Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Saravanan, V.
- Mapping and Classification of Soil Properties from Text Dataset using Recurrent Convolutional Neural Network
Abstract Views :182 |
PDF Views:1
Authors
S. Selvi
1,
V. Saravanan
2
Affiliations
1 Department of Computer Science and Engineering, Government College of Engineering, Bargur, IN
2 Department of Computer Science, College of Engineering and Technology, Dambi Dollo University, ET
1 Department of Computer Science and Engineering, Government College of Engineering, Bargur, IN
2 Department of Computer Science, College of Engineering and Technology, Dambi Dollo University, ET
Source
ICTACT Journal on Soft Computing, Vol 11, No 4 (2021), Pagination: 2438-2443Abstract
Accurate and effective mapping of soil properties is regarded as a critical task in environmental and agricultural management. The evaluation of properties of soil is a daunting task while monitoring and sensing the environment. Existing sampling methods is a time-consuming and laborious job and they are limited based on the regions. However, the need of soil analysis and its properties is essential at landscape level. In this paper, we use Recurrent Convolution Neural Network (RCNN) to assess the soil properties via its classification task. The model in turn is compared with conventional geostatistical spatial interpolation methods. The utilization of Recurrent Neural Network (RNN) aims at studying the spatial and temporal variability of the properties of soil that adopts Kriging interpolation technique. The simulation is conducted to study the efficacy of the model under different soil conditions and the efficacy of RCNN is reported. The results of simulation shows that the proposed method achieves higher rate of classification accuracy than other models.Keywords
Regional Convolutional Neural Network, Deep Learning, Soil Properties, Prediction.
References
- V. Chang, B. Gobinathan, A. Pinagapani, S. Kannan and G. Dhiman, “Automatic Detection of Cyberbullying using Multi-Feature based Artificial Intelligence with Deep Decision Tree Classification”, Computers and Electrical Engineering, Vol. 92, pp. 107186-107198, 2021.
- S.B.V. Sara, M. Anand and S.S. Priscila, “Design of Autonomous Production using Deep Neural Network for Complex Job”, Materials Today: Proceedings, Vol. 58, No. 3, pp. 1-12, 2021.
- A. Shukla, G. Kalnoor and A. Kumar, “Improved Recognition Rate of Different Material Category using Convolutional Neural Networks”, Materials Today: Proceedings, Vol. 56, No. 2, pp. 1-12, 2021.
- N.V. Kousik, M. Sivaram and R. Mahaveerakannan, “Improved Density-Based Learning to Cluster for User Web Log in Data Mining”, Proceedings of International Conference on Inventive Computation and Information Technologies, pp. 813-830, 2021.
- H. Azath, M. Mohanapriya and S. Rajalakshmi, “Software Effort Estimation using Modified Fuzzy C Means Clustering and Hybrid ABC-MCS Optimization in Neural Network”, Journal of Intelligent Systems, Vol. 29, No. 1, pp. 251-263, 2018.
- S. Khaki and L. Wang, “Crop Yield Prediction using Deep Neural Networks”, Frontiers in Plant Science, Vol. 10, pp. 621-632, 2019.
- H. Azath and R.S.D. Wahidabanu, “Function Point: A Quality Loom for the Effort Assessment of Software Systems”, International Journal of Computer Science and Network Security, Vol. 8, No. 12, pp. 321-328, 2008.
- J.H. Jeong, J.P. Resop and N.D. Mueller, “Random Forests for Global and Regional Crop Yield Predictions”, PLoS One, Vol. 11, No. 6, pp. 1-18, 2016.
- S. Fukuda, W. Spreer, E. Yasunaga and K. Yuge, “Random Forests Modelling for The Estimation of Mango Fruit Yields under Different Irrigation Regimes”, Agricultural Water Management, Vol. 116, pp. 142-150, 2013.
- J. Liu and C.E. Goering, “A Neural Network for Setting Target Corn Yields”, Transactions of the ASAE, Vol. 44, No. 3, pp. 705-713, 2001.
- C.J. Ransom, N.R. Kitchen and J.J. Camberato, “Statistical and Machine Learning Methods Evaluated for Incorporating Soil and Weather into Corn Nitrogen Recommendations”, Computers and Electronics in Agriculture, Vol. 164, pp 1-17, 2019.
- S.T. Drummond, K.A. Sudduth, A. Joshi and S.J. Birrell, “Statistical and Neural Methods for Site-Specific Yield Prediction”, Transactions of the ASAE, Vol. 46, No. 1, pp. 1-5, 2003.
- M. Shahhosseini, R.A. Martinez-Feria and G. Hu, “Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms”, Environmental Research Letters, Vol. 14, No. 12, pp. 1-13, 2019.
- M.M. Awad, “Toward Precision in Crop Yield Estimation using Remote Sensing and Optimization Techniques”, Agriculture, Vol. 9, No. 3, pp. 54-65, 2019.
- D. Jiang, X. Yang and N. Wang, “An Artificial Neural Network Model for Estimating Crop Yields using Remotely Sensed Information”, International Journal of Remote Sensing, Vol. 25, No. 9, pp. 1723-1732, 2004.
- A.K. Prasad, L. Chai and M. Kafatos, “Crop Yield Estimation Model for Iowa using Remote Sensing and Surface Parameters”, International Journal of Applied Earth Observation and Geoinformation, Vol. 8, No. 1, pp. 26-33, 2006.
- J.R. Romero and P.F. Roncallo, “Using Classification Algorithms for Predicting Durum Wheat Yield in the Province of Buenos Aires”, Computers and Electronics in Agriculture, Vol. 96, pp. 173-179, 2013.
- S. Khaki, Z. Khalilzadeh and L. Wang, “Classification of Crop Tolerance to Heat and drought-A Deep Convolutional Neural Networks Approach”, Agronomy, Vol. 9, No. 12, pp. 833-845, 2019.
- J. You, X. Li, M. Low and D. Lobell, “Deep Gaussian Process for Crop Yield Prediction based on Remote Sensing Data”, Proceedings of International Conference on Artificial Intelligence, pp. 1-6, 2017.
- N. Kim, K. Ha, K. J., Park and J. Cho, “A Comparison between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States”, International Journal of Geo-Information, Vol. 8, No. 5, pp. 240-254, 2019.
- A.X. Wang, C. Tran and N. Desai, “Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data”, Proceedings of International Conference on Computing and Sustainable Societies, pp. 1-5, 2018.
- Q. Yang, L. Shi and J. Han, “Deep Convolutional Neural Networks for Rice Grain Yield Estimation at the Ripening Stage using UAV-based Remotely Sensed Images”, Field Crops Research, Vol. 235, pp. 142-153, 2019.
- R. Tibshirani, “Regression Shrinkage and Selection via the Lasso”, Journal of the Royal Statistical Society: Series B (Methodological), Vol. 58, No. 1, pp. 267-288, 1996.
- I. Goodfellow, Y. Bengio and A. Courville, “Machine Learning Basics”, Deep Learning, Vol. 1, No. 7, pp. 98-164, 2016.
- N.V. Kousik, “Privacy Preservation between Privacy and Utility using ECC-based PSO Algorithm”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 567-573, 2021.
- S. Karthick and P.A. Rajakumari, “Ensemble Similarity Clustering Frame work for Categorical Dataset Clustering Using Swarm Intelligence”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 549-557, 2021.
- G. Kiruthiga, G.U. Devi and N.V. Kousik, “Analysis of Hybrid Deep Neural Networks with Mobile Agents for Traffic Management in Vehicular Adhoc Networks”, Proceedings of International Conference on Distributed Artificial Intelligence, pp. 277-290, 2020.
- K.M. Baalamurugan and S.V. Bhanu, “An Efficient Clustering Scheme for Cloud Computing Problems using Metaheuristic Algorithms”, Cluster Computing, Vol. 22, No. 5, pp. 12917-12927, 2019.
- K.M. Baalamurugan and S.V. Bhanu, “Analysis of Cloud Storage Issues in Distributed Cloud Data Centres by Parameter Improved Particle Swarm Optimization (PIPSO) Algorithm”, International Journal on Future Revolution in Computer Science and Communication Engineering, Vol. 4, pp. 303-307, 2018.