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Abstract
An essential component of agriculture is soil. Many types of soil exists. Different parameters can be observed in each type of soil, and various crops can be grown on different types of soils. To understand which crops develop in certain types of soil, they need to be informed on the characteristics and features of various soil types. The capability to identify the inputs needed for productive and cost-effective farming was provided by soil analysis. A proper soil test will ensure that the proper amount of fertilizers are utilized, providing the crop to meet its needs while also profiting from the nutrients already available in the soil. The phrase "soil health" refers to the biological, chemical, and physical elements of the soil that are significant for plant nutrients. Soil analysis is a collection of different chemical processes that determines the amounts of available plant nutrients in the soil. The most important factors for producing a healthy crop are micronutrient analysis. The classification of soil's micronutrients is the primary objective of this analysis. In this case, machine learning methods may be helpful. It has made significant progress in recent years. Hence in this analysis, soil health analysis and classification of Micronutrients for crop suggestion using Machine Learning is presented. The coloured images of the soil samples are obtained and processed using a number of algorithms and filters. Color, texture, and other features can be extracted using these developed algorithms. Logistic Regression algorithm is used to classify micronutrients.
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References
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 Volume 70 Issue 4, 122-134, April 2022,
doi: 10.14445/22315381/IJETT-V70I4P210
E.U. Eyo, S.J. Abbey, T.T. Lawrence, F.K. Tetteh, “Improved prediction of clay soil expansion using machine learning algorithms and meta-heuristic dichotomous ensemble classifiers”, Geoscience Frontiers 13 (2022) 101296, doi:10.1016/j.gsf.2021.101296
Lihong Wan, Shihua Li, Yao Chen, Ze He and Yanli Shi, “Application of Deep Learning in Land Use Classification for Soil Erosion Using Remote Sensing”, Frontiers in Earth Science, www.frontiersin.org 1 April 2022, Volume 10, Article 849531, doi: 10.3389/feart.2022.849531
Machbah Uddin, Md. Rakib Hassan, “A novel feature based algorithm for soil type classification”, Complex & Intelligent Systems (2022) 8:3377–3393, doi:10.1007/s40747-022-00682-0
Stanisław Gruszczy´nski, Wojciech Gruszczy´nski, “Supporting soil and land assessment with machine learning models using the Vis-NIR spectral response”, Geoderma 405 (2022) 115451, Elsevier, doi: 10.1016/j.geoderma.2021.115451
Sanjay Motia and SRN Reddy, “Exploration of machine learning methods for prediction and assessment of soil properties for agricultural soil management: a quantitative evaluation”, ICMAI 2021, Journal of Physics: Conference Series, doi:10.1088/1742-6596/1950/1/012037
Janez Trontelj ml, and Olga Chambers, “Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method”, Sensors 2021, 21, 4208, doi:10.3390/s21124208
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.
shravani v, uday kiran s, yashaswini j s, and priyanka d, “Soil classification and crop suggestion using machine Learning”, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056, Volume: 07 Issue: 06, June 2020
Vijay E V, Navya Ch, Abdul Shabana Begum, Rajaneesh D, Mahesh Babu B, “Soil Classification Using Modified Support Vector Machine”, International Journal of Research in Advent Technology, Vol.8, No.9, September 2020 E-ISSN: 2321-9637
Madhumathi R, Arumuganathan T, Sneha Iyer R, Shruthi R, Shruthhi K, “Soil Nutrient Analysis Using Machine Learning Techniques”, National E-Conference on Communication, Computation, Control and Automation ( CCCA-2020), ISSN : 2581-7175
Kamel H. Rahouma and Rabab Hamed M. Aly, “Applying Polynomial Learning for Soil Detection Based on Gabor Wavelet and Teager Kaiser Energy Operator”, Springer Nature Switzerland AG 2020,
doi:10.1007/978-3-030-14118-9_75
Barman U, Choudhury RD, “Soil texture classification using multi class support vector machine”, Inform Process Agric 7(2):318–332, 2019, Elsevier, doi: 10.1016/j.inpa.2019.08.001
Motia S, Reddy SRN, “Conceptual Framework of a Prototype Data Driven Decision Support System for Farmland Health Assessment using Wireless Sensor Network”, In 2019 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON) pp. 215-222. IEEE.
Sk Al Zaminur Rahman, Kaushik Chandra Mitra, S.M. Mohidul Islam, “Soil Classification using Machine Learning Methods and Crop Suggestion Based on Soil Series”, 2018 21st International Conference of Computer and Information Technology (ICCIT), 21-23 December, 2018, 978-1-5386-9242-4/18
Chandan, Ritula Thakur, “An Intelligent Model for Indian Soil Classification using various Machine Learning Techniques”, ISSN (e): 2250–3005, Volume, 08, Issue, 9, Sepetember 2018, International Journal of Computational Engineering Research (IJCER)
Hement Kumar Sharma, Shiv Kumar, “Soil Classification & Characterization Using Image Processing”, 2018 Second International Conference on Computing Methodologies and Communication (ICCMC)
Ashwini Rao, Janhavi U, Abhishek Gowda N S, Manjunatha, Mrs.Rafega Beham, “Machine Learning in Soil Classification and Crop Detection”, IJSRD - International Journal for Scientific Research & Development, Vol. 4, Issue 01, 2016 ,
S.Pudumalar, “Crop Recommendation System for Precision Agriculture”,2016 IEEE Eighth International Conference on Advanced Computing (ICoAC), DOI: 10.1109/ICoAC.2017.7951740
Rakesh Kumar, “Crop Selection Method to Maximize Crop Yield Rate using Machine Learning Technique” 2015
International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), 6-8 May 2015. pp.138-145.