UAV Data Collection and Analyzing Model Using Machine Learning-Based Precedence Ordered Data Gathering Technique

Climate change has created significant problems, including agriculture, industries, which could impact many fields. Agricultural technology is rapidly changing as the sector tackles the two issues of food safety and climate changes. To maximize farming outcomes and minimize losses, it is vital to collect credible and accurate data from crops. Unmanned aerial vehicles (UAVs) provide a quick and ever more cost-effective way of data collection and provide a better, unconstrained view of each crop as compared to conventional satellite images. The autonomous sensors collect high- resolution images challenging for decision making with increasing speed and efficiency and cost savings. There are not yet standard workflow and processes for most UAV applications for precision agriculture. This paper proposed a Machine Learning-based Precedence ordered Data Gathering (MLbPODG) Model used for gathering the data from the UAV devices that is helpful in precision agriculture. The proposed model is compared with the traditional models, and the results show that the proposed model performance in data collection and analysis is better.