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.