Paper Title
AI-Infused Plant Pathology: An App-Based Disease Detection System
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Registration ID: IJNRD_304745
Published ID: IJNRD2503412
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Keywords
Artificial Intelligence, Plant Disease Detection, Machine Learning, Mobile Application Development, Data Collection, Optimization, Agricultural Technology, AI Integration, Future Challenges.
Abstract
The rapid advancement of artificial intelligence (AI) technologies has significantly impacted various sectors, with agriculture being a notable beneficiary. This paper explores the application of AI in plant disease detection, focusing on the development of an AI-infused app based system. The system aims to enhance the early detection and management of plant diseases through machine learning techniques, mobile application development, and robust data processing methodologies. The study begins with an overview of AI technologies in agriculture, detailing how these innovations are revolutionizing traditional farming practices. It then delves into specific machinelearning techniques employed in plant disease detection, such as convolutional neural networks and support vector machines, which improve diagnostic accuracy. The development of mobile applications for disease detection is discussed, highlighting the importance of user friendly interfaces and real-time data processing. Key aspects of data collection and preprocessing are examined, emphasizing the need for high quality, diverse datasets to train effective models. Model training and optimization strategies are also covered, showcasing how hyperparameter tuning and crossvalidation contribute to model performance. The integration and deployment of AI models into practical applications are explored, demonstrating the challenges and solutions associated with bringing AI technologies to the field. Finally, the paper addresses future directions and challenges, including the need for scalable solutions, enhanced model generalisation, and broader accessibility for farmers. The conclusion reflects on the potential of AI to transform plant disease management and outlines the ongoing efforts required to maximize its impact on global agriculture.
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How To Cite (APA)
Kajal Burbure, Dr. Ashutosh Lanjewar, Krutika Meshram, Punam Ambule,, & Sanjavi Nishane (March-2025). AI-Infused Plant Pathology: An App-Based Disease Detection System . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(3), e83-e85. https://ijnrd.org/papers/IJNRD2503412.pdf
Issue
Volume 10 Issue 3, March-2025
Pages : e83-e85
Other Publication Details
Paper Reg. ID: IJNRD_304745
Published Paper Id: IJNRD2503412
Research Area: Science and Technology
Author Type: Indian Author
Country: nagpur, maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2503412.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2503412
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