Crop Rotation based Crop Recommendation System with Soil Deficiency Analysis through Extreme Learning Machine
Crop Rotation based Crop Recommendation System with Soil Deficiency Analysis through Extreme Learning Machine
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : G. Murugesan, B. Radha
|DOI : 10.14445/22315381/IJETT-V70I4P210|
How to Cite?
G. Murugesan, B. Radha, "Crop Rotation based Crop Recommendation System with Soil Deficiency Analysis through Extreme Learning Machine," International Journal of Engineering Trends and Technology, vol. 70, no. 4, pp. 122-134, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P210
Agriculture is the pillar of the country`s economy. Climatic change, soil fertility level, temperature and moisture level, pH value and the crop predecessors often impact crop yield in agriculture. The prediction of the right crop at the right place at the right time will be extremely helpful in increasing the crop yield, which also results in economic proliferation. Machine learning is an emerging technique in the field of agriculture in various ways, including soil classification, soil nutrient analysis, crop prediction or suggestion. This paper presents the crop recommendation system by considering various significant factors, including soil fertility and condition, season and crops predecessor, to recommend appropriate crops for improvising the cultivation based on precision agriculture. Based on the given input, the model applies an extreme learning machine, a fast learning classifier algorithm, for suggesting the appropriate crop to its users. The model also includes the deficiency analysis to identify the deficiency of nutrients in the soil with current crop requirements. The experimental analysis shows that the proposed model provides better accuracy of about 96.5% with a minimum false rate of 3.5% in predicting suitable crops and detecting the deficiency in the soil.
Crop predecessor, Crop recommendation, Crop rotation, Extreme learning machine, Soil deficiency, Soil fertility.
 A. J. Bennett, G. D. Bending, D. Chandler, S. Hilton, and P. Mills, Meeting the Demand for Crop Production: the Challenge of yield Decline in Crops Grown in Short Rotations, Biological reviews. 87(1) (2012) 52-71.
 W. Haider, A. Rehman, M. N. Durrani, and S. Rehman, Knowledge-Based Soil Classification towards Relevant Crop Production, International Journal of Advanced Computer Science and Applications. 10(12) 2019 488-501.
 V. Bhatnagar, and R. Chandra, IoT-based Soil health Monitoring and Recommendation System. In the Internet of Things and Analytics for Agriculture, Springer, Singapore. 2 (2020) 1-21.
 M. A. Al Maruf, and S. Shatabda, iRSpot-SF: Prediction of Recombination Hotspots by Incorporating Sequence-Based Features into Chou`s Pseudo Components, Genomics. 111(4) (2019) 966-972.
 A. Suruliandi, G. Mariammal, and S. P. Raja, Crop prediction Based on Soil and Environmental Characteristics using Feature Selection Techniques, Mathematical and Computer Modelling of Dynamical Systems. 27(1) (2021) 117-140.
 M. A. Hossen. Mechanization in Bangladesh: Way of Modernization in Agriculture International Journal of Engineering Trends and Technology, 67(9) (2019) 69-77.
 S. Sakthicvel, and G. Thailambal, Ada Naïve Bayesian Algorithm for Predicting the Intensity of Rain to Improve the Accuracy. International, Journal of Engineering Trends and Technology, 70(2) (2022) 24-31.
 J. Jagannathan, and C. Divya, Time Series Analyzation and Prediction of Climate using Enhanced Multivariate Prophet, International Journal of Engineering Trends and Technology, 69(10) (2021) 89-96.
 M. Thanjaivadivel, and R. Suguna, Leaf Disease Prediction Using Fast Enhanced Learning Method, International Journal of Engineering Trends and Technology, 69(9) (2021) 34-44.
 T. Jayaraj, and J. Abdul Samath, Disease Forecasting and Severity Prediction Model for COVID-19 using Correlated Feature Extraction and Feed-Forward Artificial Neural Networks, International Journal of Engineering Trends and Technology, 69(8) (2021)126-137.
 B. Vinoth, and N. M. Elango, Data Mining of Paddy Cultivation Patterns And Water Resource Management In Late Samba Season of Tamilnadu, International Journal of Engineering Trends and Technology, 69(1) (2021) 152-165.
 M. Anita, and S. Shakila, Predictive Analytics in Soil for Agriculture Using Kendall Normalized Feature Selection Based Jaccarized Rocchio Boyer-Moore Bootstrap Aggregative Mapreduce Classifier for Predictive Analytics with Big data, International Journal of Engineering Trends and Technology, 69(9) (2021) 80-91.
 S. J. Reshma, and A. S. Pillai, Edaphic Factors and Crop growth using Machine Learning A Review, in International Conference on Intelligent Sustainable Systems (ICISS), IEEE. (2017) 270-274.
 H. Teng, R. A. Viscarra, Z. Shi, and T. Behrens, Updating a National Soil Classification with Spectroscopic Predictions and Digital soil Mapping, Catena. 164 (2018) 125–134.
 J. Lacasta, F. J. Lopez-Pellicer, B. Espejo-García, J. Nogueras-Iso, and F. J. Zarazaga-Soria, Agricultural Recommendation System for Crop Protection, Computers and Electronics in Agriculture. 152 (2018) 82-89.
 M. K. S. Preetha, P. K. Priya, K. DivyaPrabha, and S. Dharanipriya, Crop Rotation and Yield Analysis using Naive Ratio Classification, International Journal of Scientific & Engineering Research. 8(5) 2017 29-34.
 M. Kalimuthu, P. Vaishnavi, and M. Kishore, Crop Prediction using Machine Learning, In Third International Conference on Smart Systems and Inventive Technology (ICSSIT, IEEE. (2020) 926-932.
 M. S. Suchithra, and M. L. Pai, Improving the Performance of Sigmoid Kernels in Multiclass SVM using Optimization Techniques for an Agricultural Fertilizer Recommendation System, In International Conference on Soft Computing Systems, Springer, Singapore. (2018) 857-868.
 A. Chougule, V. K. Jha, and D. Mukhopadhyay, Crop Suitability and Fertilizers Recommendation using data Mining Techniques, in Progress in Advanced Computing and Intelligent Engineering, Springer, Singapore (2019) 205-213.
 M. S. Suchithra, and M. L. Pai, Improving the Prediction Accuracy of Soil Nutrient Classification by Optimizing Extreme Learning Machine parameters, Information processing in Agriculture, 7(1) (2020) 72-82.
 M. S. Sirsat, E. Cernadas, M. Fernández-Delgado, and S. Barro, Automatic Prediction of Village-wise soil Fertility for Several Nutrients in India using a wide range of Regression Methods, Computers and Electronics in Agriculture, 154 (2018) 120-133.
 M. Rekha Sundari, G. Siva Rama Krishna, V. Sai Naveen, and G. Bharathi, Crop Recommendation system using k-nearest Neighbors Algorithm, in Proc. International Conference on Recent Trends in Computing, Springer, Singapore. (2021) 581-589.
 S. Gupta, G. Garg, P. Mishra, and R. C. Joshi, CDMD: An Efficient crop Disease Detection and Pesticide Recommendation System using Mobile Vision and Deep Learning, in Proc. International Conference on Big Data, Machine Learning and their Applications, Springer, Singapore. (2021) 295-305.
 R. Guan, H. Pan, W. He, M. Sun, H. Wang, X. Cui, Y. Lou, Y. Zhuge, Fertilizer Recommendation for Foxtail Millet Based on Yield Response and Nutrient Accumulation, Journal of Plant Nutrition. 45(3) (2022) 332-345.
 J. Rurinda, S. Zingore, J. M. Jibrin, T. Balemi, K. Masuki, J. A. Andersson, M. F. Pampolino, I. Mohammed, J. Mutegi, A. Y. Kamara, and B. Vanlauwe, Science-based Decision Support for Formulating crop Fertilizer Recommendations in Sub-Saharan Africa, Agricultural Systems. 180 (2020) 102790.
 X. E. Pantazi, D. Moshou, T. Alexandridis, R. L. Whetton, and A. M. Mouazen, Wheat yield Prediction using Machine Learning and Advanced Sensing Techniques, Computers and Electronics in Agriculture. 121 (2016) 57-65.
 A. K. Mariappan, C. Madhumitha, P. Nishitha, and S. Nivedhitha, Crop Recommendation System Through Soil Analysis using Classification in Machine Learning, International Journal of Advanced Science and Technology. 29(3) (2020) 12738 – 12747.
 Y. J. N. Kumar, V. Spandana, V. S. Vaishnavi, K. Neha, and V. G. R. R. Devi, Supervised Machine Learning Approach for Crop yield Prediction in the Agriculture sector, In International Conference on Communication and Electronics Systems (ICCES), IEEE. (2020) 736-741.
 A. Anitha, and D. P. Acharya, Crop Suitability Prediction in Vellore District using Rough set on fuzzy Approximation Space and Neural network, Neural Computing and Applications, 30(12) (2018) 3633-3650.
 P. K. Priya, and N. Yuvaraj, An IoT Based Gradient Descent Approach for Precision Crop Suggestion using MLP, In Journal of Physics: Conference Series, IOP Publishing. 1362(1) (2019) 012038.
 M. Garanayak, G. Sahu, S. N. Mohanty, and A. K. Jagadev, Agricultural Recommendation System for Crops using Different Machine Learning Regression Methods, International Journal of Agricultural and Environmental Information Systems (IJAEIS), 12(1) (2021) 1-20.
 T. Ashok, and P. Suresh Varma, Crop Prediction Based on Environmental Factors using Machine Learning Ensemble Algorithms. In Intelligent Computing and Innovation on Data Science, Springer, Singapore. (2020) 581-594.
 G. Murugesan, and B. Radha, Soil Data classification using Attribute group Rank with Filter-based Instance Selection model, International Journal Of Scientific & Technology Research. 9(6) (2020) 202-208.
 P. S. Ramila Rajaleximi, M. S. Irfan Ahmed, Ahmed Alenezi, Classification of Imbalanced Class Distribution using Random Forest with Multiple Weight-based Majority Voting for Credit Scoring, International Journal of Recent Technology and Engineering. 7(6S5) (2019) 517-526.
 S. Sathya Bama, and A. Saravanan, Efficient Classification using Average Weighted Pattern Score with Attribute Rank Based Feature Selection, International Journal of Intelligent Systems and Applications, 11(7) (2019) 29.
 E. Winarno, W. Hadikurniawati, and R. N. Rosso, Location-Based Service for Presence System using Haversine Method, in International Conference on Innovative and Creative Information Technology (ICITech), IEEE. (2017) 1-4.
 P. Royston, Multiple Imputations of Missing Values, The Stata Journal. 4(3) (2004) 227-241.
 G. Chhabra, V. Vashisht, and J. Ranjan, A Comparison of Multiple Imputation Methods for data with Missing Values, Indian Journal of Science and Technology. 10(19) (2017) 1-7.
 D. R. Sumner, Crop Rotation and Plant Productivity, In CRC Handbook of Agricultural Productivity, CRC Press. (2018) 273-314.
 P. Benincasa, G. Tosti, M. Guiducci, M. Farneselli, and F. Tei, Crop Rotation as a System Approach for soil Fertility Management in Vegetables, Advances in Research on Fertilization Management of Vegetable Crops. (2017) 115-148.
 D. B. Lobell, W. Schlenker, and J. Costa-Roberts, Climate Trends and Global Crop Production since 1980, Science. 333(6042) (2011) 616-620.
 D. Devi, S. K. Biswas, and B. Purkayastha, Early Detection of Parkinson`s disease: an Intelligent Diagnostic Approach, in Research Anthology on Diagnosing and Treating Neurocognitive Disorders, IGI Global. (2021) 295-328.
 Soil Health Management, Soil Health Card Portal, Department of Agriculture, Cooperation and Farmers Welfare under Ministry of Agriculture and Farmers Welfare, Government of India. https://soilhealth.dac.gov.in/
 P. L. Patil, B. I. Bidari, M. Hebbara, J. Katti, S. Dilvaranaik, S. Vishwanatha, H. M. Geetanjali, and G. S. Dasog, Identification of Soil Fertility Constraints by GIS in Bedwatti sub-watershed Under the Northern dry zone of Karnataka for Site-Specific Recommendations, Journal of Farm Sciences, 30(2) (2017) 206-211.
 P. R. Chaudhari, N. H. Desai, P. P. Chaudhari, and K. V. Rabari, Status of Chemical Properties and Available Major Nutrients in Soils of Patan District of Gujarat, India, Crop Research, 53(3/4) (2018) 147-153.
 D. E. Ratnawati, W. Marjono, and S. Anam, Comparison of Activation Function on Extreme Learning Machine (ELM) Performance for Classifying the Active Compound, in AIP Conference Proceedings, AIP Publishing LLC. 2264(1) (2020) p. 140001.
 R. Batuwita, and V. Palade, Adjusted Geometric-mean: a Novel Performance Measure for Imbalanced Bioinformatics Datasets is Learning. Journal of Bioinformatics and Computational Biology, 10(4) (2012) 1250003.