Soil texture using a hydrometer or pipette method requires expertise, although these are accurate. A soil expert may help the farmer to detect the soil texture by analyzing the visual texture of the soil, which is not always accurate. This paper presents the smartphone image-based sand and clay soil classification in wet and dry humid conditions using Self Convolution Neural Network (SCNN) and finetuned MobileNet.A soil dataset of 576 soil images was prepared using a low-cost smartphone under natural light conditions. Different augmentation techniques such as shift, range, rotation, and zoom were applied to the soil dataset to increase the number of images in the soil dataset. The best performance of the MobileNet was reported at epoch 15 with a testing and training loss of 0.0091 and 0.0194, respectively. Though the SCNN model performed best at epoch 10 with a testing accuracy of 99.85%, the MobileNet reported less computation time (167.8s) than the SCNN (273.2s). The precision and recall of the models were 99.62 (MobileNet) and 99.84 (SCNN). The accuracy of the SCNN reported itself as the best model, whereas the computing time of the MobileNet reported itself as the best model in different humid conditions. The model can be used to replicate the traditional soil texture analysis method and the farmers can use it for better productivity.
CNN, Image processing, MobileNet, Precision farming, Soil texture
Barman, U. & Choudhury, R. D. (2019). Soil texture classification using multi class support vector machine. Information Processing in Agriculture.
Barman, U., Choudhury, R. D. Saud, A., Dey, S., Pratim, M. B. & Gunjan, B. G. (2018). Estimation of chlorophyll using image processing. Int J Recent Sci Res, 9(3), 24850–24853.
Bhattacharya, B. &Solomatine, D. P. (2006). Machine learning in soil classification. Neural Networks, 19(2), 186–195.
Chung, S.-O., Cho, K.-H., Kong, J.-W., Sudduth, K. A. & Jung, K. Y. (2010). Soil texture classification algorithm using RGB characteristics of soil images. IFAC Proceedings Volumes, 43(26), 34–38.
de Oliveira Morais, P. A., de Souza, D. M., Madari, B. E. & de Oliveira, A. E. (2020). A computer-assisted soil texture analysis using digitally scanned images. Computers and Electronics in Agriculture, 174, 105435.
Guang, Y., Shujun, Q., Pengfei, C., Yu, D. & Di, T. (2015). Rock and soil classification using PLS-DA and SVM combined with a laser-induced breakdown spectroscopy Library. Plasma Science and Technology, 17(8), 656.
Honawad, S. K., Chinchali, S. S., Pawar, K. & Deshpande, P. (2017). Soil classification and suitable crop prediction. National Conference On Advances In Computational Biology, Communication, And Data Analytics, 25–29.
Mengistu, A. D. & Alemayehu, D. M. (2018). Soil Characterization and Classification: A Hybrid Approach of Computer Vision and Sensor Network. International Journal of Electrical & Computer Engineering (2088-8708), 8(2).
Riese, F. M. & Keller, S. (2019). Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data. ArXiv Preprint ArXiv:1901.04846.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. & Chen, L. C. (2019). MobileNetV2: Inverted Residuals and Linear Bottlenecks. ArXiv:1801.04381 [Cs]. http://arxiv.org/abs/1801.04381
Shenbagavalli, R. &Ramar, K. (2011). Classification of soil textures based on laws features extracted from preprocessing images on sequential and random windows. Bonfring International Journal of Advances in Image Processing, 1(Inaugural Special Issue), 15–18.
Srunitha, K. & Padmavathi, S. (2016). Performance of SVM classifier for image based soil classification. 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 411–415.
Sun, Y., Long, Z., Jang, P. R. & Plodinec, M. J. (2004). Gabor wavelet image analysis for soil texture classification. Nondestructive Sensing for Food Safety, Quality, and Natural Resources, 5587, 254–261.
Swetha, R. K., Bende, P., Singh, K., Gorthi, S., Biswas, A., Li, B., Weindorf, D. C. & Chakraborty, S. (2020). Predicting soil texture from smartphone-captured digital images and an application. Geoderma, 376, 114562.
Vibhute, A. D., Kale, K. V., Dhumal, R. K. & Mehrotra, S. C. (2015). Soil type classification and mapping using hyperspectral remote sensing data. 2015 International Conference on Man and Machine Interfacing (MAMI), 1–4.
Wadoux, A. M.-C. (2019). Using deep learning for multivariate mapping of soil with quantified uncertainty. Geoderma, 351, 59–70.
Wu, W., Li, A.-D., He, X.-H., Ma, R., Liu, H.-B. & Lv, J.-K. (2018). A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China. Computers and Electronics in Agriculture, 144, 86–93.
Yu, S., He, Z. L., Stoffella, P. J., Calvert, D. V., Yang, X. E., Banks, D. J. & Baligar, V. C. (2006). Surface runoff phosphorus (P) loss in relation to phosphatase activity and soil P fractions in Florida sandy soils under citrus production. Soil Biology and Biochemistry, 38(3), 619–628.
Zhang, X., Younan, N. H. & O’Hara, C. G. (2005). Wavelet domain statistical hyperspectral soil texture classification. IEEE Transactions on Geoscience and Remote Sensing, 43(3), 615–618.
Zhao, Z., Chow, T. L., Rees, H. W., Yang, Q., Xing, Z. & Meng, F.-R. (2009). Predict soil texture distributions using an artificial neural network model. Computers and Electronics in Agriculture, 65(1), 36–48.
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