20. https://doi.org/10.1007/s41348-017-0124-6
Trapman, M. C. 2016. Validation of the RIMpro decision support system for apple sawfly (Hop-locampa testudinea) with field observation in The Netherlands, Belgium, Denmark and Aus-tria 2010-2015. Ecofruit.17th International Conference on Organic Fruit-Growing: Proceed-ings, 15-17 February 2016, Hohenheim, Germany, pp. 69–76.
Travis, J. W., and Latin, R. X. (1991). Development, implementation and adoption of expert sys-tems. Annu. Rev. Phytopathol. 29, 343-360.
Tummapudi, S., Sadhu, S.S., Simhadri, S.N., Damarla, S.N.T., Bhukya, M. (2023). Deep Learning Based Weed Detection and Elimination in Agriculture. In Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2023— Proceedings, Lalitpur, Nepal, 26–28 April 2023; Institute of Electrical and Electronics Engineers Inc.: New York, NY, USA, 2023; pp. 147–151
Qin, J., Monje, O., Nugent, M.R., Finn, J.R., O’Rourke, A.E., Wilson, K.D., Fritsche, R.F., Baek, I., Chan, D.E., Kim, M.S. (2023). A Hyperspectral Plant Health Monitoring System for Space Crop Production. Front. Plant Sci., 14, 1133505.
Wallhead, M., and Zhu, H. (2017). Decision Support Systems for Plant Disease and Insect Manage-ment in Commercial Nurseries in the Midwest: A Perspective Review. J. Environ. Hort., 35(2), 84–92.
Wang, S., Peng, Q., Zhang, W. and He, X. (2022). Development and Application of an Intelligent Plant Protection Monitoring System. Agronomy, 12, 1046. https://doi.org/10.3390/agronomy12051046.
Willets, K. A., and Van Duyne, R. P. (2007) Localized surface plasmon resonance spectroscopy and sensing. Annu. Rev. Phys. Chem., 58, 267--97.
Zhao, L., Walkowiak, S., Fernando, W.G.D. (2023). Artificial Intelligence: A Promising Tool in Ex-ploring the Phytomicrobiome in Managing Disease and Promoting Plant Health. Plants, 12, 1852.
Zhang, J., Pu, R., Yuan, L., Huang, W., Nie, C., and Yang, G. (2014). Integrating remotely sensed and meteorological observations to forecast wheat powdery mildew at a regional scale. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 4328-4339
Zhu, H., Rosetta, R., Reding, M. E., Zondag, R. H., Ranger, C. M., Canas, L., Fulcher, A., Derksen, R. C., Ozkan, H. E., and Krause, C. R. (2017). Validation of laser-guided variable-rate sprayer for managing insects in ornamental nurseries. Transactions ASABE, 60(2), 337-345.
Zolkin, A. L., Burda, A. G., Avdeev, Y. M. and Fakhertdinova, D. I. (2021). The main areas of application of information and digital technologies in the agro-industrial complex. IOP Conf. Ser.: Earth Environ. Sci., 677, 032092
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Koleva, M. A. (2025). Digital plant protection. ANNUAL JOURNAL OF TECHNICAL UNIVERSITY OF VARNA, BULGARIA, 9(2), 49–60. https://doi.org/10.29114/ajtuv.vol9.iss2.338
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