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An Approach to Analyse the Agriculture Acreage and Estimate Production

Affiliations

  • School of Computer Science and Engineering, Lovely Professional University, Jalandhar-Delhi G.T. Road, National Highway 1, Phagwara – 144411, Punjab, India

Abstract


Background/Objective: Crop acreage and production estimate is of great challenge and requires strategic decisions to be taken by government agencies regarding controlling import and export duties and also to control inflation as a whole. Methods/Statistical Analysis: A lot of techniques exist to measures the yield and production for a given area and that too crop wise. We estimate the crop acreage with the help of remote sensing data and geographic information system techniques. In remote sensing, we acquire information related to the earth surface and to determine the crop acreage with the help of remote sensing images. We extract the useful information related to the yield estimation. Findings: In image processing, it is used to analyze or manipulate the features of an image. We used K-mean clustering and Self Organizing Map algorithms here for the crop Acreage and production estimation as a comparative approach to measure out acreage for a specific area image. By applying different image processing techniques and using the remote sensing techniques we estimated the production estimates for crops and compared the results with actual data obtained from statistical sources. Geographic Information System is widely used in crop acreage and production estimation to find out the locations of a particular image and helps in improving the accuracy of estimates. The challenge still remained regarding the crop segregation which requires lot of ground truth validation and hence makes the approach less viable as it reduces accuracy. Self organizing maps were found to be giving better results than other approaches. Applications/Improvement: It can be used by government agencies for taking decisions regarding import/export duty control and by agencies interested in procurement of crops and can be used by RBI to tackle Inflation as well.

Keywords

Remote Sensing, Geographic Information System, Un Supervised Classification, K Means, SOM Algorithm, Supervised Classification.

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References


  • Prasad AK, Chai L, Singh RP, Kafatos M. Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geo information. 2006 Jan 31; 8(1):26-33.
  • Maurya AK. Estimation of acreage & crop production through remote sensing & GIS technique. Hyderabad, India: Proceedings of Geospatial World Forum. 2011; p.1-14.
  • Vibhute A, Bodhe SK. Applications of image processing in agriculture: a survey. International Journal of Computer Applications. 2012 Jan 1; 52(2):34-40.
  • Pratiwi D. The use of self organizing map method and feature selection in image database classification system. 2012 Jun 1; p.1-5.
  • Xin D, Bingfang W, Jihua M, Qiangzi L, FeiFei Z. A Method to Assess Land Productivity in Huang-Huai-Hai Region Using Remote Sensing. IEEE, 2010 International Conference on Multimedia Technology (ICMT). 2010 Oct 29; p. 1-5.
  • Ballabio D, Vasighi M. A MATLAB toolbox for Self Organizing Maps and supervised neural network learning strategies. Chemometrics and Intelligent Laboratory Systems. 2012 Aug 15; 118:24-32.
  • Goswami SB, Saxena A, Bairagi GD. Remote Sensing and GIS based wheat crop acreage estimation of Indore district, MP. International Journal of Emerging Technology and Advanced Engineering. 2012; 2(3):200-3.
  • Defourny P, Blaes X, Bogaert P. Respective contribution of yield and area estimates to the error in crop production forecasting. ISPRS Archives XXXVI-8/W48 Workshop proceedings: Remote sensing support to crop yield forecast and area estimates 2007.
  • Chopin P, Blazy JM, Dore T. A new method to assess farming system evolution at the landscape scale. Agronomy for Sustainable Development. 2015 Jan 1; 35(1):325-37.
  • Grunwald S, McSweeney K, Rooney DJ, Lowery B. Soil layer models created with profile cone penetrometer data. Geoderma. 2001 Sep 30; 103(1):181-201.
  • Baskar SS, Arockiam L, Jeyasimman L. SOM-ANN A novel approach to analyzing the Brown Plant Hopper. International Journal of Computer Science and Applications (TIJCSA). 2013 Jun; 2(04):107-16.
  • Bingfang W, Qiangzi L. Crop area estimation using remote sensing on two-stage stratified sampling. International Society for Photogrametry and Remote Sensing (ISPRS). 2004 Jul; 20:12-23.
  • Xiao X, Boles S, Frolking S, Li C, Babu JY, Salas W, Moore B. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sensing of Environment. 2006 Jan 15; 100(1):95-113.
  • Cao X, Li Q, Du X, Zhang M, Zheng X. Exploring effect of segmentation scale on orient-based crop identification using HJ CCD data in Northeast China. IOP Publishing: IOP Conference Series: Earth and Environmental Science. 2014; 17(1):012047.
  • Hu X, Weng Q. Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment. 2009 Oct 31; 113(10):2089-102.
  • Shao Y, Fan X, Liu H, Xiao J, Ross S, Brisco B, Brown R, Staples G. Rice monitoring and production estimation using multitemporal RADARSAT. Remote sensing of Environment. 2001 Jun 30; 76(3):310-25.
  • Suma VR, Renjith S, Ashok S, Judy MV. Analytical Study of Selected Classification Algorithms for Clinical Dataset. Indian Journal of Science and Technology. 2016 Mar 22; 9(11):1-9.
  • Sudarshan MR, Jayapradha A, Amarnath DJ. Evaluation of Groundwater Quality at Oragadam -A GIS Approach. Indian Journal of Science and Technology. 2016 Apr 27; 9(14):1-8.

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