An Analytical Approach for Soil and Land Classification System using Image Processing

Deorankar, A. V.; Rohankar, Ashwini A.

doi:10.1109/icces48766.2020.9137952

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…Texture characteristics are extracted using the Laws mask method, involving filtering images with five different mask types: edge, ripple, level, wave and spot. The application of Convolutional Neural Networks (CNNs) with three-layered hidden layers enables the algorithm to effectively classify land images and soil, distinguishing between categories such as Clayey peat, Silty sand, Clay, Humus clay, Peat, Sandy clay and Clayey sand [6].…”

Section: Literature Surveymentioning

confidence: 99%

“…The study in [5] [16] proposed a technique implementing an, Network Model (CNN), SVM classifier and StoolNet. The research in [15] [6], the author implemented a Network Model based on Convolutional Neural Networks (CNN). This model was designed to filter images using five different types of masks: spot, wave, level, ripple, and edge.…”

Section: Introductionmentioning

confidence: 99%

“…This hybrid approach is utilized to analyze physical parameters like water content and dry density, as well as soil parameters such as internal friction angle, cohesion. Simultaneously in [6] the study employs a Gabor Filter for edge detection, followed by Std, skew, Mean, and kurtosis and other sinusoidal measures www.ijacsa.thesai.org to retrieve images. The classification task involves using the CNN algorithm for land and soil images.…”

Section: Introductionmentioning

confidence: 99%

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Investigating Efficiency of Soil Classification System using Neural Network Models

Rao,

Priyanka,

Rao

et al. 2023

IJACSA

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Soil is a vital requirement for agricultural activities providing numerous functionalities restoring both abiotic and biotic materials. There are different types of soils, and each type of soil possesses distinctive characteristics and unique harvesting properties that impact agricultural development in various ways. Generally, farmers in the olden days used to analyse soil by looking at it visually while some prefer laboratory tests which are time-consuming and costly. Testing of soil is done to analyse the features and characteristics of the soil type, which results in selecting a suitable crop. This in turn results in increased food productivity which is very beneficial to farmers. Hence, to recognize the soil type an automatic soil identification model is proposed by implementing Deep Learning Techniques. It is used to classify the soil for crop recommendation by analysing accurate soil type. Different Convolution Neural Networks have been applied in the proposed model. They are VGG16, VGG19, InceptionV3 and ResNet50.Among all those techniques it is analysed that better results were obtained with ResNet50 having an accuracy of about 87% performing Multi-classification that is Black soil, Laterite Soil, Yellow Soil, Cinder soil & Peat soil.

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Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating Efficiency of Soil Classification System using Neural Network Models

Rao,

Priyanka,

Rao

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…A careful assessment of land can be used to utilize the available land effectively and boost productivity without burdening the ecosystem. Land cover mapping aids in establishing the relationship between the environment and anthropogenic activities (Prakasam, 2010; Jensen and Cowen, 2011; Deorankar and Rohankar, 2020). Remote sensing satellite images can be used to examine the dynamic profile of land use land cover.…”

Section: Introductionmentioning

confidence: 99%