Priya Singh

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



Research Interests: Computer Networks, Deep Learning


Priya Singh is an research scholar in the Department of Computer Science and Engineering at JIIT,Noida. She has done M.Tech from Banasthali University, Rajasthan and B.Tech from U.P Technical University, Lucknow. Her areas of interest are IoT, Deep Learning, Computer Network. She has guided many B.Tech projects and has published research papers in journals and reputed conferences.

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

By Priya Singh Rajalakshmi Krishnamurthi

DOI:, 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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