CITRUS PEST DISEASE RECOGNITION APP

April 2024
Vol-10, Issue-2
Paper ID: 23093
ISSN: 2395-4396
Downloads: 0

Abstract & Details

Research Area
Computer Engineering
Keywords
Deep Learning Citrus Pests Recurrent Neural Network Convolutional Neural Network Flutter Dart 
Abstract
Citrus crops play a crucial role in global agriculture and economy, but they are constantly threatened by various pests and diseases, posing significant challenges to growers. Early detection and accurate identification of pest diseases are crucial for effective management and mitigation strategies. This abstract presents a mobile application designed to address this pressing issue by leveraging the power of artificial intelligence (AI) and image recognition technology. The Citrus Pest Disease Recognition App utilizes advanced machine learning algorithms to identify and classify common pests and diseases affecting citrus crops. Through the integration of image recognition technology, users can simply capture images of affected citrus plants using their smartphones or tablets. The captured images are then processed by the app, which employs a trained AI model to analyze the symptoms and patterns indicative of specific pests or diseases. The app provides real-time feedback, promptly alerting users to the presence of potential threats in their citrus orchards. By accurately identifying the pest or disease, growers can swiftly implement appropriate management measures, such as targeted pesticide application or cultural practices, thus minimizing crop damage and ensuring optimal yield and quality. Furthermore, the Citrus Pest Disease Recognition App offers additional features such as pest and disease information, management recommendations, and integration with existing agricultural databases. This comprehensive tool empowers growers with valuable knowledge and resources to effectively combat pest infestations and disease outbreaks, ultimately contributing to the sustainability and resilience of citrus cultivation worldwide. In conclusion, the Citrus Pest Disease Recognition App represents a significant advancement in agricultural technology, providing growers with a user-friendly and efficient solution for early pest and disease detection. By harnessing the capabilities of AI and image recognition, this app has the potential to revolutionize citrus crop management practices, promoting sustainable agriculture and safeguarding global citrus production.

Author Information

# Name Institute / Affiliation
1 ATHISH S R Bannari Amman Institute of Technology
2 RAAGAVENDIRAN M Bannari Amman Institute of Technology
3 PRITHVI RAJ L Bannari Amman Institute of Technology
4 KALAIVANI E Bannari Amman Institute of Technology

How to Cite

Use the following formats to cite this article in your research.

APA Style
R, ATHISH S, M, RAAGAVENDIRAN, L, PRITHVI RAJ, & E, KALAIVANI (2024). CITRUS PEST DISEASE RECOGNITION APP. International Journal of Advance Research and Innovative Ideas In Education, 10(2), 2519-2528.
MLA Style
R, ATHISH S, et al. "CITRUS PEST DISEASE RECOGNITION APP." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 2, 2024, pp. 2519-2528.
IEEE Style
ATHISH S R, RAAGAVENDIRAN M, PRITHVI RAJ L, and KALAIVANI E, "CITRUS PEST DISEASE RECOGNITION APP," International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 2, pp. 2519-2528, 2024.
Vancouver Style
R ATHISH S, M RAAGAVENDIRAN, L PRITHVI RAJ, E KALAIVANI. CITRUS PEST DISEASE RECOGNITION APP. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(2):2519-2528.
Harvard Style
R, ATHISH S, M, RAAGAVENDIRAN, L, PRITHVI RAJ, & E, KALAIVANI (2024) 'CITRUS PEST DISEASE RECOGNITION APP', International Journal of Advance Research and Innovative Ideas In Education, 10(2), pp. 2519-2528.
Chicago Style
R, ATHISH S, et al. "CITRUS PEST DISEASE RECOGNITION APP." International Journal of Advance Research and Innovative Ideas In Education 10, no. 2 (2024): 2519-2528.
Turabian Style
R, ATHISH S, et al. "CITRUS PEST DISEASE RECOGNITION APP." International Journal of Advance Research and Innovative Ideas In Education 10, no. 2 (2024): 2519-2528.

Export Citation

Related Research

CYBERSECURITY WITH AI
SAHANA MURTHY 2026 Computer Science
PDF Unavailable
A NOVEL HYBRID IMAGE STEGANOGRAPHY TECHNIQUE BASED ON LSB AND CRYPTOGRAPHIC SECURITY
Pankaj Nandan et al. 2026 Computer Science
PDF Unavailable
AnimalAid AI: A Deep Learning Powered Early Warning System for Detecting Skin Infections and Diseases in Stray Dogs
Sharan Subhas Savalagi et al. 2026 Computer Science and Engineering
PDF Unavailable
LiverCare AI: Intelligent Medical Imaging Platform for Liver Tumor Detection and Clinical Guidance
Sheshank et al. 2026 Computer Science and Engineering
PDF Unavailable