Authors

Varsha Patel

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Abstract

The study emphasizes the characteristics, prerequisites, and inherent challenges involved in the accurate identification and assessment of plant diseases. It illustrates the application of Convolutional Neural Networks (CNNs) as a robust approach for classifying and validating disease conditions in crops. A comprehensive plant disease diagnosis system is proposed, wherein CNN-based models are employed to automatically detect, classify, and quantify the severity of infections. In this AI-driven framework, a deep neural network is trained on a curated dataset of crop leaf images to categorize diseases into multiple predefined classes, enabling precise and rapid disease recognition. The proposed system is designed for real-time deployment via a mobile application, allowing farmers to capture plant images and receive instant diagnostic results along with expert-recommended preventive and remedial measures. Beyond individual diagnosis, the system incorporates a geo-tagged database that aggregates disease occurrence data to facilitate spatial mapping, density analysis, and predictive modeling of disease spread. This functionality enables agricultural experts to monitor regional disease patterns and implement proactive interventions through an interactive web-based platform. The architecture leverages cloud computing and scalable services, ensuring that the system can handle large datasets, support multiple users, and continuously update disease models with new data. By integrating AI, mobile accessibility, and cloud-based analytics, the framework not only enhances the accuracy and speed of disease detection but also contributes to sustainable crop management by reducing unnecessary pesticide application. The system demonstrates the potential of combining advanced image processing, deep learning, and geospatial analytics to empower farmers, optimize resource utilization, and mitigate crop losses at both local and regional scales.

Keywords

Plant Disease Diagnosis AI-driven Solution Convolutional Neural Network (CNN) Deep Neural Network Model Image Processing Disease Classification Mobile App for Farmers

Citation of this Article

Varsha Patel. (2025). Intelligent Crop Health Monitoring System Using Machine Learning and Cloud Infrastructure. Journal of Artificial Intelligence and Emerging Technologies. 2(6), 1-5. Article DOI: https://doi.org/10.47001/JAIET/2025.206001

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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