A comprehensive review on soil classification using deep learning and computer vision techniques

Srivastava, Pallavi; Shukla, Aasheesh; Bansal, Atul

doi:10.1007/s11042-021-10544-5

Cited by 72 publications

(22 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…al. [3] exhibited some research that gives a detailed overview of soil classifying techniques and these methods may be classified into two groups. Firstly, Image Processing and then followed by Computer Visionbased soil classification, in which image capture, segmentation, feature extraction, and soil classification are the four phases involved in these methods.Among the classification algorithms used in the approaches are random forest, maximum likelihood estimation, and k-nearest neighbour.…”

Section: Background and Related Workmentioning

confidence: 99%

Visual Transformer for Soil Classification

Jagetia

Goenka

Kumari

et al. 2022

2022 IEEE Students Conference on Engineering and Systems (SCES)

View full text Add to dashboard Cite

Our food security is built on the foundation of soil. Farmers would be unable to feed us with fiber, food, and fuel if the soils were not healthy. Accurately predicting the type of soil helps in planning the usage of the soil and thus increasing productivity. This research employs state-of-theart Visual Transformers and also compares performance with different models such as SVM, Alexnet, Resnet, and CNN. Furthermore, this study also focuses on differentiating different Visual Transformers architectures. For the classification of soil type, the dataset consists of 4 different types of soil samples such as alluvial, red, black, and clay. The Visual Transformer model outperforms other models in terms of both test and train accuracies by attaining 98.13% on training and 93.62% while testing. The performance of the Visual Transformer exceeds the performance of other models by at least 2%. Hence, the novel Visual Transformers can be used for Computer Vision tasks including Soil Classification.

show abstract

Section: Background and Related Workmentioning

confidence: 99%

Visual Transformer for Soil Classification

Jagetia

Goenka

Kumari

et al. 2022

2022 IEEE Students Conference on Engineering and Systems (SCES)

View full text Add to dashboard Cite

show abstract

“…SVM with polynomial kernel function shows best performance with overall accuracy of 94.30%. Srivastava et al [30] presented a vast review of soil texture classification which included methods with conventional image processing techniques as well as deep learning algorithms. Ajdadi et al [13] presented a machine vision technique to classify soil aggregate size.…”

Section: State-of-the-artmentioning

confidence: 99%

Transfer Learning Analysis for Predicting Soil Texture Classes from Soil Images

2022

View full text Add to dashboard Cite

“…In addition, CNN has a great role for soil classification [25][26][27], hydrogeological classification [19], lithology [28], soil permeability [29], land temperature forecasting [30], flood susceptibility map [10,21], predicting groundwater level and flow [31][32][33], and prediction of rainfall [34]. CNN can be applied in the recognition of waste type [11] and groundwater quality prediction.…”

Section: Introductionmentioning

confidence: 99%

Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones

Tegegne

2022

Journal of Engineering

View full text Add to dashboard Cite

In the field of groundwater engineering, a convolutional neural network (CNN) has become a great role to assess the spatial groundwater potentiality zones and land use/land cover changes based on remote sensing (RS) technology. CNN can be offering a great potential to extract complex spatial features with multiple high levels of generalization. However, geometric distortion and fuzzy entity boundaries as well as a huge data preparation severance may be the main constraint and affect the spatial potential of CNN application for land cover classification. This study aims to recognize the proficiency of deep learning algorithms, i.e., CNN, for spatial assessment of groundwater potential zones and land cover. Among the groundwater influencing factors, classification of land cover (agriculture, built-up, water bodies, forests, and bare land) has been reported by several researchers for different purposes and they approved the CNN capability for the prediction of spatial groundwater potentiality zones like very high, high, moderate, poor, and very poor areas. In this study, CNN is recommended as a very essential algorithm for the identification of groundwater potential zones and classification of land use/land cover change. CNN gives a better option for scholars regarding when the limited data sets are available for validation.

show abstract

A comprehensive review on soil classification using deep learning and computer vision techniques

Cited by 72 publications

References 46 publications

Visual Transformer for Soil Classification

Visual Transformer for Soil Classification

Transfer Learning Analysis for Predicting Soil Texture Classes from Soil Images

Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones

Contact Info

Product

Resources

About