Rajalakshmi Krishnamurthi

Work place: Department of Computer Science, Jaypee Institute of Information Technology, Noida, India

E-mail: k.rajalakshmi@mail.jiit.ac.in


Research Interests: Wireless Networks, Mobile Computing, Cloud Computing, Internet of Things


Dr Rajalakshmi Krishnamurthi is a Senior Member of IEEE, Board Member in ACM-N2Women, Professional Member of ACM, SIAM and CSI. She is currently serving as Treasurer, Delhi ACM-W chapter. Dr Rajalakshmi is currently serving as Associate Professor, Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida. She has more than 17 years of teaching experience. She has more than 70 research publications in various reputed peer-reviewed International Journal, Book Chapters, and International Conferences. She has edited two books. She is serving as Guest Editor in Springer Nature and Recent Patent on Engineering. Her research interest includes Internet of Things, Cloud Computing, optimization techniques in wireless mobile networks, layer 2 problems in LTE, Femtocells, e-learning applications using mobile platform and advanced fuzzy approaches. She has introduced and developed several courses at B.Tech and M.Tech level. She has refereed in reputed Journals like IEEE IoT, Wireless Networks, Peer to Peer Springer, IGI global. She has been a technical program member in several International Conferences IC3’19, NGCT’19, NGCT’18, UPCON’19, IC3'16, IC3'17, IC3’18, ICACC’17, IC3TSN’17.

Author Articles
Enhanced Deep Learning Algorithm for Object Detection in the Agriculture Field

By Priya Singh Rajalakshmi Krishnamurthi

DOI: https://doi.org/10.5815/ijigsp.2024.03.02, Pub. Date: 8 Jun. 2024

Agriculture is one of the most prominent industries which guarantee food requirements and employment throughout the globe due to huge land availability, and atmospheric conditions. But nowadays, security of the available resources are the major concerns due to damage caused by objects inside the agriculture field. There are many traditional algorithms for object detection, but they are not very effective in terms of real time environments. Hence, a deep learning-based object detection model is generated by enhancing YOLOv3. The process involved firstly, k-means clustering was used to identify clusters, followed by modifying the convolutional neural network layers. Additionally, the batch and subdivision values of the actual YOLOv3 model were optimized under the darknet53 framework. The architecture was also configured to detect eleven classes of objects, ensuring that the model could identify a broad range of objects. The experimental results demonstrate that the Delta model achieved a remarkable increase in accuracy from 75.19% to 95.86%. In addition, the model outperformed other models in terms of precision(97%), recall(96%), F1_Score(96%), IoU(80.81%), and mAP(95.86%). Based on these findings, it can be concluded that the delta model offers superior detection capabilities and lower computational complexity compared to conventional methods used in the agriculture field.

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