Paper Title
AUTONOMOUS GRID-DRIVEN UNMANNED AERIAL VEHICLE TECHNOLOGY FOR PRECISE QUANTIFICATION IN MULTI-CROPPEDFIELDS

Abstract
Inspired by the ongoing difficulties farmers have with conventional crop quantification procedures—which are marked by labourintensive techniques and constrained field coverage—this research aims to transform agriculture via technological innovation. Our revolutionary method is based on the combinationofmeticulouslyselecteddatasets,photographedwith aRaspberryPicamerabyaUAV,andYouOnlyLookOnceTiny (YOLO) object detection framework. Seeing the need for a solution that goes beyond traditional constraints, our methodology incorporatesaUAVthatdividesthegridautonomously,ensuring comprehensive field representation dynamically. By addressing the issues with manual quantification, this strategic integration gives farmers a comprehensive understanding of crop health and distribution. The automated system that was created as a result helps farmers overcome obstacles and provides them with real- time insights, which greatly aids in the pursuit of sustainable agriculture and global food security. Abstract - Inspired by the ongoing difficulties farmers have with conventional crop quantification procedures—which are marked by labour intensive techniques and constrained field coverage—this research aims to transform agriculture via technological innovation. Our revolutionary method is based on the combination of meticulously selected datasets, photographed with a Raspberry Pi camera by a UAV, and You Only Look Once Tiny (YOLO) object detection framework. Seeing the need for a solution that goes beyond traditional constraints, our methodology incorporates a UAV that divides the grid autonomously, ensuring comprehensive field representation dynamically. By addressing the issues with manual quantification, this strategic integration gives farmers a comprehensive understanding of crop health and distribution. The automated system that was created as a result helps farmers overcome obstacles and provides them with real- time insights, which greatly aids in the pursuit of sustainable agriculture and global food security. Keywords - Agriculture, Crop Quantification, Unmanned Aerial Vehicle, YOLO, Automated System.