Genetic-Based Neural Network for Enhanced Soil Texture Analysis: Integrating Soil Sensor Data for Optimized Agricultural Management
DOI:
https://doi.org/10.22399/ijcesen.572Keywords:
Genetic Algorithm, Soil Texture, Neural Networks, Composition, IoT SensorsAbstract
Soil texture analysis is vital in agricultural management due to its influence on crop growth and yield. Defined by the proportions of clay, sand, and silt particles, soil texture affects properties like aeration, water-holding capacity, and nutrient retention, all crucial for plant development. The OBJECTIVES: This study aims to design a Genetic-Based Neural Network (GBNN) for accurate soil texture analysis, particularly for soils with similar structures but different compositions. It also aims to collect environmental impact data through soil sensors to enhance the understanding of soil texture.METHODS: The methodology involves developing a GBNN, leveraging genetic algorithms to group homogeneous particles, thus improving texture classification. This approach addresses the shortcomings of previous deep learning models. Additionally, soil sensor data will be collected to study environmental factors affecting soil texture.RESULTS: The GBNN showed improved accuracy in texture classification compared to previous models. Genetic algorithms effectively grouped similar particles, and soil sensor data provided insights into environmental impacts on soil texture.CONCLUSION: The GBNN for soil texture analysis overcame previous models' challenges, improving classification accuracy. The integration of soil sensor data provided valuable environmental insights, aiding farmers in optimizing crop selection, fertilizer application, and soil management for better yields and sustainability.
References
Zhao, Z., Feng, W., Xiao, J., Liu, X., Pan, S., & Liang, Z. (2022). Rapid and Accurate Prediction of Soil Texture Using an Image-Based Deep Learning Autoencoder Convolutional Neural Network Random Forest (DLAC-CNN-RF) Algorithm. Agronomy, 12(12). https://doi.org/10.3390/agronomy12123063
Srivastava, P., Shukla, A., & Bansal, A. (n.d.). Transfer Learning Analysis For Predicting Soil Texture Classes From Soil Images. https://doi.org/
Sofou, A., Evangelopoulos, G., & Maragos, P.(2005). Soil image segmentation and texture analysis: A computer vision approach. IEEE Geoscience and Remote Sensing Letters, 2(4);394–398. https://doi.org/10.1109/LGRS.2005.851752
Uddin, M., & Hassan, M. R. (2022). A novel feature based algorithm for soil type classification. Complex and Intelligent Systems, 8(4);3377–3393. https://doi.org/10.1007/s40747-022-00682-0
Anandan, K., Shankar, R., & Duraisamy, S. (2021). Convolutional Neural Network approach for the prediction of Soil texture properties. Indian Journal of Science and Technology, 14(3);190–196. https://doi.org/10.17485/IJST/v14i3.2047
Zhang, H., Dong, Q., & Liu, S. (2020). Soil texture classification using convolutional neural networks. Applied Sciences, 10(5);1-15. https://doi.org/10.3390/app10051838
Zou, L., Shafiee, M. J., & Wang, H. (2021). Soil texture classification using multiscale and multiresolution image features. Remote Sensing, 13(7);1-18. https://doi.org/10.3390/rs13071265
Li, Y., Chen, L., & Li, J. (2020). Deep learning based soil texture analysis for digital soil mapping. Journal of Applied Remote Sensing, 14(1);1-15. https://doi.org/10.1117/1.JRS.14.016507
Yar, S. A., Farooq, M. A., & Iqbal, M. Z. (2021). Soil texture classification using histogram of oriented gradients and support vector machines. Remote Sensing Applications: Society and Environment, 23;1-10. https://doi.org/10.1016/j.rsase.2021.100541
Lu, J., Zhang, W., & Zhang, J. (2020). Soil texture classification using deep convolutional neural networks and feature visualization. International Journal of Applied Earth Observation and Geoinformation, 91;1-12. https://doi.org/10.1016/j.jag.2020.102156
Sri Silpa Padmanabhuni and Pradeepini Gera, (2022). Synthetic Data Augmentation of Tomato Plant Leaf using Meta Intelligent Generative Adversarial Network: Milgan International Journal of Advanced Computer Science and Applications(IJACSA), 13(6). http://dx.doi.org/10.14569/IJACSA.2022.0130628
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