Monitoring of potato crops based on multispectral image feature extraction with vegetation indices
Tóm tắt
The number of individuals working in agriculture is decreasing due to a labor shortage. When switching from manual to automation mode, image mapping must survey soil testing and plant vegetation development. The existing studies met several shortcomings in terms of higher cost remote sensing tools, higher execution time, higher computational complexity, and so on. To tackle these issues, we proposed a multispectral images feature extraction with vegetation indices for potato crops monitoring. This article discusses the many characteristics that are used to identify plants, such as plant count, plant height estimation, plant area evaluation, plant distance, crop vegetation growth detection, damaged area identification, and higher-lower vegetation. Calculating Crop growth days and minimizing leaf or root damage are provided by QGIS and Pix4Dmapper software. It measured all soil testing parameters as well as plant vegetative development. We provided a raster function for aggregation based on a low-resolution image for estimating plant area evaluation and parameters in a growing zone for higher cultivation plants. This research revealed 98% vegetation indices as well as plant characteristics. Various key parameters such as leaf area density, plants coordinates, plant height, the proportion of diffuse light for incident sunlight, and solar zenith angle are examined. The Quality Report of Vegetation Indices Value resulted in an accuracy of 96% when good or terrible weather conditions exist. All Vegetation indices values are compared with other existing technique results in terms of maximum and minimum vegetation to verify its superiority.
Tài liệu tham khảo
Budiharto, W., Chowanda, A., Gunawan, A. A., Irwansyah, E., & Suroso, J. S. (2019). A review and progress of research on autonomous drone in agriculture, delivering items and geographical information systems (GIS). In 2nd World symposium on communication engineering (WSCE) (pp. 205–209). IEEE. https://doi.org/10.1109/WSCE49000.2019.9041004
Cucho-Padin, G., Loayza, H., Palacios, S., Balcazar, M., Carbajal, M., & Quiroz, R. (2020). Development of low-cost remote sensing tools and methods for supporting smallholder agriculture. Applied Geomatics, 12(3), 247–263. https://doi.org/10.1007/s12518-019-00292
Cui, C., Zhang, W., Hong, Z., & Meng, L. (2020). Forecasting NDVI in multiple complex areas using neural network techniques combined feature engineering. International Journal of Digital Earth, 3(12), 1733–1749. https://doi.org/10.1080/17538947.2020.1808718
Fan, X., & Liu, Y. (2017). A comparison of NDVI intercalibration methods. International Journal of Remote Sensing, 38(19), 5273–5290. https://doi.org/10.1080/01431161.2017.1338
Filipovic, V., Nedic, N., & Stojanovic, V. (2011). Robust identification of pneumatic servo actuators in the real situations. Forschung Im Ingenieurwesen, 75(4), 183–196.
Gandhi, G. M., Parthiban, S., Thummalu, N., & Christy, A. (2015). NDVI: Vegetation change detection using remote sensing and gis—A case study of Vellore District. Procedia Computer Science, 57, 1199–1210.
Guha, S., & Govil, H. (2021). An assessment on the relationship between land surface temperature and normalized difference vegetation index. Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 23(2), 1944–1963.
Jorge, J., Vallbé, M., & Soler, J. A. (2019). Detection of irrigation inhomogeneities in an olive grove using the NDRE vegetation index obtained from UAV images. European Journal of Remote Sensing, 52(1), 169–177. https://doi.org/10.1080/22797254.2019.1572459
Jose, J., Gautam, N., Tiwari, M., Tiwari, T., Suresh, A., Sundararaj, V. & Rejeesh, M.R. (2021). An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion. Biomedical Signal Processing and Control, 66, 102480
MorlinCarneiro, F., AngeliFurlani, C. E., Zerbato, C., Candida de Menezes, P., da Silva Gírio, L. A., & Freire de Oliveira, M. (2020). Comparison between vegetation indices for detecting spatial and temporal variabilities in soybean crop using canopy sensors. Precision Agriculture, 21, 979–1007. https://doi.org/10.1007/s11119-019-09704-3
Munyati, C., & Mboweni, G. (2013). Variation in NDVI values with change in spatial resolution for semi-arid Savanna vegetation: A case study in northwestern South Africa. International Journal of Remote Sensing, 34(7), 2253–2267.
Panda, S. S., Ames, D. P., & Panigrahi, S. (2010). Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sensing, 2(3), 673–96. https://doi.org/10.3390/rs2030673
Robinson, N. P., Allred, B. W., Jones, M. O., Moreno, A., Kimball, J. S., Naugle, D. E., Erickson, T. A., & Richardson, A. D. (2017). A dynamic Landsat derived normalized difference vegetation index (NDVI) product for the conterminous United States. Remote Sensing, 9(8), 863. https://doi.org/10.3390/rs9080863
Sari, F., Kandemir, İ, & Ceylan, D. A. (2020). Integration of NDVI imagery and crop coverage registration system for apiary schedule. Journal of Apicultural Science, 64(1), 105–121.
Seong, N. H., Jung, D., Kim, J., & Han, K. S. (2020). Evaluation of NDVI estimation considering atmospheric and BRDF correction through Himawari-8/AHI. Asia-Pacific Journal of Atmospheric Sciences, 56, 265–274.
Sundararaj, V., Selvi, M. Opposition grasshopper optimizer based multimedia data distribution using user evaluation strategy. Multimed Tools Appl, 80, 29875–29891. https://doi.org/10.1007/s11042-021-11123-4 (2021).
Tao, H., Li, X., Paszke, W., Stojanovic, V., & Yang, H. (2021). Robust PD-type iterative learning control for discrete systems with multiple time-delays subjected to polytopic uncertainty and restricted frequency-domain. Multidimensional Systems and Signal Processing, 32(2), 671–692.
Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G. (2019). A review on UAV-based applications for precision agriculture. Information, 10(11), 349. https://doi.org/10.3390/info10110349
Tucker, C. J., Pinzon, J. E., Brown, M. E., Slayback, D. A., Pak, E. W., Mahoney, R., Vermote, E. F., & El Saleous, N. (2005). An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing, 26(20), 4485–4498. https://doi.org/10.1080/01431160500168686
Wei, T., Li, X., & Stojanovic, V. (2021). Input-to-state stability of impulsive reaction–diffusion neural networks with infinite distributed delays. Nonlinear Dynamics, 103(2), 1733–1755.
Zaitunah, A., Ahmad, A. G., & Safitri, R. A. (2018). Normalized difference vegetation index (Ndvi) analysis for land cover types using landsat 8 oli in besitang watershed, Indonesia. In InIOP Conference Series: Earth and Environmental Science (Vol. 126, No. 1, p. 012112). IOP Publishing.