An Smart Intelligence Performance Analysis Using ANN Classifiers For Soil Color Texture Identification

Authors

  • Deepika Ajalkar G H Raisoni College of Engineering and Management, Pune
  • Anil Kumar C. Associate Professor and HoD, Dept of ECE, R L JALAPPA INSTITUTE OF TECHNOLOGY, Doddaballapur
  • Ashish Sharma Department of Computer Engineering and Applications, GLA University, Mathura (U. P.)-281406, India
  • Deepak A. Vidhate Professor & Head, Department of Information Technology, Dr. Vithalrao Vikhe Patil College of Engineering, Ahmednagar, Maharashtra
  • A. Deepak Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu
  • T. R. Vijaya Lakshmi Associate Professor, Mahatma Gandhi institute of Technology, Gandipet, Hyderabad -75

Keywords:

Soil texture, convolutional neural network, hyperspectral data, deep learning

Abstract

The principal purpose is to growth the accuracy of soil belongings prediction the usage of hyperspectral facts. By spatial interpolation, a convolution schooling is achieved to apprehend the premise of hyperspectral records in this examine. Statistical evaluation/strategies: natural carbon steels, ionic energy, nitrogen content (N), the pH stage in water, mud particle, and sand particle are all expected the use of the counseled technique. The ratio of clay, sand, plus silt in the soil determines the soil texture, which describes the relative awareness of soil debris. Hyperspectral information in the form of several arrays are dispatched into the ANN. The foundation-suggest-rectangular mistakes at the same time as being square is used to evaluate version overall performance statistics. Findings: A deep mastering technique turned into employed in this take a look at to capture the pattern hid in the soil. Machine studying is a category of neural network that could mirror non-linearity within the scaled information from modelling complicated relationships. Identifying a soil type is the toughest challenge since it involves complicated structural homes and soil variables. Novelty/upgrades: The cautioned ANN model's automated picture getting to know the capability complements the effectiveness of soil texture prediction. The proposed method yielded an average upward push value of five.68 percent for all six soil texture parameters.

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References

V. Kumar, B.K. Vimal, R. Kumar, M. Kumar, Determination of soil pH by using digital image processing technique. J. Appl. Nat. Sci. 6(1), 14–18 (2014)

R. Sudha, S. Aarti, S. Anitha, K. Nanthini, Determination of soil Ph and nutrient using image processing. IJCTT (2017)

S. Kshirsagar, P. Lendave, A. Vibhute, Soil nutrients analysis using color image processing. IRJET 5 (2018).

J.C. Puno, E. Sybingco, E. Dadios, I. Valenzuela, J. Cuello, Determination of soil nutrients and pH level using image processing and artificial neural network. IEEE (2017)

M.S. Gurubasava, S.D. Mahantesh, Analysis of agricultural soil pH using digital image processing. Int. J. Res. Advent Technol. 6(8), 1812–1816 (2018)

M.M. Aziz, D.R. Ahmeed, B.F. Ibrahim, Determine the Ph of soil by using neural network based on soil’s colour. IJARCSSE 6 (2016)

U. Kamble, P. Shingne, R. Kankrayane, S. Somkuwar, S. Kamble, Testing of agriculture soil by digital image processing. IJSRD 5 (2017)

S.-O. Chung, K.-H. Cho, J.-W. Cho, K.-Y. Jung, Texture classification algorithm using RGB characteristics of soil images. Kyushu University Institutional Repository 57 (2012)

E. Prathibha, A. Manjunath, R. Likitha, RGB to YCbCr color conversion using VHDL approach. Int. J. Eng. Res. Dev. (2012)

H.B. Kekre, S.D. Thepade, Image blending in vista creation using Kekre’s LUV color space, in Proceedings of SPIT-IEEE Colloquium and International Conference, Mumbai,

M.R. Dharwad, T.A. Badebade, M.M. Jain, A.R. Maigur, Estimation of moisture content in soil using image processing. IJIRD 3 (2014)

S. Krishna Prasad, B. Siva Sreedharan, S. Jaishanth, Crop Monitoring And Recommendation System Using Machine Learning Techniques. Madras Institute of Technology, Chennai (2017).

Motia S, Reddy S. Ensemble classifier to support decisions on soil classification. IOP Conf. Ser.: Mater. Sci. Eng. 2021; 1022:012044.

El-Ramady HR, et al. Soil quality and plant nutrition. In Sustainable Agriculture Reviews 14, Springer. 2014;345–447.

Karlen DL, Ditzler CA, Andrews SS. Soil quality: Why and how?. Geoderma. 2003;114(3–4):145–156.

Hartemink AE. The use of soil classification in journal papers between 1975 and 2014. Geoderma Regional. 2015;5:127–139.

Brifcani A, Issa A. Intrusion detection and attack classifier based on three techniques: A comparative study. Eng. & Tech. Journal. 2011;29(2):368–412.

P Gite, A Shrivastava, KM Krishna, GH Kusumadev, Under water motion tracking and monitoring using wireless sensor network and Machine learning, Materials Today: Proceedings, Volume 80, Part 3, 2023, Pages 3511-3516

Anurag Shrivastava, Midhun Chakkaravathy, Mohd Asif Shah, A Novel Approach Using Learning Algorithm for Parkinson’s Disease Detection with Handwritten Sketches’, Cybernetics and Systems, Taylor & Francis

Mukesh Patidar, Anurag Shrivastava, Shahajan Miah, Yogendra Kumar, Arun Kumar Sivaraman, An energy efficient high-speed quantum-dot based full adder design and parity gate for nano application, Materials Today: Proceedings, Volume 62, Part 7, 2022, Pages 4880-4890

Kovačević M, Bajat B, Gajić B. Soil type classification and estimation of soil properties using support vector machines. Geoderma. 2010;154(3–4):340–347.

Anurag Shrivastava, S. J. Suji Prasad, Ajay Reddy Yeruva, P. Mani, Pooja Nagpal & Abhay Chaturvedi (2023): IoT Based RFID Attendance Monitoring System of Students using Arduino ESP8266 & Adafruit.io on Defined Area, Cybernetics and Systems, DOI: 10.1080/01969722.2023.2166243

P. William, A. Shrivastava, H. Chauhan, P. Nagpal, V. K. T. N and P. Singh, "Framework for Intelligent Smart City Deployment via Artificial Intelligence Software Networking," 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), 2022, pp. 455-460, doi: 10.1109/ICIEM54221.2022.9853119

Sorokin A, Owens P, Láng V, Jiang Z-D, Michéli E, Krasilnikov P. 'Black soils’ in the Russian soil classification system, the US soil taxonomy and the WRB: Quantitative correlation and implications for pedodiversity assessment. CATENA. 2021;196:104824.

Bouayad D, Baroth J, Dano C. Gaussian mixture model based soil classification using multiple cone penetration tests. IOP Conf. Ser.: Earth Environ. Sci. 2021;696(1):012034.

Murugesan G, Radha DB. Soil data classification using attribute group rank with filter based instance selection model. 2020;9(06):7.

Pandith V, Kour H, Singh S, Manhas J, Sharma V. Performance evaluation of machine learning techniques for mustard crop yield prediction from soil analysis. JSR. 2020;64(02):394–398.

Ahmed AZ. Application of bayesian approach to decision tree algorithm for classification of soil types. International Journal of Advanced Research in Engineering and Technology (IJARET). 2020;11(8):808-814.

Barman U, Choudhury RD. Soil texture classification using multi class support vector machine. Information Processing in Agriculture. 2020;7(2):318–332.

Jahan R. Applying naive bayes classification technique for classification of improved agricultural land soils. IJRASET. 2018;6(5):189–193.

Arooj A, Riaz M, Akram MN. Evaluation of predictive data mining algorithms in soil data classification for optimized crop recommendation. In 2018 International Conference on Advancements in Computational Sciences (ICACS), Lahore, Pakistan. 2018;1–6.

Isbell RF. The Australian Soil Classification., Australian Soil and Land Survey Handbook (CSIRO Publishing: Collingwood, Vic.). 1996;4.

Pham BT, Hoang T-A, Nguyen D-M, Bui DT. Prediction of shear strength of soft soil using machine learning methods. Catena. 2018;166:181–191.

Patnaik S, Yang X-S, Sethi IK. Eds., Advances in Machine Learning and Computational Intelligence: Proceedings of ICMLCI 2019. Singapore: Springer Singapore; 2021. 32. Anuradha C, Velmurugan T. A comparative analysis on the evaluation of classification algorithms in the prediction of students performance. Indian Journal of Science and Technology. 2015;8(15):1– 12.

Vaideghy, A. ., & Thiyagarajan, C. . (2023). An Ensemble Classification and Hybrid Feature Selection Approach for Fake News Stance Detection . International Journal on Recent and Innovation Trends in Computing and Communication, 11(4s), 28–39. https://doi.org/10.17762/ijritcc.v11i4s.6304

Muhammad Rahman, Automated Machine Learning for Model Selection and Hyperparameter Optimization , Machine Learning Applications Conference Proceedings, Vol 2 2022.

Anupong, W., Yi-Chia, L., Jagdish, M., Kumar, R., Selvam, P. D., Saravanakumar, R., & Dhabliya, D. (2022). Hybrid distributed energy sources providing climate security to the agriculture environment and enhancing the yield. Sustainable Energy Technologies and Assessments, 52 doi:10.1016/j.seta.2022.102142

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Published

03.09.2023

How to Cite

Ajalkar, D. ., Kumar C., A. ., Sharma, A. ., Vidhate, D. A. ., Deepak, A. ., & Lakshmi, T. R. V. . (2023). An Smart Intelligence Performance Analysis Using ANN Classifiers For Soil Color Texture Identification. International Journal of Intelligent Systems and Applications in Engineering, 12(1s), 18 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3391

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Research Article

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