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


  • 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


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


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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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



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