A Contemporary CNN Implanted Deep Learning Framework for Crop Protection from Animal Intrusion in Farming Environment

In general, monitoring wild animal behavior at reasonable outlay is necessary for both managing human wildlife conflicts and protecting wildlife thereby reliable method is needed to detect wild animals. On the other side, Indian farmers endure severe risks from pests, natural disasters and animal damage resulting decreases the production process. To inspect crops and prevent animals from roaming farmers find it difficult to hire security and their standard approaches tend to be highly effective. Therefore, by applying efficient Deep Learning (DL) framework facilitate to develop Convolutional Neural Network CNN algorithm that can identify wild animal intrusion and provide an indication to end user. Further research highlights smart agriculture applications that significantly minimize losses in production by utilizing DL to make digital notifications that protect fields against mammalian attack. In addition to implementing into consideration the constraints imposed by the farming environment in terms of power supply and network connection, the proposed system is built on image processing platform that provide an optimal combination of efficiency, expense, and power consumption. Ultimately, significant advancement toward cutting-edge technology and sustainable crop protection offers farmers can able to minimize the negative effects of wildlife on the farming industry while reducing adverse ecological implications.