An Intelligent Prediction System Using Machine Learning Techniques

Abstract

This paper presents the incident of an intelligent prophecy order based on machine intelligence techniques to produce correct and reliable outputs from recommendation data. The system includes key stages containing data preprocessing, feature pick, model training, and depiction judgment using standard versification, accompanying multiple algorithms resolved to identify ultimate appropriate model for prediction tasks. A handy interface is created to allow seamless interplay, allowing consumers to recommendation data and acquire predictions capably, while supplementary functionalities such as plant ailment diagnosis and indicator annals enhance allure applicability, specifically in the land domain. The projected system enhances in charge, reduces manual effort, and guarantees faster prediction effects, while providing a adaptable framework that maybe lengthened to incorporate state-of-the-art machine learning methods and absolute-time data conversion. The paper discusses bureaucracy construction, methodology, exercise details, and exploratory results, professed its influence in addressing honest-experience problems.

Introduction

The document presents an AI-based intelligent prediction system using Machine Learning and Deep Learning techniques, mainly focused on plant disease detection and advisory generation. It aims to improve prediction accuracy, reduce manual effort, and provide useful decision-support information, especially for agricultural applications.

The system uses EfficientNetB0 for plant disease classification. The model is trained using preprocessed and augmented image datasets and optimized through transfer learning to improve accuracy and reduce overfitting. Performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, showing strong classification results.

In addition to prediction, the system integrates a large language model (Qwen 2.0) to generate detailed, human-readable recommendations, including disease descriptions, symptoms, causes, and treatment suggestions. This makes the system not only predictive but also advisory in nature.

The methodology includes data preprocessing, feature extraction, model training, testing, and deployment. The combination of CNN-based image classification and LLM-based text generation creates an end-to-end intelligent system.

Conclusion

The project named “An Intelligent Prediction System Using Machine Learning Techniques” has happened successfully created and achieved to determine accurate and adept forecast efficiencies. The system effectively employs a Convolutional Neural Network, expressly EfficientNetB0, for plant ailment discovery from images, reaching trustworthy categorization performance. In addition, the unification of the Qwen 2.0 language model embellishes bureaucracy by generating itemized and organized information concerning disease survey, syndromes, causes, and possible answers. This mixture of image-located guess and text-located explanation molds bureaucracy into a comprehensive conclusion-support form. The grown request not only reduces manual effort but likewise develops the speed and accuracy of disease, making it well useful in honest-experience agricultural sketches. Furthermore, face such as forecasting experiences and additional facts modules increase allure usability and common sense. Overall, the project manifests the persuasive application of machine intelligence and machine intelligence methods in solving palpable-globe questions and provides a powerful organization for future augmentations and scalability.

References

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Copyright

Copyright © 2026 Yash Kumar Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.