Soil Classification Using Modified Support Vector Machine

Vijay, E. Vargil; Ch, Navya; Begum, Abdul Shabana; Rajaneesh, D; B, Mahesh Babu

doi:10.32622/ijrat.88202001

Cited by 3 publications

(3 citation statements)

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“…al. [10] presents Soil Classification Using Modified Support Vector Machine. The conventional methods for classifying soils are examined in this analysis, and an image processing method for classifying soils with an effective classifier has been developed and tested.…”

Section: Literature Surveymentioning

confidence: 99%

Soil Health Analysis and Classification of Micronutrients for Crop Suggestion Using Machine Learning

N.,

Prasad K

2024

IJMCR

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An essential component of agriculture is soil. Many types of soil exists. Different parameters can be observed in each type of soil, and various crops can be grown on different types of soils. To understand which crops develop in certain types of soil, they need to be informed on the characteristics and features of various soil types. The capability to identify the inputs needed for productive and cost-effective farming was provided by soil analysis. A proper soil test will ensure that the proper amount of fertilizers are utilized, providing the crop to meet its needs while also profiting from the nutrients already available in the soil. The phrase "soil health" refers to the biological, chemical, and physical elements of the soil that are significant for plant nutrients. Soil analysis is a collection of different chemical processes that determines the amounts of available plant nutrients in the soil. The most important factors for producing a healthy crop are micronutrient analysis. The classification of soil's micronutrients is the primary objective of this analysis. In this case, machine learning methods may be helpful. It has made significant progress in recent years. Hence in this analysis, soil health analysis and classification of Micronutrients for crop suggestion using Machine Learning is presented. The coloured images of the soil samples are obtained and processed using a number of algorithms and filters. Color, texture, and other features can be extracted using these developed algorithms. Logistic Regression algorithm is used to classify micronutrients.

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Section: Literature Surveymentioning

confidence: 99%

Soil Health Analysis and Classification of Micronutrients for Crop Suggestion Using Machine Learning

N.,

Prasad K

2024

IJMCR

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“…They opted for SVM due to its versatility and suitability for comparison purposes. The research utilized a range of preprocessing methods to identify patterns, leading to improved classification analysis [2].…”

Section: Literature Surveymentioning

confidence: 99%

“…Some of the existing models are implemented using both Machine Learning and Deep Learning Techniques [1]. The author implements a model using K-NN Classifier and SVM Classifier on different types of soil image datasets [2] [9] [14]. The author implements a model using an SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

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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Analytical Approach for Soil and Land Classification Using Image Processing with Deep Learning

Aparna

Somasekhar

Bhaskar

et al. 2023

2023 2nd International Conference for Innovation in Technology (INOCON)

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Soil Classification Using Modified Support Vector Machine

Cited by 3 publications

References 0 publications

Soil Health Analysis and Classification of Micronutrients for Crop Suggestion Using Machine Learning

Soil Health Analysis and Classification of Micronutrients for Crop Suggestion Using Machine Learning

Investigating Efficiency of Soil Classification System using Neural Network Models

Analytical Approach for Soil and Land Classification Using Image Processing with Deep Learning

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