Classification of Banana Leaf Diseases Using Enhanced Gabor Feature Descriptor

Mary, Nirmala; Singh, Anshuman; Athisayamani, Suganya

doi:10.1007/978-981-15-7345-3_19

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…This is the learning step (or training phase), where a classification algorithm builds the classifier by analyzing or "learning from" a training set made up of tuples and their associated class labels. In the second step, the model is used for classification [24]. Then the predictive accuracy of the classifier is estimated using the test set.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Statistical Methods for Classification of Human Semen Data

V.S¹,

Mary²

2023

Preprint

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Classification is quite an important task in medical data analysis. The goal of classification is to build a concise model of the distribution of the dependent attribute in terms of the predictor attributes. Statistical classification is the problem of identifying the subpopulation to which new observations belong to whereas probabilistic classification returns a probability distribution for the class label attribute rather than returning a single class label. The aim is to classify human semen sample either as fertile or infertile using probabilistic and statistical classification algorithm. The accuracy of classifying the semen sample is assessed using Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis, Naive Bayes and Logistic Regression to distinguish infertile men from normal individuals. The comparisons were made in terms of sensitivity, specificity, precision, accuracy and error to determine the suitability of the statistical methods. The results have shown that QDA outperforms Logistic Regression, Naive Bayes and LDA in classifying the semen samples.

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Section: Introductionmentioning

confidence: 99%

Comparison of Statistical Methods for Classification of Human Semen Data

V.S¹,

Mary²

2023

Preprint

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“…Besides, there is a prerequisite for a previous sickness. Acknowledgment framework for safeguarding the yield over the long run [5].As a result, CNN is extensively used to identify agricultural diseases, with acceptable results. However, the crop disease photos collected from lands were characterized by a high level of unpredictability, which has an important effect on the improvement of crop disease detection via image accuracy [6].The "Plant Village dataset" a widely used dataset for identifying leaf diseases is used to identify these illnesses [7].CNN trained the big datasets.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Crop Diseases

Rabbani,

Singh

2023

IJSREM

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Abstract— Agriculture is a significant yet low-paying occupation in India. Machine learning has the ability to transform agriculture by altering the revenue scenario through crop selection. The goal of this study is to estimate agricultural yield using various machine learning techniques. The outcomes of various solutions are compared using mean absolute error. The predictions of machine learning algorithms will help farmers decide which crop to produce to optimize yield. Keywords- CNN,RNN,ReLU,

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