Application of deep learning models for pest detection and identification
The quality and productivity of crops are seriously threatened by insect infestations, which is the primary focus of this research. Traditional monitoring methodologies tend to be ineffective and incorrect, resulting in wasted resources and loss of money. By incorporating cutting-edge AI and deep le...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Mehran University of Engineering and Technology
2025-04-01
|
| Series: | Mehran University Research Journal of Engineering and Technology |
| Subjects: | |
| Online Access: | https://murjet.muet.edu.pk/index.php/home/article/view/293 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849701222310739968 |
|---|---|
| author | Ayesha Rafique Madiha Abbasi Noreen Akram Quratulain |
| author_facet | Ayesha Rafique Madiha Abbasi Noreen Akram Quratulain |
| author_sort | Ayesha Rafique |
| collection | DOAJ |
| description | The quality and productivity of crops are seriously threatened by insect infestations, which is the primary focus of this research. Traditional monitoring methodologies tend to be ineffective and incorrect, resulting in wasted resources and loss of money. By incorporating cutting-edge AI and deep learning technologies, this study unveils a fresh method for rapid and precisely identifying pests in agricultural settings. This research makes use of high-resolution image technologies and Convolutional Neural Networks (CNNs) to showcase the promise of deep learning models in automated pest detection. The generalizability and model performance may be improved using transfer learning techniques leading to more efficient use of available resources. Key goals of this research include extensive testing across varied pest types and environmental settings, combined with the design and refinement of a CNN model specifically engineered for accurate pest identification. The gap between traditional pest monitoring practices and data-driven procedures is filled by the suggested method which ensures a significant increase in agricultural productivity that will contribute to greater food security and overall economic prosperity. This research strengthens the influential effects on agriculture, including enhancement of pest control, increasing food security, and boosting economic expansion. To promote this cutting-edge use of deep learning in agriculture, continuous cooperation between academics, businesses, and farmers is essential. |
| format | Article |
| id | doaj-art-a7400f26ecdb455a8bb0169899a16cef |
| institution | DOAJ |
| issn | 0254-7821 2413-7219 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Mehran University of Engineering and Technology |
| record_format | Article |
| series | Mehran University Research Journal of Engineering and Technology |
| spelling | doaj-art-a7400f26ecdb455a8bb0169899a16cef2025-08-20T03:18:01ZengMehran University of Engineering and TechnologyMehran University Research Journal of Engineering and Technology0254-78212413-72192025-04-0144211712810.22581/muet1982.3080295Application of deep learning models for pest detection and identificationAyesha Rafique0Madiha Abbasi1Noreen Akram2Quratulain3Sir Syed University of Engineering and Technology, Karachi, PakistanSir Syed University of Engineering and Technology, Karachi, PakistanSir Syed University of Engineering and Technology, Karachi, PakistanSir Syed University of Engineering and Technology, Karachi, PakistanThe quality and productivity of crops are seriously threatened by insect infestations, which is the primary focus of this research. Traditional monitoring methodologies tend to be ineffective and incorrect, resulting in wasted resources and loss of money. By incorporating cutting-edge AI and deep learning technologies, this study unveils a fresh method for rapid and precisely identifying pests in agricultural settings. This research makes use of high-resolution image technologies and Convolutional Neural Networks (CNNs) to showcase the promise of deep learning models in automated pest detection. The generalizability and model performance may be improved using transfer learning techniques leading to more efficient use of available resources. Key goals of this research include extensive testing across varied pest types and environmental settings, combined with the design and refinement of a CNN model specifically engineered for accurate pest identification. The gap between traditional pest monitoring practices and data-driven procedures is filled by the suggested method which ensures a significant increase in agricultural productivity that will contribute to greater food security and overall economic prosperity. This research strengthens the influential effects on agriculture, including enhancement of pest control, increasing food security, and boosting economic expansion. To promote this cutting-edge use of deep learning in agriculture, continuous cooperation between academics, businesses, and farmers is essential.https://murjet.muet.edu.pk/index.php/home/article/view/293deep learningpest detectionfusioncnnaucroc |
| spellingShingle | Ayesha Rafique Madiha Abbasi Noreen Akram Quratulain Application of deep learning models for pest detection and identification Mehran University Research Journal of Engineering and Technology deep learning pest detection fusion cnn auc roc |
| title | Application of deep learning models for pest detection and identification |
| title_full | Application of deep learning models for pest detection and identification |
| title_fullStr | Application of deep learning models for pest detection and identification |
| title_full_unstemmed | Application of deep learning models for pest detection and identification |
| title_short | Application of deep learning models for pest detection and identification |
| title_sort | application of deep learning models for pest detection and identification |
| topic | deep learning pest detection fusion cnn auc roc |
| url | https://murjet.muet.edu.pk/index.php/home/article/view/293 |
| work_keys_str_mv | AT ayesharafique applicationofdeeplearningmodelsforpestdetectionandidentification AT madihaabbasi applicationofdeeplearningmodelsforpestdetectionandidentification AT noreenakram applicationofdeeplearningmodelsforpestdetectionandidentification AT quratulain applicationofdeeplearningmodelsforpestdetectionandidentification |