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AGRIGUARD: A Mobile Application for AI-Powered Crop Disease Detection and Personalized Advisory
Subject area: Science,Engineering and Technology · Area of research: AI/ML/DL Predictive Models
DOI: https://doi.org/10.64388/IREV9I10-1716746
Abstract
In Agriculture, crop diseases are the main reasons for reduced productivity and financial loss for farmers. Disease detection at early stage is important so that farmers can take proper actions to prevent it. In this project, we developed an AI-based mobile application called AgriGuard, to identify crop diseases automatically. Our system uses CNN model known as Convolutional Neural Network to analyze plant leaves images that are captured using a smartphone. This trained model predicts the type of diseases and provides suitable suggestions related to treatment to help farmers make quick decisions. This model is optimized to reduce time processing and improve performance. Our model allows farmers to get instant results directly in the field. This developed approach provides accurate predictions making it practical for real-time agricultural use.
Keywords
Crop disease detection, Deep learning, CNN, Mobile application, Image classification and AI in Agriculture.
How to cite this paper
@article{1716746,
author = {G. Sharmila, Gopika KD, Dhanapandi A, Dinesh Kumar S},
title = {AGRIGUARD: A Mobile Application for AI-Powered Crop Disease Detection and Personalized Advisory},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {2623-2629},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716746.pdf},
abstract = {In Agriculture, crop diseases are the main reasons for reduced productivity and financial loss for farmers. Disease detection at early stage is important so that farmers can take proper actions to prevent it. In this project, we developed an AI-based mobile application called AgriGuard, to identify crop diseases automatically. Our system uses CNN model known as Convolutional Neural Network to analyze plant leaves images that are captured using a smartphone. This trained model predicts the type of diseases and provides suitable suggestions related to treatment to help farmers make quick decisions. This model is optimized to reduce time processing and improve performance. Our model allows farmers to get instant results directly in the field. This developed approach provides accurate predictions making it practical for real-time agricultural use.},
keywords = {Crop disease detection, Deep learning, CNN, Mobile application, Image classification and AI in Agriculture.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716746}
}