Optimal Deep Convolutional Neural Network based Fusion Model for Soil Nutrient Analysis
Article Sidebar
Main Article Content
Abstract
The vast majority of people in India, agriculture is their main line of work, and it has a large economic impact.. Soil is important for supplying vital nutrients to crops for better yield. Determining soil nutrients is certainly essential for selecting appropriate crops and monitoring growth. Common methods used by agriculturalists are inadequate to satisfy increasing demands and have to obstruct cultivating soil. For a better crop yield, agriculturalists must have an awareness regarding the soil nutrients for a specific crop. There comes a need for using Deep learning methods in soil analysis that would help farmers in the domain. This study introduces an Optimal Deep Convolutional Neural Network Fusion Model for Soil nutrient Type Classification (ODCNNF-STC) technique. The presented ODCNNF-STC technique examines the input soil images to classify them into different nutrients present in the soil. In this approach, the noise present in the soil images are initially filtered using a bilateral filter (BF) followed by contrast enhancement. The preprocessed soil images are fed to the model formed by the fusion of DenseNet201 and InceptionResNetV2 models extracting the soil images that can successfully differentiate soil nutrients. Finally, classification of soil nutrients were performed by three classifiers namely extreme learning machine (ELM), RMSProp optimizer-based 1DCNN, and RMSProp optimizer-based Stacked Auto Encoder (SAE). The experimental validation of ODCNNF-STC method is examined on real-time dataset of soil images with a maximum accuracy of 99.39% over recent methods.
Article Details
References
Zhou, C., Hu, J., Xu, Z., Yue, J., Ye, H. and Yang, G., 2020. A novel greenhouse-based system for the detection and plumpness assessment of strawberry using an improved deep learning technique. Frontiers in plant science, 11, p.559.
Janani, M. and Jebakumar, R., 2023. Detection and classification of groundnut leaf nutrient level extraction in RGB images. Advances in Engineering Software, 175, p.103320.
Riese, F.M. and Keller, S., 2019. Soil texture classification with 1D convolutional neural networks based on hyperspectral data. arXiv preprint arXiv:1901.04846.
Palaparthi, A., Ramiya, A.M., Ram, H. and Mishra, D., 2023, January. Classification of Horticultural Crops in High Resolution Multispectral Imagery Using Deep Learning Approaches. In 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS) (Vol. 1, pp. 1-4). IEEE.
Lanjewar, M.G. and Gurav, O.L., 2022. Convolutional Neural Networks based classifications of soil images. Multimedia Tools and Applications, 81(7), pp.10313-10336.
?nik, O., ?nik, Ö., Özta?, T., Demir, Y. and Yüksel, A., 2023. Prediction of Soil Organic Matter with Deep Learning. Arabian Journal for Science and Engineering, pp.1-21.
Nasiri, A., Omid, M., Taheri-Garavand, A. and Jafari, A., 2022. Deep learning-based precision agriculture through weed recognition in sugar beet fields. Sustainable Computing: Informatics and Systems, 35, p.100759.
Odebiri, O., Mutanga, O., Odindi, J. and Naicker, R., 2023. Mapping soil organic carbon distribution across South Africa's major biomes using remote sensing-topo-climatic covariates and Concrete Autoencoder-Deep neural networks. Science of The Total Environment, 865, p.161150.
Suhag, S., Singh, N., Jadaun, S., Johri, P., Shukla, A. and Parashar, N., 2021, June. IoT based soil nutrition and plant disease detection system for smart agriculture. In 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT) (pp. 478-483). IEEE.
Durai, S.K.S. and Shamili, M.D., 2022. Smart farming using machine learning and deep learning techniques. Decision Analytics Journal, 3, p.100041.
Zhong, L., Guo, X., Xu, Z. and Ding, M., 2021. Soil properties: Their prediction and feature extraction from the LUCAS spectral library using deep convolutional neural networks. Geoderma, 402, p.115366.
Azizi, A., Gilandeh, Y.A., Mesri-Gundoshmian, T., Saleh-Bigdeli, A.A. and Moghaddam, H.A., 2020. Classification of soil aggregates: A novel approach based on deep learning. Soil and Tillage Research, 199, p.104586.
Arbawa, Y.K. and Dewi, C., 2020. Soil Nutrient Content Classification for Essential Oil Plants using kNN. In Proceedings of the 2nd International Conference of Essential Oils (pp. 96-100).
Padarian, J., Minasny, B. and McBratney, A.B., 2019. Using deep learning to predict soil properties from regional spectral data. Geoderma Regional, 16, p.e00198.
Padmapriya, J. and Sasilatha, T., 2023. Deep learning based multi-labelled soil classification and empirical estimation toward sustainable agriculture. Engineering Applications of Artificial Intelligence, 119, p.105690.
Akca, S. and Gungor, O., 2022. Semantic segmentation of soil salinity using in-situ EC measurements and deep learning based U-NET architecture. Catena, 218, p.106529.
Wang, Y., Li, M., Ji, R., Wang, M. and Zheng, L., 2021. A deep learning-based method for screening soil total nitrogen characteristic wavelengths. Computers and Electronics in Agriculture, 187, p.106228.
Kalyani, N.L. and Kolla, B.P., 2022. Soil Color as a Measurement for Estimation of Fertility using Deep Learning Techniques. International Journal of Advanced Computer Science and Applications, 13(5).
Gulhane, V.A., Rode, S.V. and Pande, C.B., 2023. Correlation analysis of soil nutrients and prediction model through ISO cluster unsupervised classification with multispectral data. Multimedia Tools and Applications, 82(2), pp.2165-2184.
Zhang, T., Zhang, Y., Wang, A., Wang, R., Chen, H. and Liu, P., 2023. Intelligent Analysis Cloud Platform for Soil Moisture-Nutrients-Salinity Content Based on Quantitative Remote Sensing. Atmosphere, 14(1), p.23.
Dong, L., Li, J., Zhang, Y., Bing, M., Liu, Y., Wu, J., Hai, X., Li, A., Wang, K., Wu, P. and Shangguan, Z., 2022. Effects of vegetation restoration types on soil nutrients and soil erodibility regulated by slope positions on the Loess Plateau. Journal of Environmental Management, 302, p.113985.
Koresh, M.H. and Deva, J., 2021. Analysis of soil nutrients based on potential productivity tests with balanced minerals for maize-chickpea crop. J. Electron, 3(01), pp.23-35.
Suchithra, M.S. and Pai, M.L., 2020. Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters. Information processing in Agriculture, 7(1), pp.72-82.
Suleymanov, A., Abakumov, E., Suleymanov, R., Gabbasova, I. and Komissarov, M., 2021. The soil nutrient digital mapping for precision agriculture cases in the trans-ural steppe zone of Russia using topographic attributes. ISPRS International Journal of Geo-Information, 10(4), p.243.
Anam, C., Naufal, A., Sutanto, H., Adi, K. and Dougherty, G., 2022. Impact of Iterative Bilateral Filtering on the Noise Power Spectrum of Computed Tomography Images. Algorithms, 15(10), p.374.
Zhou, Z., Liu, M., Deng, W., Wang, Y. and Zhu, Z., 2023. Clothing Image Classification with DenseNet201 Network and Optimized Regularized Random Vector Functional Link. Journal of Natural Fibers, 20(1), p.2190188.
Karanam, S.R., Srinivas, Y. and Chakravarty, S., 2023. A Supervised Approach to Musculoskeletal Imaging Fracture Detection and Classification Using Deep Learning Algorithms. Computer Assisted Methods in Engineering and Science.
Forootan, E., Dehvari, M., Farzaneh, S. and Karimi, S., 2023. Improving the Wet Refractivity Estimation Using the Extremely Learning Machine (ELM) Technique. Atmosphere, 14(1), p.112.
Huang, S., Tang, J., Dai, J. and Wang, Y., 2019. Signal status recognition based on 1DCNN and its feature extraction mechanism analysis. Sensors, 19(9), p.2018.
Fakhry, M. and Brery, A.F., 2022, March. A comparison study on training optimization algorithms in the biLSTM neural network for classification of PCG signals. In 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) (pp. 1-6). IEEE.
Zabalza, J., Ren, J., Zheng, J., Zhao, H., Qing, C., Yang, Z., Du, P. and Marshall, S., 2016. Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging. Neurocomputing, 185, pp.1-10.
Trontelj ml., J.; Chambers, O. Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method. Sensors 2021, 21, 4208. https://doi.org/10.3390/ s21124208.
G. Sharmila and K. Rajamohan, “Soil classification using active contour model for efficient texture feature extraction,” Int. J. Inf. Technol., vol. 15, no. 7, pp. 3791–3805, 2023, doi: 10.1007/s41870-023-01404-6.
G. Sharmila and K. Rajamohan, “Image Processing and Artificial Intelligence for Precision Agriculture,” Proc. 2022 Int. Conf. Innov. Comput. Intell. Commun. Smart Electr. Syst. ICSES 2022, pp. 1–8, 2022, doi: 10.1109/ICSES55317.2022.9914148.
G. Sharmila and K. Rajamohan, A Systematic Literature Review on Image Preprocessing and Feature Extraction Techniques in Precision Agriculture, vol. 114. Springer Nature Singapore, 2022.