A Review on Pattern Recognition Using Machine Learning

Saini, Preeti; Kaur, Jasleen; Lamba, Shweta

doi:10.1007/978-981-16-0942-8_58

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…Over the previous two decades, machine learning (ML) techniques have attracted much attention to diagnose the disease and a suitable solution. [2] DOI: 10.1002/masy. 202200216 In the agriculture industry, prompt plant disease diagnosis is vital.…”

Section: Introductionmentioning

confidence: 99%

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Image‐Feature Based Deep Learning Model for Plant Leaf Disease Detection

Rani,

Sarangi,

Sahoo

2024

Macromolecular Symposia

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Currently, everyone is facing significant difficulties with food scarcity. There may be several causes for this, but food loss is a well‐known issue. Specifically, crop losses bring on by plant and leaf diseases during farming operations. Plant disease is typically identified visually or through laboratory testing, which delays detection and reduces crop yields by the time it is finished. The wide use of smartphones along with recent advancements in computer vision has made it viable to diagnose any ailment by applying machine learning techniques. Smartphone‐assisted disease diagnosis is now a reality. In this study, a multi‐Support Vector Machine (SVM) model is used to detect four different diseases as well as healthy conditions of plant leaves. Using 13 feature vectors for each input, a total of 2400 leaves representing four distinct classes (four varieties) have been used to train and test the model. The experimental results give an average accuracy of 91.25% for diseased leaves detection and 99% accuracy for healthy leaves detection.

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Section: Introductionmentioning

confidence: 99%

“…Over the previous two decades, machine learning (ML) techniques have attracted much attention to diagnose the disease and a suitable solution. [ 2 ]…”

Section: Introductionmentioning

confidence: 99%

Image‐Feature Based Deep Learning Model for Plant Leaf Disease Detection

Rani,

Sarangi,

Sahoo

2024

Macromolecular Symposia

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“…The market has seen the rise of open-source apps with the introduction of artificial DOI: 10.1002/masy.202200207 intelligence and more broadly the ability to increase the threshold of farming expertize without the requirement for large capital expenditures. [1] One primary emphasis is on Artificial Neural Networks, self-learning algorithms can assist in the creation of fresh insights into the settings and schedules that maximize a specific process's yield and quality. Smart agriculture with precision farming has become the global trends in modern agricultural development today.…”

Section: Introductionmentioning

confidence: 99%

Implementation of AI Based Model for Identification of Different Soil Types Using Tensorflow

Mavi,

Mittal,

Sarangi

2024

Macromolecular Symposia

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The agricultural industry is among the most important industry in the world and playing a critical part in human growth. Soil classification is becoming incredibly valuable, and recent study reveals that classification of images is proven as the preferred way of soil information for farmers. The testing of soil is the first step in construction planning to determine whether the plot of land is suitable for constructing any structure in order to prevent future disasters. In this work, the model has been proposed to address the visual classification of types of soil using the deep neural network (DNN) and tensor flow framework. Further, AI based sequential model has been created in python to classify the soil. This has been analyzed that with the use of proposed technique five types of soil viz. black, cinder, laterite, peat, and yellow soil has been classified while keeping the practical simplicity of the images. Far superior accuracy has been achieved using the CNN model on the above dataset. The model is useful for farmers to increase the crop production after identification of the appropriate soil type for respective crops.

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