Problems of Multispectral Image Processing in Agriculture
DOI:
https://doi.org/10.18372/1990-5548.83.19866Keywords:
multispectral imaging, precision agriculture, crop yield prediction, satellite-based monitoring, drone-based monitoring, spectral indices, convolutional neural networkAbstract
This study provides a comprehensive comparative analysis of satellite-based and drone-based imaging platforms for agricultural monitoring, with particular emphasis on multispectral imaging capabilities. Our analysis reveals that while satellite systems offer broad coverage and cost-effectiveness for large-scale monitoring, drone-based platforms provide superior spatial resolution (up to 2.5 cm/pixel) and greater flexibility for targeted data acquisition, making them ideal for medium-sized agricultural plots. The research examines key imaging technologies and platforms, including the Sentinel-2 satellite system and drone-mounted sensors such as the MicaSense RedEdge-MX, evaluating their performance across critical agricultural applications. The paper further explores the implementation of convolutional neural networks for processing multispectral data, demonstrating their exceptional capability in performing crucial agricultural tasks including crop classification, disease detection, and stress assessment. By incorporating spectral indices, thermal indices and biophysical parameters (LAI, chlorophyll content) into neural network training, we develop a robust framework for agricultural monitoring and yield prediction. This research contributes both to the theoretical understanding of remote sensing in agriculture and provides practical guidance for implementing precision agriculture solutions that enhance productivity and sustainability in modern farming systems.
References
A. F. H. Goetz, G. Vane, J. E. Solomon, & B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science, 228(4704), 1147–1153, 1985. https://doi.org/10.1126/science.228.4704.1147
R. D. Jackson, S. B. Idso, R. J. Reginato, & P. J. Pinter, “Canopy temperature as a crop water stress indicator,” Water Resources Research, 17(4), 1133–1138, 1981. https://doi.org/10.1029/WR017i004p01133
D. B. Lobell, D. Thau, C. Seifert, E. Engle, & B. Little, “A scalable satellite-based crop yield mapper,” Remote Sensing of Environment, 164, 324–333, 2015. https://doi.org/10.1016/j.rse.2015.02.016
D. J. Mulla, “Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps,” Biosystems Engineering, 114(4), 358–371, 2013. https://doi.org/10.1016/j.biosystemseng.2012.08.009
L. Ma, Y. Liu, X. Zhang, Y. Ye, G. Yin, & B. A. Johnson, “Deep learning in remote sensing applications: A meta-analysis and review,” ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166–177, 2019. https://doi.org/10.1016/j.isprsjprs.2019.04.015
M. Drusch, U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, ... & P. Bargellini, “Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services,” Remote Sensing of Environment, 120, 25–36, 2012. https://doi.org/10.1016/j.rse.2011.11.026
C. Zhang, & J. M. Kovacs, “The application of small unmanned aerial systems for precision agriculture: a review,” Precision Agriculture, 13(6), 693–712, 2012. https://doi.org/10.1007/s11119-012-9274-5
A. Matese, P. Toscano, S. F. Di Gennaro, L. Genesio, F. P. Vaccari, J. Primicerio, ... & M. Mancini, “Intercomparison of UAV, aircraft, and satellite remote sensing platforms for precision viticulture,” Remote Sensing, 7(3), 2971–2990, 2015. https://doi.org/10.3390/rs70302971
J. Boulent, P. Foucher, J. Théau, & S. St-Charles, “Convolutional neural networks for the automatic identification of plant diseases. Computers and Electronics in Agriculture,” 2019. https://doi.org/10.3389/fpls.2019.00941
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, & R. Moore, “Google Earth Engine: Planetary-scale geospatial analysis for everyone” Remote Sensing of Environment, 202, 18–27, 2017. https://doi.org/10.1016/j.rse.2017.06.031
J. G. P. W. Clevers, & L. Kooistra, “Using hyperspectral remote sensing data for retrieving canopy chlorophyll and nitrogen content,” IEEE Transactions on Geoscience and Remote Sensing, 2012. https://doi.org/10.1109/JSTARS.2011.2176468
J. A. Foley, N. Ramankutty, K. A. Brauman, E. S. Cassidy, J. S. Gerber, M. Johnston,, … & D. P. Zaks, Moscow, Solutions for a cultivated planet. Nature, 478, 2011, pp. 337–342. https://doi.org/10.1038/nature10452
D. B. Lobell, W. Schlenker, & J. Costa-Roberts, Climate trends and global crop production since Science, 333(6042), 616–620, 1980. https://doi.org/10.1126/science.1204531
D. B. Lobell, M. B. Burke, C. Tebaldi, , M. D. Mastrandrea, W. P. Falcon, & R. L. Naylor, “Prioritizing climate change adaptation needs for food security in 2030,” Science, 319(5863), 607–610, 2007. https://doi.org/10.1126/science.1152339
A. K. Mahlein,, T. Rumpf, P. Welke, H. W. Dehne, L. Plümer, U. Steiner,, & E. C. Oerke, “Development of spectral indices for detecting and identifying plant diseases, Remote Sensing of Environment, 128, 21–30, 2012. https://doi.org/10.1016/j.rse.2012.09.019
J. Pretty, “Agricultural sustainability: Concepts, principles, and evidence, Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1491), 447–465, 2008. https://doi.org/10.1098/rstb.2007.2163
P. S. Thenkabail, J. G. Lyon, & A. Huete, “Biophysical and Biochemical Characterization and Plant Species Studies,” 2018. https://doi.org/10.1201/9780429431180
Y. Wang,, H. Zhang,, & X. Li, “Urban Tree Canopy Mapping Based on Double-Branch Convolutional Neural Network and Multi-Temporal High Spatial Resolution Satellite Imagery,” 2023. https://doi.org/10.3390/rs15030765
H. Petersson, D. Gustavsson, and D. Bergstrom, “Deep learning approaches for hyperspectral image analysis: A review. Remote Sensing,” 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), 2016. https://doi.org/10.1109/IPTA.2016.7820963
J. Xue, & B. Su, “Significant remote sensing vegetation indices: A review of developments and applications,” Sensors, 2017. https://doi.org/10.1155/2017/1353691
Lin Ke, Gong Liang, Huang Yixiang, Liu Chengliang, and Pan Junsong, “Deep learning-based segmentation and quantification of cucumber powdery mildew using convolutional neural networks, Computers and Electronics in Agriculture, vol. 10, 2019. https://doi.org/10.3389/fpls.2019.00155
R. Gebbers and V. Adamchuck, “Precision Agriculture and Food Security,” vol. 327, no. 5967, 828–831, 2010, https://doi.org/10.1126/science.1183899
C. Toth, and G. Jóźków, “Remote sensing platforms and sensors: A survey,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 115, pp. 22–36, May 2016, https://doi.org/10.1016/j.isprsjprs.2015.10.004
R. Martínez-Peña, S. Vélez, R. Vacas, H. Martín, and S. Álvarez, “Remote Sensing for Sustainable Pistachio Cultivation and Improved Quality Traits Evaluation through Thermal and Non-Thermal UAV Vegetation Indices,” Appl. Sci., 13(13), 7716, 2023, https://doi.org/10.3390/app13137716
Anne M. Thompson, Herman G. J. Smit, Debra E. Kollonige, Ryan M. Stauffer, Field Measurements for Passive Environmental Remote Sensing, Instrumentation, Intensive Campaigns, and Satellite Applications 2023, pp. 57–78. https://doi.org/10.1016/B978-0-12-823953-7.00011-3
S. P. Mohanty, D. P. Hughes, & M. Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in Plant Science, 7, 1419. 2016. https://doi.org/10.3389/fpls.2016.01419
A. Kamilaris, &, F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70–90, April 2018. https://doi.org/10.1016/j.compag.2018.02.016
M. Dyrmann, H. Karstoft, & H. S. Midtiby, “Plant species classification using deep convolutional neural networks,” Biosystems Engineering, 151, pp. 72–81, 2016. https://doi.org/10.1016/j.biosystemseng.2016.08.024
J. G. A. Barbedo, “Plant disease identification from individual lesions and spots using deep learning,” Biosystems Engineering, vol. 180, pp. 96–107, ref. 22. 2019, https://doi.org/10.1016/j.biosystemseng.2019.02.002
NASA. Harmonized Landsat and Sentinel-2 (HLS) Dataset. https://hls.gsfc.nasa.gov/ . 2021.
ESA. Sentinel-2 User Handbook. European Space Agency. 2021.
USGS. Landsat 9 Mission Overview. United States Geological Survey. 2021.
Planet. PlanetScope Imagery. 2023. https://www.planet.com/products/satellite-monitoring/
Sinergise. EO Browser. 2023. https://apps.sentinel-hub.com/eo-browser/
DJI. Phantom 4 Multispectral. 2021. https://www.dji.com/global/support/product/p4-multispectral
Parrot. Sequoia Multispectral Sensor. 2021. https://www.parrot.com/global/drones/parrot-sequoia
MicaSense. Altum Multispectral Camera. 2021. https://www.micasense.com/altum
Pix4D. Drone mapping software. 2023. https://www.pix4d.com/
DroneDeploy. Cloud-based drone software. 2023. https://www.dronedeploy.com/
ASD. FieldSpec Handheld 2 Spectroradiometer. 2021. https://www.malvernpanalytical.com/en/products/product-range/asd-range/fieldspec-range/fieldspec-handheld-2
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