Abstract
Agriculture is the main source of income for most of the people from thousands of years but still every year farmers suffer loss of crop and money, due to misinterpretation of soil or climatic conditions. In the recent years, researchers have worked to improve this state and agriculture for production of crop by analyzing soil or climate conditions. In this paper, we proposed the methods as fractal analysis and machine-learned decision system for smart and precision farming. The long-term behavior of the different parameter on which production of crop depends is analyzed using fractal analysis which is helpful for crop maintenance and also helpful in preparing the framework for the government and farmers in advance to know the total automating of the supplements, water framework, water system controls for accuracy of cultivating in the fields. Using Hurst exponent and Fractal analysis, it is observed that all the seven parameters affecting the crops follow anti-persistent behavior which shows the drawn out exchanging among high and low qualities with a definite pattern. Machine-learned system including artificial neural network, kNN Classifier, XG Boost, Random Forest classifier conclude that different decision systems show the accuracy for different crops with parameters from 95 to 99%. Random Forest classifier gave more accuracy among all classifier for testing and providing support for crop management systems. It is concluded that the proposed technique using machine-learned classifier is giving more accuracy for precise and smart farming with good crop management and helpful for the government to make the decision and formulate policies for the stack of farmers, consumers and for the development of the nation as farmers are backbone of any country.
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References
A.N. Mohammad-Reza, R. Hassan, A. Hossein, Fractal dimension of soil aggregates as an index of soil erodibility. J Hydrol 400, 305–311 (2011)
Y. Ai, Z. Chen, P. Guo et al., Fractal characteristics of synthetic soil for cut slope revegetation in the purple soil area of China. Can. J. Soil Sci. 92(2), 277–284 (2012)
S. de Bartolo, C. Fallico, G. Severino, A fractal analysis of the water retention curve. Hydrol. Process. 32(10), 1401–1405 (2018)
J.B. Bassingthwaighte, G.M. Raymond, Evaluation of the dispersional analysis method for fractal time series. Ann. Biomed. Eng. 23(4), 491–505 (1999)
N. Behzad, D. Hugh, Fractal dimension of soil fragment mass-size distribution: a critical analysis. Geoderma 245–246, 98–103 (2015)
R. Bhardwaj, K.S. Parmar, Statistical, time series and fractal analysis of full stretch of River Yamuna (India) for water quality management. Environ. Sci. Pollut. Res. 22, 397–414 (2015)
G.E.P. Box, D.R. Cox, An analysis of transformations. J. R. Stat. Soc. Ser. B 26, 211–252 (1964)
T.F. Deng, Y. Liu, Q.X. Yan, T.B. He, A.Q. Gao, Mechanical composition and soil nutrient characteristics and their relationships in typical Lonicera confusa soil of Guizhou. J. Soil Water Conserv. 28, 209–214 (2014)
F. Evelyn, J. L. Hodges. Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties (Report) (USAF School of Aviation Medicine, Randolph Field) (1951)
C. Gulser, Effect of forage cropping treatments on soil structure and relationships with fractal dimensions. Geoderma 131(1–2), 33–44 (2006)
J. Han, J. Xie, Y. Zhang, Potential role of feldspathic sandstone as a natural water retaining agent in Mu Us sandy land, Northwest China. Chin. Geogr. Sci. 22(5), 550–555 (2012)
J. Han, Y. Liu, Y. Zhang, Sand stabilization effect of feldspathic sandstone during the fallow period in Mu Us Sandy Land. J. Geogr. Sci. 25(4), 428–436 (2015)
H. Trevor, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, 2nd edn. (Springer, Berlin, 2008). ((ISBN 0-387-95284-5))
T. K. Ho. Random decision forests. In Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August 1995 (1995). pp. 278–282
T.K. Ho, The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)
S. Khriji, D.E. Houssaini, M.W. Jmal, C. Viehweger, M. Abid, O. Kanoun, Precision irrigation based on wireless sensor network. IET Sci. Meas. Technol. 8(3), 98–106 (2014)
L. Kirichenko, T. Radivilova, V. Bulakh, Machine learning in classification time series with fractal properties. Data 4, 5 (2019). https://doi.org/10.3390/data4010005
D.C. Li, T.L. Zhang, Fractal features of particle size distribution of soils in China. Soil Environ. Sci. 9, 263–265 (2000)
K. Liao, X. Lai, Z. Zhou, Q. Zhu, Applying fractal analysis to detect spatio-temporal variability of soil moisture content on two contrasting land use hillslopes. Catena 157, 163–172 (2017)
X. Liu, Z. Li, P. Li, Particle fractal dimension and total phosphorus of soil in a typical watershed of Yangtze River, China. Environ. Earth Sci. 73(10), 6091–6099 (2015)
X. Liu, G. Zhang, G.C. Heathman, Y. Wang, C.-H. Huang, Fractal features of soil particle-size distribution as affected by plant communities in the forested region of mountain Yimeng, China. Geoderma 154(1–2), 123–130 (2009)
C. M. Martin, Crop yield prediction using artificial neural networks and genetic algorithms, M.Sc. thesis, University of Athens, Georgia (2007)
D.K. McClish, Analyzing a portion of the ROC curve. Med. Decis. Mak. 9(3), 190–195 (1989). https://doi.org/10.1177/0272989X8900900307
M. Meysam, S. Mahmoud, H.M. Mohammad, D. Nasser, Characterizing spatial variability of soil textural fractions and fractal parameters derived from particle size distributions. Pedosphere 29(2), 224–234 (2019)
C.Y. Miao, Y.F. Wang, X. Wei, X. Xu, W. Shi, Fractal characteristics of soil particles in surface layer of black soil. Chin. J. Appl. Ecol. 18, 1987–1993 (2007)
T. Mitchell, Machine Learning (McGraw Hill, New York, 1997). ((0-07-042807-7. OCLC 36417892))
H. Ni, L. Zhang, D. Zhang, X. Wu, X. Fu, Weathering of Pisha-sandstones in the wind-water erosion crisscross region on the Loess Plateau. J. Mt. Sci. 5(4), 340–349 (2008)
E. Perfect, B.D. Kay, Applications of fractals in soil and tillage research: a review. Soil Tillage Res. 36(1–2), 1–20 (1995)
R.A. Pulley, M. Min, J. Chaplin, Developing a texture-based soil hydrologic characteristics model and extending this model to predict soil strength characteristics. Trans. ASABE 51(2), 485–498 (2008)
G. Rangarajan, D.A. Sant, Fractal dimensional analysis of Indian climatic dynamics. Chaos Soliton Fractals 19(2), 285–293 (2004)
M. Resta, Hurst Exponent and its application in Time series Analysis. Recent Patents Comput Sci. 5, 211–219 (2012)
S. Sunil Kumar, B. Shivam, A. Majed, B. Rashmi, Nonlinear time series analysis of pathogenesis of COVID-19 epidemiology spread in Saudi Arabia. Comput. Mater. Continua 66(1), 805–825 (2021)
R. Soud, S. Talukdar, Contemporary crisis of rhinoceros in Assam: a critical review. Asian J. Conserv. Biol. 2(1), 82–83 (2013)
Z.Z. Su, R. Liu, A.M. Liang et al., Study on soil mechanical composition and organic matter of desertification land in Northwest of Shanxi province. Res. Soil Water Conserv. 25, 61–67 (2018)
Z. Sun, J. Han, Effect of soft rock amendment on soil hydraulic parameters and crop performance in Mu Us sandy land, China. Field Crops Res. 222, 85–93 (2018)
G.L. Wang, S.L. Zhou, Q.G. Zhao, Volume fractal dimension of soil particles and its applications to land use. Acta Pedologica Sinica 42, 545–550 (2005)
H. Wang, J. Han, W. Tong, J. Cheng, H. Zhang, Analysis of water and nitrogen use efficiency for maize (Zea mays L.) grown on soft rock and sand compound soil. J. Sci. Food Agric. 97(8), 2553–2560 (2017)
N. Wang, J. Xie, J. Han, L.T. Luo, A comprehensive framework on land-water resources development in Mu Us sandy land. Land Use Policy 40, 69–73 (2014)
X. Wang, H.J. Zhang, J.H. Cheng et al., Fractal characteristics and related affecting factors of particle size distribution of different forest soil in Simian Mountains, Chongqing. J. Soil Water Conserv. 25, 154–159 (2011)
Y. Wang, X. Su, X. Zhan, Fractal analysis of agricultural products price time series. Int. J. u- & e-Serv. Sci. Technol. 8(10), 395–404 (2015)
G. Xu, Z. Li, P. Li, Fractal features of soil particle-size distribution and total soil nitrogen distribution in a typical watershed in the source area of the middle Dan River, China. Catena 101, 17–23 (2013)
P.L.Y. Yang, P. Luo, Y.C. Shi, Fractal characteristics of soil characterized by particle size distribution. Chin. Sci. Bull. 38, 1896–1899 (1993)
L. Zhang, J.C. Han, Z.H. Ma, L.T. Luo, H.Y. Wang, J. Li, Texture character study of feldspathic sandstone and sand compound “soil’’. Acta Agriculturae Boreali-Occidentalis Sinica 23, 166–172 (2014)
Q. Zhen, J. Zheng, H. He, F. Han, X. Zhang, Effects of Pisha sandstone content on solute transport in a sandy soil. Chemosphere 144, 2214–2220 (2016)
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Bhardwaj, R., Bhardwaj, S. & Sajid, M. Fractal analysis and machine-learned decision system for precision and smart farming. Eur. Phys. J. Spec. Top. 230, 3955–3969 (2021). https://doi.org/10.1140/epjs/s11734-021-00333-4
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DOI: https://doi.org/10.1140/epjs/s11734-021-00333-4