Machine Learning in UAV-Assisted Smart Farming

Abstract

Over the years, economic loss in the agricultural sector has been attributed to the late detection of varying plant diseases due to incongruent detection technologies. With the advent of disruptive technologies and their deployment, such as incorporating artificial intelligence (AI) models into unmanned autonomous vehicles for real-time monitoring, the curtailment of losses and inadvertent waste of agricultural produce can be significantly addressed. This study examines the role of deploying AI models in improving the early and accurate detection of crop lesions for prompt, intuitive, and decisive action to forestall recurrence and guarantee a return on investment to farmers. Furthermore, the chapter established scientific basis for the acceleration of crop yields through allied mechano-biosynthesis in a quest to cushion the effect of the contemporary global food crisis. Connected intelligence in smart farming can be achieved through convergence technology for cost effective agro-allied production by improving the limitations of UAVs and AI-models.