Fig. 2. Flow chart of the image processing and analysis steps.
Fig. 3. Generated normalized difference vegetation index (NDVI) map of the whole test site (upper) and detected weed map based on NDVI value (lower). NDVI value reflects chlorophyll content.
Fig. 4. Normalized difference vegetation index (NDVI) histogram for weed detection with threshold value based on inflection point.
Fig. 5. Normalized difference vegetation index (NDVI) histogram for weed detection with a threshold value based on inflection point.
Table 1. Specifications of developed unmanned aerial vehicle (UAV) platform.
Fig. 1. (a) View of a commercial multi-spectral camera with sunshine sensor (Parrot Sequoia) and (b) spectral response of four bands in the camera.
Table 2. Technical specifications of multi-spectral camera.
Table 3. Experimental unmanned aerial vehicle (UAV) flight details.
Table 4. Number of grids extracted from the generated grid map.
References
- Alexandridis, T., A. A. Tamouridou, X. E. Pantazi, A. Lagopodi, J. Kashefi, G. Ovakoglou, V. Polychronos, and D. Moshou. 2017. Novelty Detection Classifiers in Weed Mapping: Silybum Marianum Detection on Uav Multispectral Images. Sens. 17(9) : 2007. https://doi.org/10.3390/s17092007
- Barroso, J., C. Fernandez-Quintanilla, B. Maxwell, and L. Rew. 2004. Simulating the Effects of Weed Spatial Pattern and Resolution of Mapping and Spraying on Economics of Site-Specific Management. Weed Res. 44(6) : 460-468. https://doi.org/10.1111/j.1365-3180.2004.00423.x
- Brown, R. B. and S. D. Noble. 2005. Site-Specific Weed Management: Sensing Requirements-What Do We Need to See?. Weed Sci. 53(2) : 252-258. https://doi.org/10.1614/WS-04-068R1
- Cardina, J., G. A. Johnson, and D. H. Sparrow. 1997. The Nature and Consequence of Weed Spatial Distribution. Weed Sci. 45(3) : 364-373. https://doi.org/10.1017/S0043174500092997
- Christensen, S., E. Nordbo, T. Heisel, and A. Walter. 1998. Overview of Developments in Precision Weed Management, Issues of Interest and Future Directions Being Considered in Europe. CRC Weed Manage Syst. 3-13.
- Christensen, S., H. T. Sogaard, P. Kudsk, M. Norremark, I. Lund, E. S. Nadimi, and R. Jorgensen. 2009. Site-Specific Weed Control Technologies. Weed Res. 49(3) : 233-241. https://doi.org/10.1111/j.1365-3180.2009.00696.x
- Gerhards, R. and H. Oebel. 2006. Practical Experiences with a System for Site-Specific Weed Control in Arable Crops Using Real-Time Image Analysis and Gps-Controlled Patch Spraying. Weed Res. 46(3) : 185-193. https://doi.org/10.1111/j.1365-3180.2006.00504.x
- Gerhards, R., M. Sokefeld, C. Timmermann, W. Kuhbauch, and M. Williams. 2002. Site-Specific Weed Control in Maize, Sugar Beet, Winter Wheat, and Winter Barley. Precis Agric. 3(1) : 25-35. https://doi.org/10.1023/A:1013370019448
- Goel, P., S. Prasher, R. Patel, D. Smith, and A. DiTommaso. 2002. Use of Airborne Multi-Spectral Imagery for Weed Detection in Field Crops. Trans ASAE. 45(2) : 443.
- Johnson, G., J. Cardina, and D. Mortensen. 1997. Site-Specific Weed Management: Current and Future Directions. Site-Specific Manage Agric. 131-147.
- Jurado-Exposito, M., F. Lopez-Granados, L. Garcia-Torres, A. Garcia-Ferrer, M. S. de la Orden, and S. Atenciano. 2003. Multi-Species Weed Spatial Variability and Site-Specific Management Maps in Cultivated Sunflower. Weed Sci. 51(3) : 319-328. https://doi.org/10.1614/0043-1745(2003)051[0319:MWSVAS]2.0.CO;2
- Kim, D.-W., H. Yun, S.-J. Jeong, Y.-S. Kwon, S.-G. Kim, W. Lee, and H.-J. Kim. 2018. Modeling and Testing of Growth Status for Chinese Cabbage and White Radish with UAV-Based RGB Imagery. Remote Sens. 10(4) : 563. https://doi.org/10.3390/rs10040563
- LOPEZ-GRANADOS, F. 2011. Weed Detection for Site-Specific Weed Management: Mapping and Real-Time Approaches. Weed Res. 51(1) : 1-11. https://doi.org/10.1111/j.1365-3180.2010.00829.x
- Oerke, E.-C. 2006. Crop Losses to Pests. The Journal of Agricultural Science. 144(1) : 31-43. https://doi.org/10.1017/S0021859605005708
- Pena, J. M., J. Torres-Sanchez, A. I. de Castro, M. Kelly, and F. Lopez-Granados. 2013. Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images. PloS One. 8(10) : e77151. https://doi.org/10.1371/journal.pone.0077151
- Perez-Ortiz, M., J. Pena, P. A. Gutierrez, J. Torres-Sanchez, C. Hervas-Martinez, and F. Lopez-Granados. 2015. A Semi-Supervised System for Weed Mapping in Sunflower Crops Using Unmanned Aerial Vehicles and a Crop Row Detection Method. Appl Soft Comput. 37 : 533-544. https://doi.org/10.1016/j.asoc.2015.08.027
- Rew, L., G. Cussans, M. Mugglestone, and P. Miller. 1996. A Technique for Mapping the Spatial Distribution of Elymus Repots, with Estimates of the Potential Reduction in Herbicide Usage from Patch Spraying. Weed Res. 36(4) : 283-292. https://doi.org/10.1111/j.1365-3180.1996.tb01658.x
- Ribeiro, A., C. Fernandez-Quintanilla, J. Barroso, M. Garcia-Alegre, and J. Stafford. 2005. Development of an Image Analysis System for Estimation of Weed Pressure. Precis Agric. 5 : 169-174.
- Richardson, A. D., S. P. Duigan, and G. P. Berlyn. 2002. An Evaluation of Noninvasive Methods to Estimate Foliar Chlorophyll Content. New Phytol. 153(1) : 185-194. https://doi.org/10.1046/j.0028-646X.2001.00289.x
- Swinton, S. M. 2005. Economics of Site-Specific Weed Management. Weed Sci. 53(2) : 259-263. https://doi.org/10.1614/WS-04-035R2
- Thompson, K. and J. P. Grime. 1979. Seasonal Variation in the Seed Banks of Herbaceous Species in Ten Contrasting Habitats. J Ecol. 893-921. https://doi.org/10.1111/j.0022-0477.2004.00928.x
- Thorp, K. and L. Tian. 2004. A Review on Remote Sensing of Weeds in Agriculture. Precis Agric. 5(5) : 477-508. https://doi.org/10.1007/s11119-004-5321-1
- Tian, L., J. F. Reid, and J. W. Hummel. 1999. Development of a Precision Sprayer for Site-Specific Weed Management. Trans ASAE. 42(4) : 893. https://doi.org/10.13031/2013.13269
- Torres-Sanchez, J., F. Lopez-Granados, A. I. De Castro, and J. M. Pena-Barragan. 2013. Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management. PloS One. 8(3) : e58210. https://doi.org/10.1371/journal.pone.0058210
Cited by
- Can Commercial Low-Cost Drones and Open-Source GIS Technologies Be Suitable for Semi-Automatic Weed Mapping for Smart Farming? A Case Study in NE Italy vol.13, pp.10, 2019, https://doi.org/10.3390/rs13101869