Abstract
Map** weeds in a field is important for efficient weed management practices. This study evaluates the efficiency of three vegetation indices: NDVI (normalized difference vegetation index), ReNDVI (Red-Edge normalized vegetation index) and SAVI (soil-adjusted vegetation index) for evaluating the impact of weeds on sugar beet crop. The study was carried out with open-source drone data of a sugar beet field (Beta vulgaris) in Rheinbach, Germany, provided by ASL (Autonomous System Lab; Department of Mechanical and Process Engineering, ETHZ) which was infested with weeds such as Galinsoga spec., Amaranthus retroflexus. Random forest, support vector machine and object-based image analysis were used to classify the multispectral drone image for map** crops and weeds. Using the best outcome of the three methods, a crop and weed mask was generated. These masks were used with three vegetation indices, to generate health maps for understanding the impact of weeds on the health of sugar beet crop. The accuracy of SVM, random forest and OBIA was 96%, 95% and 95%, respectively. The comparison of sugar beet and weed health mask indicated that the weed pressure was low on crop. The study also found that the overall range of vegetation indices was low, indicating a potential deficit in nutrients in the soil. Overestimation of the area under healthy vegetation was observed when NDVI and SAVI were used. The maps produced in this study can be further used to prepare weed density maps. These maps can be used to allocate the appropriate method and dosage of herbicides required in the field.
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References
Alhammadi, M. S., & Glenn, E. P. (2008). Detecting date palm trees health and vegetation greenness change on the eastern coast of the United Arab Emirates using SAVI. International Journal of Remote Sensing, 29(6), 1745–1765.
Awad, M., & Khanna, R. (2015). Support vector regression. Efficient Learning Machines. https://doi.org/10.1007/978-1-4302-5990-9_4
Baatz, M., & Schape, A. (2000). Multiresolution segmentation-an optimization approach for high quality multiscale image segmentation. In J. Strobl & T. Blaschke (Eds.), Angewandte Geographische Informationsverarbeitung XII (pp. 12–23). Wichmann Verlag.
Ballanti, L., Blesius, L., Hines, E., & Kruse, B. (2016). Tree species classification using hyperspectral imagery: A comparison of two classifiers. Remote Sensing, 8(6), 445. https://doi.org/10.3390/RS8060445
Báez-González, A. D., Chen, P. Y., Tiscareño-López, M., & Srinivasan, R. (2002). Using satellite and field data with crop growth modeling to monitor and estimate corn yield in Mexico. Crop Science, 42(6), 1943–1949. https://doi.org/10.2135/CROPSCI2002.1943
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
Burns, B. W., Green, V. S., Hashem, A. A., Massey, J. H., Shew, A. M., Adviento-Borbe, M. A. A., & Milad, M. (2022). Determining nitrogen deficiencies for maize using various remote sensing indices. Precision Agriculture, 23(3), 791–811. https://doi.org/10.1007/S11119-021-09861-4/TABLES/7
Candiago, S., Remondino, F., de Giglio, M., Dubbini, M., & Gattelli, M. (2015). Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing, 7(4), 4026–4047. https://doi.org/10.3390/RS70404026
Cánovas-García, F., & Alonso-Sarría, F. (2015). A local approach to optimize the scale parameter in multiresolution segmentation for multispectral imagery. Geocarto International, 30(8), 937–961. https://doi.org/10.1080/10106049.2015.1004131
Carmona, F., Rivas, R., & Fonnegra, D. C. (2017). Vegetation Index to estimate chlorophyll content from multispectral remote sensing data. European Journal of Remote Sensing, 48, 319–326. https://doi.org/10.5721/EUJRS20154818
de Castro, A. I., López-Granados, F., & Jurado-Expósito, M. (2013). Broad-scale cruciferous weed patch classification in winter wheat using QuickBird imagery for in-season site-specific control. Precision Agriculture, 14(4), 392–413. https://doi.org/10.1007/S11119-013-9304-Y/METRICS
Du, M., & Noguchi, N. (2017). Monitoring of wheat growth status and map** of wheat yield’s within-field spatial variations using color images acquired from UAV-camera system. Remote Sensing, 9(3), 289. https://doi.org/10.3390/RS9030289
ECognition .(2019). About Classification. https://docs.ecognition.com/v9.5.0/eCognition_documentation/UserGuide Developer/6 About Classification.htm
Esposito, M., Crimaldi, M., Cirillo, V., Sarghini, F., & Maggio, A. (2021). Drone and sensor technology for sustainable weed management: A review. Chemical and Biological Technologies in Agriculture, 8(1), 1–11. https://doi.org/10.1186/S40538-021-00217-8
Fernández-Quintanilla, C., Peña, J. M., Andújar, D., Dorado, J., Ribeiro, A., & López-Granados, F. (2018). Is the current state of the art of weed monitoring suitable for site-specific weed management in arable crops? Weed Research, 58(4), 259–272. https://doi.org/10.1111/WRE.12307
Gitelson, A., & Merzlyak, M. N. (1994). Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 143(3), 286–292. https://doi.org/10.1016/S0176-1617(11)81633-0
Guan, S., Fukami, K., Matsunaka, H., Okami, M., Tanaka, R., Nakano, H., Sakai, T., Nakano, K., Ohdan, H., & Takahashi, K. (2019). Assessing correlation of high-resolution NDVI with fertilizer application level and yield of rice and wheat crops using small UAVs. Remote Sensing, 11(2), 112. https://doi.org/10.3390/RS11020112
Horler, D. N. H., Dockray, M., & Barber, J. (1983). The red edge of plant leaf reflectance. International Journal of Remote Sensing, 4(2), 273–288.
Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6. https://doi.org/10.1007/S11676-020-01155-1/FIGURES/2
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-X
Johnson, G. E., Achutuni, V. R., Thiruvengadachari, S., & Kogan, F. (1993). The role of NOAA satellite data in drought early warning and monitoring: Selected case studies. Drought Assessment, Management, and Planning: Theory and Case Studies. https://doi.org/10.1007/978-1-4615-3224-8_3
Fawagreh, K., Gaber, M. M., & Elyan, E. (2014). Random forests: From early developments to recent advancements. Systems Science and Control Engineering, 2(1), 602–609. https://doi.org/10.1080/21642583.2014.956265
Lamb, D. W., & Brown, R. B. (2001). PA—Precision agriculture: Remote-sensing and map** of weeds in crops. Journal of Agricultural Engineering Research, 78(2), 117–125. https://doi.org/10.1006/JAER.2000.0630
Lamb, D. W., Weedon, M. M., & Rew, L. J. (1999). Evaluating the accuracy of map** weeds in seedling crops using airborne digital imaging: Avena spp. in seedling triticale. Weed Research, 39(6), 481–492. https://doi.org/10.1046/J.1365-3180.1999.00167
Longchamps, L., Panneton, B., Simard, M. J., & Leroux, G. D. (2013). A technique for high-accuracy ground-based continuous weed map** at field scale. Transactions of the ASABE, 56(6), 1523–1533. https://doi.org/10.13031/TRANS.56.10110
López-Granados, F., Torres-Sánchez, J., de Castro, A. I., Serrano-Pérez, A., Mesas-Carrascosa, F. J., & Peña, J. M. (2016). Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery. Agronomy for Sustainable Development, 36(4), 1–12. https://doi.org/10.1007/S13593-016-0405-7/TABLES/1
López-Granados, F., Torres-Sánchez, J., Serrano-Pérez, A., de Castro, A. I., Mesas-Carrascosa, F.-J., & Peña, J.-M. (2015). Early season weed map** in sunflower using UAV technology: Variability of herbicide treatment maps against weed thresholds. Precision Agriculture, 17(2), 183–199. https://doi.org/10.1007/S11119-015-9415-8
Lottes, P., Khanna, R., Pfeifer, J., Siegwart, R., & Stachniss, C. (2017). UAV-based crop and weed classification for smart farming. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 3024–3031). https://doi.org/10.1109/ICRA.2017.7989347
Mink, R., Dutta, A., Peteinatos, G. G., Sökefeld, M., Engels, J. J., Hahn, M., & Gerhards, R. (2018). Multi-temporal site-specific weed control of Cirsium arvense (L.) Scop. and Rumex crispus L. in maize and sugar beet using unmanned aerial vehicle based map**. Agriculture, 8(5), 65. https://doi.org/10.3390/AGRICULTURE8050065
Liaqat, M. U., Cheema, M. J. M., Huang, W., Mahmood, T., Zaman, M., & Khan, M. M. (2017). Evaluation of MODIS and Landsat multiband vegetation indices used for wheat yield estimation in irrigated Indus Basin. Computers and Electronics in Agriculture, 138, 39–47. https://doi.org/10.1016/j.compag.2017.04.006
Narrowband Greenness. (2022) from https://www.l3harrisgeospatial.com/docs/NarrowbandGreenness.html
Negash, L., Kim, H. Y., & Choi, H. L. (2019). Emerging UAV Applications in Agriculture. In 7th International conference on robot intelligence technology and applications, RiTA 2019 (pp. 254–257). https://doi.org/10.1109/RITAPP.2019.8932853
Oerke, E.-C. (2006). Crop losses to pests. The Journal of Agricultural Science, 144(1), 31–43. https://doi.org/10.1017/S0021859605005708
Reynolds, C. A., Yitayew, M., Slack, D. C., Hutchinson, C. F., Huete, A., & Petersen, M. S. (2000). Estimating crop yields and production by integrating the FAO crop specific water balance model with real-time satellite data and ground-based ancillary data. International Journal of Remote Sensing, 21(18), 3487–3508.
Sa, I., Popović, M., Khanna, R., Chen, Z., Lottes, P., Liebisch, F., Nieto, J., Stachniss, C., Walter, A., & Siegwart, R. (2018). WeedMap: A large-scale semantic weed map** framework using aerial multispectral imaging and deep neural network for precision farming. Remote Sensing, 10(9), 1423. https://doi.org/10.3390/RS10091423
Sellers, P. J. (2007). Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing, 6(8), 1335–1372. https://doi.org/10.1080/01431168508948283
Slaughter, D. C., Giles, D. K., & Downey, D. (2008). Autonomous robotic weed control systems: A review. Computers and Electronics in Agriculture, 61(1), 63–78. https://doi.org/10.1016/J.COMPAG.2007.05.008
Story, M., & Congalton, R. G. (1986). Accuracy assessment: A user’s perspective. Photogrammetric Engineering and Remote Sensing, 52(3), 397–399.
Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2015). Remote sensing and image interpretation (7th ed.). Wiley.
USGS. (2018). NDVI, the Foundation for Remote Sensing Phenology. https://www.usgs.gov/core-sciencesystems/eros/phenology/science/ndvi-foundation-remote-sensing-phenology?qt-science_center_objects=0#qtscience_center_objects
Vani, V., & Mandla, V. R. (2017). Comparative study of NDVI and SAVI vegetation indices in Anantapur district semi-arid areas. International Journal of Civil Engineering and Technology, 8(4), 559–566.
Vrindts, E., De Baerdemaeker, J., & Ramon, H. (2002). Weed detection using canopy reflection. Precision Agriculture, 3(1), 63–80. https://doi.org/10.1023/A:1013326304427/METRICS
Wahab, I., Hall, O., & Jirström, M. (2018). Remote sensing of yields: Application of UAV imagery-derived NDVI for estimating maize vigor and yields in complex farming systems in Sub-Saharan Africa. Drones, 2(3), 28. https://doi.org/10.3390/DRONES2030028
Weih, R. C., & Riggan, N. D. (2010). Object-based classification vs. pixel-based classification: Comparative importance of multi-resolution imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(4), C7.
Weis, M., Peteinatos, G. G., Andujar, D., et al. (2014). Potential use of ground-based sensor technologies for weed detection. Pest Management Science, 70, 190–199. https://doi.org/10.1002/ps.3677
Xu, Y., Yang, Y., Chen, X., & Liu, Y. (2022). Bibliometric analysis of global NDVI research trends from 1985 to 2021. Remote Sensing, 14(16), 3967. https://doi.org/10.3390/RS14163967/S1
Yang, D. (2021). Research on farmland crop classification based on UAV multispectral remote sensing images. International Journal of Precision Agricultural Aviation, 4(1), 29–35. https://doi.org/10.33440/j.ijpaa.20210401.153
Zohaib, A., Abbas, T., & Tabassum, T. (2016). Weeds cause losses in field crops through allelopathy. Notulae, Scientia Biologicae, 8(1), 47–56. https://doi.org/10.15835/NSB819752
Zwiggelaar, R. (1998). A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops. Crop Protection, 17(3), 189–206. https://doi.org/10.1016/S0261-2194(98)00009-X
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We are grateful to ASL for making the drone data available in the open-source domain for performing studies on crop and weed map**.
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SS developed the workflow and carried out the analysis, preparation and correction of the manuscript. VK developed workflow and guidance on analysis.
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Stephen, S., Kumar, V. Detection and Analysis of Weed Impact on Sugar Beet Crop Using Drone Imagery. J Indian Soc Remote Sens 51, 2577–2597 (2023). https://doi.org/10.1007/s12524-023-01782-1
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DOI: https://doi.org/10.1007/s12524-023-01782-1