1Assistant Professor, Department of Information Technology, Manakula Vinayagar Institute of Technology, Puducherry, India. Email: uthayashangarit@mvit.edu.in
2,3,4U.G Scholar, Department of Information Technology, Manakula Vinayagar Institute of Technology, Puducherry, India. Email: hemaleela003@gmail.com, Yaazhinisaravanan09@gmail.com, praveenaarumugam1602@gmail.com
Title: Pest Prediction and Pesticides Recommendation Using Deep Learning
Agricultural pests are a serious threat to harvest production, which leads to significant economic losses and malnutrition uncertainty. Traditional methods for identifying pests are time-consuming and prone to human error. The project addresses these challenges by using deep learning techniques to automate pest detection and classification. The persuasive model spread across ImageNet has been finely tuned to identify 51 pests from the photographs. The system process uploads images, classifies pests, and provides appropriate pesticide issues. A resolution-based web interface ensures user friendly interactions and allows for efficient identification of pests. This automated approach improves accuracy, reduces identification times and supports the appropriate decision process for pest control. Future work will include expanding data records and integrating real-time monitoring of field applications.
Keywords: Pest Detection, Deep Learning, Image Classification, ConvNeXt, Flask, Transfer Learning, Agricultural Pests, Pest Identification, Pesticide Recommendation, Automated System
How to cite this article: Uthayashangar S, Hema S, Yaazhini SK, Praveena A. Pest Prediction and Pesticides Recommendation Using Deep Learning. Int J Drug Deliv Technol. 2026;16(16s): 442-450. DOI: 10.25258/ijddt.16.16s.46.
Source of support: Nil.
Conflict of interest: None