Automated Papaya Farm Monitoring system using Unmanned Aerial Vehicle (UAV)

Abstract

Papaya Farm monitoring plays a key role in taking the required early-stage steps to increase the efficiency of harvesting. Using Unmanned aerial vehicles (UAVs) we can increase the efficiency of faming and get maximum yield. Improving performance Unmanned aerial vehicles (UAVs) offer a quick and reliable means of collecting data from difficult to reach large farms. Awareness of the latest usability and technologies used in UAVs and the techniques of programming used to process images taken from UAVs. The approaches used to evaluate the stage of development with available algorithms. To plants, local binary patterns, distance transformation, and watershed segmentation methods are applied to pictures. Using YOLOv4 architecture trine the system to program to detect the plats and find the deficiency in it. this will help to monitor the large farm very easy. Papaya is one of the plants that have the we can find the deficiency of the tree by using monitoring the arial leaf images. at proper care of the plant yielding is can be improved. here with the help of ML (machine learning) the program can be done. using python, we are going to trine the system and gest the result from the program. making the use of available algorithms to modify the performance the get the best result up to loss parentage of 3. The available free were coding yolo is used to performing the task. Here we are using the lasted vision of YOLOv4. that improve the accuracy up to 97%. the increasing the iterations making the result best, here we use 3600 iterations for this project.