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@article{175771,
        author = {H S Nasir Basha and A.J.Rajasekhar},
        title = {AgriShield: A Deep Learning and Machine Learning-Based Plant Disease Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4481-4484},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175771},
        abstract = {This paper introduces Agri-Shield, a novel plant disease detection system that employs advanced deep learning techniques for early and precise agricultural diagnostics. The system utilizes a custom-designed convolutional neural network, enhanced by transfer learning, to evaluate digital images of plant foliage, achieving high accuracy in classifying various plant diseases. In addition to real-time identification, Agri-Shield provides comprehensive insights into disease causation, along with recommended preventive and remedial measures, thereby enabling prompt and informed decision-making by farmers and agronomists.
The current implementation is built on a scalable architecture incorporating a Flask backend, a React-based frontend, and a MongoDB database for historical data management. Designed with future enhancements in mind, the framework can seamlessly integrate edge computing for on-site processing, incorporate explainable AI methods to clarify prediction logic, and adopt emerging paradigms such as federated learning for secure model updates, drone-based aerial imaging for extensive crop monitoring, and blockchain for robust data logging.
This document details the complete project lifecycle—from system analysis and design through implementation and testing—and is supplemented with original visual assets, including diagrams, flowcharts, and annotated screenshots, all created using open-source tools. The comprehensive presentation of Agri-Shield underscores its potential as a significant advancement in modern agricultural technology research.},
        keywords = {},
        month = {April},
        }