SPECIAL ISSUE: VOL.-9, ISSUE-6, June 2023

Abstract

Date organic products are the most widely recognized natural product in the Center East and North Africa. There are a wide assortment of dates with various kinds, colors, shapes, tastes, and healthy benefits. Grouping, distinguishing, and perceiving dates would assume an essential part in the farming, business, food, and wellbeing areas. Deciding the range of natural products by taking a gander at their outer appearance might require mastery, which is tedious and requires incredible exertion. The aim of this study is to classify the types of date fruit, that are, Barhee, Deglet Nour, Sukkary, Rotab Mozafati, Ruthana, Safawi, and Sagai by using two different machine learning methods. This study presents a comparative analysis of two Naive Bayes algorithms, namely Naive Bayes and Naive Bayes Multinomial, for the classification of date fruits. The experimental results evaluate the performance of these algorithms in terms of accuracy, precision, and recall. The findings contribute to understanding the effectiveness of different Naive Bayes variants in the context of date fruit classification.

Keywords: Date Fruit Classification; Naive Bayes; Naive Bayes Multinomial; Machine Learning; Agricultural Classification; Precision; Recall

References

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Abstract

Dry beans are the most generally developed palatable vegetable harvest around the world, with high hereditary variety. Crop creation is firmly affected by seed quality. Thus, seed characterization is significant for both showcasing and creation since it helps fabricate economical cultivating frameworks. The accurate prediction of dry beans can greatly benefit agricultural practices by enabling effective crop management. In this study, we compared the performance of two popular machine learning algorithms, Decision Tree and Logistic Regression, for dry beans prediction. Experimental results showed that the Decision Tree algorithm achieved an accuracy of 95.81%, with precision and recall values of 95.9% and 95.8% respectively. On the other hand, Logistic Regression achieved an accuracy of 92.79%, with precision and recall values of 92.8%. The Decision Tree algorithm outperformed Logistic Regression in all metrics, showcasing its ability to accurately classify dry beans instances. These findings contribute to the growing body of knowledge in machine learning for agricultural applications, providing valuable insights for optimizing crop management strategies.

Keywords: Dry Beans Prediction; Decision Tree; Logistic Regression; Seed Classification; Machine Learning; Crop Management

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Abstract

The agricultural sector generates a vast amount of data, containing valuable information that can be effectively analyzed through data mining techniques. Classification prediction is a crucial method in agricultural data mining. This study presents three AI algorithms - Decision Tree, Random Forest, and SVM aimed at enhancing the classification accuracy for multi-class agricultural data. The performance of these algorithms was evaluated using a standard agricultural multi-class dataset of Eucalyptus. The experimental results demonstrate that the Random Forest approach achieved significant improvements in classification accuracy for the Eucalyptus dataset, achieving a precision of 95.84%.

Keywords: Multi-Class Classification; Random Forest; Decision Tree; SVM; Eucalyptus Dataset; Agricultural Data Mining; AI in Agriculture

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Abstract

Pumpkin seeds are frequently consumed as confection worldwide because of their adequate amount of protein, fat, carbohydrate, and mineral contents. Pumpkin seeds are frequently consumed as confection worldwide because of their adequate amount of protein, fat, carbohydrate, and mineral contents. This research paper presents a comparative analysis of three ensemble learning algorithms, namely Bagging, AdaBoost, and LogitBoost, for the prediction of pumpkin seed quality. The study aims to determine the algorithm that provides the most accurate and reliable predictions. Experimental results are obtained using a carefully curated dataset, and various evaluation metrics including accuracy, precision, and recall are utilized to assess the performance of each algorithm.

Keywords: Pumpkin Seed Prediction; Ensemble Learning; Bagging; AdaBoost; LogitBoost; Seed Quality; Machine Learning

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Abstract

This study compares the performance of three machine learning algorithms, namely Decision Tree, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP), for predicting outcomes on the Raisin dataset. The dataset comprises features related to raisin production, and the objective is to predict a binary outcome based on these features. The algorithms were evaluated using accuracy, precision, and recall metrics. The results demonstrate that the Decision Tree algorithm outperforms the others with consistent scores of 89.22% across all metrics. While SVM and MLP also yield strong performance, the Decision Tree algorithm emerges as the most reliable choice for predicting raisin production outcomes. These findings highlight the significance of algorithm selection and evaluation in machine learning tasks, providing insights for future research in agricultural forecasting and similar domains.

Keywords: Raisin Classification; Decision Tree; SVM; Multi-Layer Perceptron; Binary Classification; Machine Learning; Accuracy Comparison

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Abstract

Rice being perhaps of the most broadly delivered and consumed cereal harvest on the planet, is likewise the one of the fundamental food in our country on account of its efficient and nutritious nature. The prediction of crop yield plays a crucial role in agricultural planning and resource allocation. In this study, we compare the performance of two popular machine learning algorithms, Multilayer Perceptron (MLP) and Logistic Regression (LR), for predicting rice crop yield using the Rice Dataset. The dataset contains various features related to environmental conditions, soil properties, and agricultural practices, along with corresponding yield measurements. The algorithms were trained and evaluated based on accuracy, precision, and recall metrics to assess their predictive capabilities.

Keywords: Rice Yield Prediction; Multilayer Perceptron; Logistic Regression; Crop Yield; Agricultural Planning; Machine Learning

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Abstract

Soybean prediction plays a crucial role in optimizing agricultural practices, enhancing crop yield, and ensuring food security. Machine learning algorithms have proven to be effective tools in predicting crop outcomes based on various environmental factors. This research paper presents a comprehensive comparative analysis of two popular algorithms, Naive Bayes and Logistic Regression, for soybean prediction. The study aims to evaluate the performance of these algorithms in terms of accuracy, precision, recall, and F1-score. Additionally, we investigate the interpretability and computational efficiency of each algorithm to provide valuable insights for agricultural decision-making. The experimental results demonstrate the strengths and weaknesses of both algorithms and provide recommendations for selecting the most suitable algorithm for soybean prediction.

Keywords: Soybean Prediction; Naive Bayes; Logistic Regression; Crop Yield; F1-Score; Food Security; Machine Learning

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Abstract

Agricultural production and operations generate vast amounts of data, harboring crucial information. Data mining technology can analyze the relationships between various variables within the extensive agricultural dataset. Classification prediction is one of the most significant data mining techniques for agricultural data. This paper presents an exploratory study utilizing three AI algorithms: Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) to address the challenges of multi-class classification in agriculture. The algorithms were tested on the Eucalyptus standard agricultural multi-class dataset, and the results showed that the MLP method performed exceptionally well, achieving a significant increase in classification accuracy for the Eucalyptus dataset, with a precision of 89.97%.

Keywords: Multi-Class Classification; Multi-Layer Perceptron; SVM; Eucalyptus Dataset; Data Mining; AI; Agricultural Data Analysis

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Abstract

Ensemble classification is a powerful approach that combines multiple base classifiers to improve the accuracy and robustness of predictions. This paper presents a comprehensive study on ensemble classification techniques and their applications. We discuss two ensemble methods, including bagging and boosting along with their underlying principles and benefits. This paper focuses on the application of classification methods, specifically Bagging and Boosting, for predicting fish catch data. A dataset comprising 252 cases with 15 independent variables and one dependent variable was utilized for the analysis. Experimental results demonstrate that Boosting outperforms Bagging in terms of accuracy and precision. This exploratory study sheds light on the suitability and effectiveness of classification algorithms for fish catch prediction. Experimental results demonstrate that ensemble classification consistently outperforms single classifiers in terms of accuracy, precision, and recall. The findings highlight the effectiveness of ensemble techniques for solving complex classification problems and provide insights for researchers and practitioners in the field.

Keywords: Fish Catch Prediction; Ensemble Classification; Bagging; Boosting; Machine Learning; Fishery Data; Predictive Modeling

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Abstract

Sugarcane, a vital agricultural produce, is susceptible to diseases that can severely impact crop quality and yield. Early identification and effective mitigation of sugarcane diseases are crucial for successful crop management. Disease outbreaks can lead to significant financial losses for farmers as they can devastate entire crop fields. To combat this issue, researchers are exploring the application of Artificial Intelligence (AI) techniques, particularly Bagging and Boosting algorithms in Machine Learning (ML), to analyze agricultural data and prevent crop damage caused by various factors, with diseases being a major concern.

 The research is prompted by the rapid evolution of sugarcane disease classes and the lack of disease diagnostic and recognition skills among farmers. By leveraging Bagging and Boosting algorithms, sugarcane farmers can gain valuable insights into disease identification and prediction, enabling them to take timely preventive measures. Integrating AI and ML techniques in sugarcane cultivation can enhance disease management strategies, safeguard crop productivity, and contribute to sustainable agricultural practices.

Keywords: Sugarcane Disease Resistance; Bagging; Boosting; Machine Learning; Crop Management; Disease Classification; AI in Agriculture

References

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