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Convolutional Neural Networks based classifications of soil images

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Abstract

The utilization of Artificial Intelligence (AI) and Machine Learning(ML) in the image processing domain is useful to detect and recognize the types of soil. The main aim of the present work is to process the soil images and classify them accurately by using Tensorflow and Keras Deep Learning (DL) frameworks with pre-trained weights. There are several ML models already implemented for the classification of soil images. A dataset has 903 soil images of four different types of soil (alluvial, black, clay, and red). These images were divided into a training dataset and a validation dataset. The image augmentation process was applied to the dataset, and then the models are trained with these augmented images. In the present work, the Convolutional Neural Network (CNN) model was implemented to classify the soil images and achieved an accuracy of 99.86% for training and 97.68% for validation. Furthermore, six Deep Convolution Neural Network (DCNN) models were implemented, such as Rsnet152V2, VGG-16, VGG-19, Inception-ResNetV2, Xception, and DenseNet201, to classify the soil images. The accuracy of a Rsnet152V2, VGG-16, VGG-19, Inception-ResNetV2, Xception, and Densnet201 DCNN models were 99.15%, 97.58%, 98.44%, 98.15%, 98.86%, and 98.58%, respectively. The performance of CNN and DCNN models was evaluated using a confusion matrix and K-fold technique. The proposed CNN model has outperformed the DCNN models, and also literature reported works.

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Authors and Affiliations

  1. School of Physical and Applied Sciences, Goa University, Taleigao Plateau, Goa, 403206, India

    M. G. Lanjewar

  2. Science College Pimpri, Maharashtra, Pune, 411018, India

    O. L. Gurav

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M G Lanjewar: Methodology, Software, Validation, Investigation, Writing - Original Draft, Performance analysis. O L Gurav: Dataset creation, Software.

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Correspondence to M. G. Lanjewar.

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Lanjewar, M.G., Gurav, O.L. Convolutional Neural Networks based classifications of soil images. Multimed Tools Appl 81, 10313–10336 (2022). https://doi.org/10.1007/s11042-022-12200-y

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