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@article{207140,
        author = {Mr. Rajesh Naik and Ms. Sharanya M and Ms. Ananya Kedila and Ms. Anushree},
        title = {A Literature Review on Cellular Automata Frameworks for Fruit Disease Modeling},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {4391-4397},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207140},
        abstract = {This paper addresses the limitations of static disease detection by introducing a dynamic modeling approach using Cellular Automata (CA). While current methods like convolutional neural networks (CNNs) are proficient at classifying existing diseases, they do not capture the spatio-temporal progression of an infection. This work presents a CA framework to simulate how diseases spread across a fruit's surface over time. The model represents the fruit surface as a 2D grid where each cell can exist in one of four states: Healthy, Infected, Symptomatic, or Necrotic. The system evolves based on simple, local rules governing the probabilistic spread of infection and deterministic transitions between disease stages. The paper discusses the model's core components and demonstrates its ability to generate realistic lesion morphologies, from irregular spots to classic "target" rings. This framework serves a dual purpose: forecasting the future severity of an observed lesion and generating vast synthetic datasets for training more robust static classifiers. Overall, this CA approach promises to shift the paradigm from reactive detection to proactive, predictive crop management.},
        keywords = {Cellular Automata, Fruit Disease, Predictive Modeling, Spatio-Temporal Dynamics, Precision Agriculture, Simulation Introduction (Heading 1)},
        month = {July},
        }