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IJSTR >> Volume 10 - Issue 6, June 2021 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Classification Of Wheat Crop Using Remotely Sensed Multi-Spectralplanet-Scope Temporal Data

[Full Text]



Awab Ur Rashid Durrani, Arbab Masood Ahmad



Remote sensing, Classification, Temporal, Artificial neural network, Support vectormachine, Minimum distance, Planetscope.



Agriculture plays a vital role in the economies of developing countries and provide the mainsource of food, income and employment for general public. Monitoring and assessment of the crop yieldis a crucial task and is critical in ensuring good agricultural management. We propose the monitoring andclassification of wheat crops through remote sensing by utilizing satellite imagery. In our research work,we have utilized multi-spectral imagery of Planet-Scope satellite for the classification of wheat crop. Theimagery used is a temporal stack of remotely sensed imageries obtained on various dates with reference tothe phenological cycle of wheat.We employ three different machine learning classifiers i.e., Artificial NeuralNetwork (ANN), Support Vector Machine (SVM) and Minimum Distance (MD) classifier for the wheatcrop classification. Confusion matrix and Kappa Coefficient(Kappa Coefficient) analyzes the performanceof these three classifiers. The results obtained shows that ANN with an overall accuracy of 98:7031%and Kappa Coefficient equivalent to 0:9825 outperforms the SVM and MD classifiers having the overallaccuracy of 85:2005% and 73:1604% and Kappa Coefficient values of 0:8097 and 0:6455, respectively.



[1] A. A. Chandio, J. Yuansheng, T. Rahman, M. N. Khan, X. Guangshun, and Z. Zhi, “Analysis of agricultural subsectors contribution growth ratein the agriculture gdp growth rate of pakistan,” International Journal ofHumanities and Social Science Invention, vol. 4, no. 8, pp. 101–105, 2015.
[2] “Pakistan grain and feed annual,” ://www.agripunjab.gov.pk/overview
[3] “Agriculture department pakistan statistics,” https://apps.fas.usda.gov/newgainapi/api/report/downloadreportbyfilename?filename=Grain%20and%20Feed%20Annual_Islamabad_Pakistan_3-28-2019.pdf, accessed: 2020-05-2.
[4] D. Poli, F. Remondino, E. Angiuli, and G. Agugiaro, “Radiometric andgeometric evaluation of geoeye-1, worldview-2 and pléiades-1a stereoimages for 3d information extraction,” ISPRS Journal of Photogrammetryand Remote Sensing, vol. 100, pp. 35–47, 2015.
[5] R. Hooda, M. Yadav, and M. Kalubarme, “Wheat production estimationusing remote sensing data: An indian experience,” in Workshop Proceedings: Remote Sensing Support to Crop Yield Forecast and Area Estimates, Stresa, Italy. 30 Nov.–1 Dec. 2006., 2006, pp. 85–89.
[6] M.-C. Cheng and C. Zhang, “‘formosat-2 for international societal benefits,” Remote Sensing, vol. 2016, 2016.
[7] P. Team, “Planet application program interface: In space for life on earth,” San Francisco, CA, vol. 2017, p. 40, 2017.
[8] “Finance division,” http://www.finance.gov.pk, accessed: 2020-02-2.
[9] H. Mu, L. Zhou, X. Dang, and B. Yuan, “Winter wheat yield estimationfrommultitemporal remote sensing images based on convolutional neuralnetworks,” in 2019 10th International Workshop on the Analysis of MultitemporalRemote Sensing Images (MultiTemp). IEEE, 2019, pp. 1–4.
[10] Ö. Vanlı, A. Sabuncu, and Z. D. U. Avcı, “Regional classification of winterwheat using remote sensing data in southeastern turkey,” in 2019 8th InternationalConference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE, 2019, pp. 1–4.
[11] F. Xu, Z. Li, S. Zhang, N. Huang, Z. Quan, W. Zhang, X. Liu, X. Jiang, J. Pan, and A. V. Prishchepov, “Mapping winter wheat with combinationsof temporally aggregated sentinel-2 and landsat-8 data in shandongprovince, china,” Remote Sensing, vol. 12, no. 12, p. 2065, 2020.
[12] C.-j. LI, J.-h. WANG, W. Qian, D.-c. WANG, X.-y. SONG, W. Yan, and W.-j. HUANG, “Estimating wheat grain protein content using multitemporalremote sensing data based on partial least squares regression,” Journal of Integrative Agriculture, vol. 11, no. 9, pp. 1445–1452, 2012.
[13] P. Yang, Q. Zhou, Z. Chen, Y. Zha, W. Wu, and R. Shibasaki, “Estimationof regional crop yield by assimilating multi-temporal tm images into cropgrowth model,” in 2006 IEEE International Symposium on Geoscience andRemote Sensing. IEEE, 2006, pp. 2259–2262.
[14] S. Xiaoyu, C. Bei, Y. Guijun, and F. Haikuan, “Comparison of winterwheat growth with multi-temporal remote sensing imagery,” in Proceedingsof Conference Series on Earth and Environmental Science, 2014, pp. 1–7.
[15] J. Liu, P. Chen, and X. Xu, “Estimating wheat coverage using multispectralimages collected by unmanned aerial vehicles and a new sensor,” in 2018 7th International Conference on Agro-geoinformatics (Agrogeoinformatics). IEEE, 2018, pp. 1–5.
[16] M. Du and N. Noguchi, “Monitoring of wheat growth status and mappingof wheat yield’s within-field spatial variations using color images acquiredfromuav-camera system,” Remote sensing, vol. 9, no. 3, p. 289, 2017.
[17] W. Zhuo, J. Huang, L. Li, X. Zhang, H. Ma, X. Gao, H. Huang, B. Xu, and X. Xiao, “Assimilating soil moisture retrieved from sentinel-1 andsentinel-2 data into wofost model to improve winter wheat yield estimation,” Remote Sensing, vol. 11, no. 13, p. 1618, 2019.
[18] L. Liangyun, W. Jihua, S. Xiaoyu, L. Cunjun, H. Wenjiang, and Z. Chunjiang, “Study on winter wheat yield estimation model based on ndvi andseedtime,” in IGARSS 2004. 2004 IEEE International Geoscience andRemote Sensing Symposium, vol. 6. IEEE, 2004, pp. 4045–4047.
[19] N.-B. Chang, C. Mostafiz, Z. Sun, W. Gao, and C.-F. Chen, “Developinga prototype satellite-based cyber-physical system for smart wastewatertreatment,” in 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2017, pp. 339–344.
[20] Y. Zhongcheng, W. Fengge, and Z. Junsuo, “Research of a constructionmethod of space-based cyber-physical system,” in 2016 Chinese Controland Decision Conference (CCDC). IEEE, 2016, pp. 6862–6866.
[21] A. Mirkouei, “A cyber-physical analyzer system for precision agriculture,” J Environ SciCurr Res, vol. 3, p. 016, 2020.
[22] National Center for Big Data Cloud Computing, UET Peshawar, GeoSurvey. [Online]. Available: https://play.google.com/store/apps/details?id=com.ncbc.survey.gis&hl=en
[23] K. Perumal and R. Bhaskaran, “Supervised classification performance ofmultispectral images,” arXiv preprint arXiv:1002.4046, 2010.
[24] M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, “Supportvector machines,” IEEE Intelligent Systems and their applications, vol. 13, no. 4, pp. 18–28, 1998.
[25] M. Ustuner, F. B. Sanli, and B. Dixon, “Application of support vectormachines for landuse classification using high-resolution rapideye images: A sensitivity analysis,” European Journal of Remote Sensing, vol. 48, no. 1, pp. 403–422, 2015.
[26] A. Wacker and D. Landgrebe, “Minimum distance classification in remotesensing,” LARS Technical Reports, p. 25, 1972.
[27] K. Yang, A. Pan, Y. Yang, S. Zhang, S. H. Ong, and H. Tang, “Remotesensing image registration using multiple image features,” Remote Sensing, vol. 9, no. 6, p. 581, 2017.
[28] B. Ankayarkanni and A. E. S. Leni, “A new statistical rule model forimage retrieval system of remote sensing images,” in 2016 InternationalConference on Circuit, Power and Computing Technologies (ICCPCT). IEEE, 2016, pp. 1–5