Abstract
Automation of disease detection and monitoring can facilitate targeted and timely disease control, which can lead to increased yield, improved crop quality and reduction in the quantity of applied pesticides. Further advantages are reduced production costs, reduced exposure to pesticides for farm workers and inspectors and increased sustainability. Symptoms are unique for each disease and crop, and each plant may suffer from multiple threats. Thus, a dedicated integrated disease-detection system and algorithms are required. The development of such a robotic detection system for two major threats of bell pepper plants: powdery mildew (PM) and Tomato spotted wilt virus (TSWV), is presented. Detection algorithms were developed based on principal component analysis using RGB and multispectral NIR-R-G sensors. High accuracy was obtained for pixel classification as diseased or healthy, for both diseases, using RGB imagery (PM: 95%, TSWV: 90%). NIR-R-G multispectral imagery yielded low classification accuracy (PM: 80%, TSWV: 61%). Accordingly, the final sensing apparatus was composed of a RGB sensor and a single-laser-beam distance sensor. A relatively fast cycle time (average 26.7 s per plant) operation cycle for detection of the two diseases was developed and tested. The cycle time was mainly influenced by sub-tasks requiring motion of the manipulator. Among these tasks, the most demanding were the determination of the required detection position and orientation. The time for task completion may be reduced by increasing the robotic work volume and by improving the algorithm for determining position and orientation.
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References
Aramburu, J., Galipienso, L., Soler, S., & López, C. (2010). Characterization of Tomato spotted wilt virus isolates that overcome the Sw-5 resistance gene in tomato and fitness assays. Phytopathologia Mediterranea, 49, 342–351.
Avila, Y., Stavisky, J., Hague, S., Funderburk, J., Reitz, S., & Momol, T. (2006). Evaluation of Frankliniella bispinosa (Thysanoptera: Thripidae) as a vector of the Tomato spotted wilt virus in pepper. Florida Entomologist, 89(2), 204–207.
Barbedo, A., & Garcia, J. (2013). Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus, 2, 660. doi:10.1186/2193-1801-2-660.
Bauer, S. D., Kore, F., & Forstner, W. (2011). The potential of automatic methods of classification to identify leaf diseases from multispectral images. Precision Agriculture, 12, 361–377.
Bélanger, M. C., Roger, J. M., Cartolaro, P., Viau, A. A., & Bellon-Maurel, V. (2008). Detection of Powdery mildew in grapevine using remotely sensed UV-induced fluorescence. International Journal of Remote Sensing, 29(6), 1707–1724.
Bock, C. H., Poole, G. H., Parker, P. E., & Gottwald, T. R. (2010). Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Critical Reviews in Plant Science, 29, 59–107.
Cerkauskas, R. F., & Buonassisi, A. (2003). First report of Powdery mildew of greenhouse pepper caused by Leveillula taurica in British Columbia, Canada. Plant Disease, 87(9), 1151.
Crescenzi, A., Viggiano, A., & Fanigliulo, A. (2013). Resistance breaking tomato spotted wilt virus isolates on resistant pepper varieties in Italy. Communications in Agricultural and Applied Biological Sciences, 78(3), 609–612.
Eizicovits, D., Van Tuijl, B., Berman, S., & Edan, Y. (2016). Integration of perception capabilities in gripper design using graspability maps. Biosystems Engineering, 146, 98–113.
Elad, Y., Messika, Y., Brand, M., Rav David, D., & Sztejnberg, A. (2007). Effect of microclimate on Leveillula taurica powdery mildew of sweet pepper. Phytopathology, 97(7), 813–824.
Fereres, A., & Raccah, B. (2015). Plant virus transmission by insects. Encyclopedia of Life Sciences. doi: 10.1002/9780470015902.a0000760.pub3
Franke, J., Gebhardt, S., Menz, G., & Helfrich, H. P. (2009). Geostatistical analysis of the spatiotemporal dynamics of powdery mildew and leaf rust in wheat. Phytopathology, 99, 974–984.
Franke, J., & Menz, G. (2007). Multi-temporal wheat disease detection by multi-spectral remote sensing. Precision Agriculture, 8(3), 161–172.
González, R., Rodríguez, F., Sánchez-Hermosilla, J., & Donaire, J. G. (2009). Navigation techniques for mobile robots in greenhouses. Applied Engineering in Agriculture, 25(2), 153–165.
Hillnhuetter, C., & Mahlein, A. K. (2008). Early detection and localisation of sugar beet diseases: new approaches. Gesunde Pflanzen, 60, 143–149.
Kenyon, L., Kumar, S., Tsai, W. S., & Hughes, J. A. (2014). Virus diseases of peppers (Capsicum spp.) and their control. Advances in Virus Research, 90, 297–354.
Lee, W. S., Alchanatis, V., Yang, C., Hirafuji, M., Moshou, D., & Li, C. (2010). Sensing technologies for precision specialty crop production. Computers and Electronics in Agriculture, 74, 2–33.
Mahlein, A. K., Rumpf, T., Welke, P., Dehne, H. W., Plumer, L., Steiner, U., et al. (2013). Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment, 128, 21–30.
Margaria, P., Ciuffo, M., & Turina, M. (2004). Resistance breaking strain of Tomato spotted wilt virus (Tospovirus; Bunyaviridae) on resistant pepper cultivars in Almería, Spain. Plant Pathology, 53, 795.
Moshou, D., Bravo, C., Oberti, R., West, J. S., Ramon, H., Vougioukas, S., et al. (2011). Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops. Biosystems Engineering, 108(4), 311–321.
Moury, B., & Verdin, E. (2012). Viruses of pepper crops in the Mediterranean basin: a remarkable stasis. Advances in Virus Research, 84, 127–162.
Oberti, R., Marchi, M., Tirelli, P., Calcante, A., Iriti, M., & Borghese, A. N. (2014). Automatic detection of Powdery mildew on grapevine leaves by image analysis: Optimal view-angle range to increase the sensitivity. Computers and Electronics in Agriculture, 104, 1–8.
Oerke, E. C., & Dehne, H. W. (2004). Safeguarding production losses in major crops and the role of crop protection. Crop Protection, 23, 275–285.
Oerke, E. C., Froehling, P., & Steiner, U. (2011). Thermographic assessment of scab disease on apple leaves. Precision Agriculture, 12(5), 699–715.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.
Patil, J. K., & Kumar, R. (2011). Advances in image processing for detection of plant diseases. Journal of Advanced Bioinformatics Applications and Research, 2(2), 135–141.
Pernezny, K. L., Roberts, P. D., Murphy, J. F., & Goldberg, N. P. (2003). Compendium of pepper diseases. Wisconsin: American Phytopathological Society.
Pilli, S. K., Nallathambi, B., George, S. J., & Diwanji, V. (2014). eAGROBOT—A robot for early crop disease detection using image processing. In Proceedings of the IEEE International Conference on Electronics and Communication Systems (pp. 1–6). New York: IEEE.
Pujari, J. D., Yakkundimath, R., & Byadgi, A. S. (2015). Image processing based detection of fungal diseases in plants. In Proceedings of the International Conference on Information and Communication Technologies (pp. 1802–1808). Amsterdam, The Netherlands: Elsevier Science.
Rosella, S., Jose Diez, M., & Nuez, F. (1996). Viral diseases causing the greatest economic losses to the tomato crop. I. The Tomato spotted wilt virus—a review. Scientia Horticulturae, 67(3–4), 117–150.
Rumpf, T., Mahlein, A. K., Steiner, U., Oerke, E. C., Dehne, H. W., & Plumer, L. (2010). Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1), 91–99.
Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627–1639.
Schor, N., Bechar, A., Ignat, T., Dombrovsky, A., Elad, Y., & Berman, S. (2016). Robotic disease detection in greenhouses: Combined detection of Powdery mildew and Tomato spotted wilt virus. IEEE Robotics and Automation Letters, 1(1), 354–360.
Schor, N., Berman, S., Dombrovsky, A., Elad, Y., Ignat, T., & Bechar, A. (2015). A robotic monitoring system for diseases of pepper in greenhouse. In Stafford, J. V. (Ed.) Proceedings of the 10th European Conference on Precision Agriculture (pp. 627–634). Wageningen, The Netherlands: Wageningen Academic Publishers.
Van Henten, E. J., Hemming, J., Van Tuijl, B. A. J., Kornet, J. G., Meuleman, J., Bontsema, J., et al. (2002). An autonomous robot for harvesting cucumbers in greenhouses. Autonomous Robots, 13(3), 241–258.
West, J. S., & Kimber, R. B. E. (2015). Innovations in air sampling to detect plant pathogens. Annals of Applied Biology, 166(1), 4–17.
Wetterich, C. B., Neves, R. F. O., Belasque, J., & Marcassa, L. G. (2016). Detection of citrus canker and Huanglongbing using fluorescence imaging spectroscopy and support vector machine technique. Applied Optics, 55(2), 400–407.
Zheng, Z., Nonomura, T., Appiano, M., Pavan, S., Matsuda, Y., Toyoda, H., et al. (2013). Loss of function in Mlo orthologues reduces susceptibility of pepper and tomato to powdery mildew disease caused by Leveillula taurica. PLoS One. doi:10.1371/journal.pone.0070723.
Acknowledgements
This research was supported by the Helmsley Charitable Trust through the Agricultural, Biological and Cognitive Robotics Initiative of Ben-Gurion University of the Negev and the Chief Scientist Fund of the Ministry of Agriculture. The authors would like to thank Prof. Dan G. Blumberg for use of the multispectral camera.
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Schor, N., Berman, S., Dombrovsky, A. et al. Development of a robotic detection system for greenhouse pepper plant diseases. Precision Agric 18, 394–409 (2017). https://doi.org/10.1007/s11119-017-9503-z
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DOI: https://doi.org/10.1007/s11119-017-9503-z