Abstract
Agriculture forms a major occupation in countries like India. More than 75% people rely on farming for their daily wages. Hence, achieving good yield in the crops grown by farmers is the major concern. Various environmental factors have a significant impact on the crop yield. One such component that contributes majorly to the crop yield is soil. Due to urbanization and enhanced industrialization, the agricultural soil is getting contaminated, losing fertility, and hindering the crop yield. Machine Learning (ML) is employed for agricultural data analysis. The proposed ML based model aims at classifying the given soil sample datasets into four different classes, namely very high fertile, high fertile, moderately fertile, and low fertile soil utilizing support vector machine (SVM) technique. It also predicts the suitable crops that can be grown based on the class which the soil sample belongs to and suggests the fertilizers that can be used to further enhance the fertility of soil. Using proposed model, farmers can make decisions on which crop to grow based on the soil classification and decide upon the nitrogen–phosphorous–potassium (NPK) fertilizers ratio that can be used. Comparison of the SVM algorithm with k-nearest neighbor (k-NN), and decision tree (DT) has shown that SVM performed with a higher accuracy.
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References
K.A. Shastry, H.A. Sanjay, H. Kavya, A novel data mining approach for soil Classification, in 9th International Conference on Computer Science & Education, Vancouver, BC, pp. 93–98 (2014)
K. Aditya Shastry, H.A. Sanjay, G. Deexith, Quadratic-radial-basis-function-kernel for classifying multi-class agricultural datasets with continuous attributes. Appl. Soft Comp. 58, 65–74 (2017)
S. Panchamurthi, Soil analysis and prediction of suitable crop for agriculture using Machine Learning. Int. J. Res. Appl. Sci. Eng. Technol. 7(3), 2328–2335 (2019)
S. Al Zaminur Rahman, K. Chandra Mitra, S.M. Mohidul Islam, Soil classification using ML methods and crop suggestion based on soil series, in 21st International Conference of Computer and Information Technology (ICCIT), pp. 1–4. IEEE (2018)
A. Rao, A. Gowda, R. Beham, Machine Learning in soil Classification and crop detection. Int. J. Sci. Res. & Dev. 4(1), 792–794 (2016)
J. Gholap, A. lngole, J. Gohil, S. Gargade, V. Attar, Soil data analysis using classification techniques and soil attribute prediction. Int. J. Comput. Sci. Issues 9(3), 1–4 (2012)
R. Vamanan, K. Ramar, Classification of agricultural land soils: A data mining approach. Agricultural J. 6(3), 379–384 (2011). https://doi.org/10.3923/aj.2011.82.86
L. Vibha, G.M. HarshaVardhan, S.J. Prasanth, P. Deepa Shenoy, K.R.L. VenuGopal, M. Patnaik, A hybrid clustering and classification technique for soil data mining, in Proc. ICTES. IEEE, pp. 1090–1095 (2007)
S. Mutalib, S-N Fadhlun Jamian, S. Abdul-Rahman, A. Mohamed, Soil classification: an application of self organizing map and k-means, in: Proc 10th IEEE-ISDA. IEEE, pp 439–444 (2010)
G. Yi-Yang, Ren, Nan-ping, Data mining and analysis of our agriculture based on the DT, in Proc. IEEE-ISECS, pp 134–138 (2009). 10.1109 ICCCM. 5267962
N. Jain, A. Kumar, S. Garud, V. Pradhan, P. Kulkarni, Crop selection method based on various environmental factors using ML, February 2017.
K. Srunitha, S. Padmavathi, Performance of SVM classifier for image-based soil classification, in 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), pp. 411–415. IEEE (2016)
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Pruthviraj, Akshatha, G.C., Shastry, K.A., Nagaraj, Nikhil (2022). Crop and Fertilizer Recommendation System Based on Soil Classification. In: Shetty D., P., Shetty, S. (eds) Recent Advances in Artificial Intelligence and Data Engineering. Advances in Intelligent Systems and Computing, vol 1386. Springer, Singapore. https://doi.org/10.1007/978-981-16-3342-3_3
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