Artificial Intelligence Assisted Autonomous Unmanned Aerial Vehicles (UAVs) and Aerial drones based on Machine Vision for Enhancing Remote Sensing of Precision crop Health Monitoring

Abstract

With unmanned aerial vehicles (UAVs), agricultural monitoring has developed into a new phase of innovation providing remedies to precision farming. The common traditional agricultural methods are based on manual inspection and few observations on the ground using sensors that may be inaccurate and time-consuming.  New technologies such as drones and AI provide us with an opening of large scale, early detection, but most systems currently only seek pests or diseases and are usually specific to a single type of crop in controlled laboratory conditions. Drone-operated AI system, which combines RGB and, where feasible, multispectral cameras and a YOLOv8 pipeline to detect pests and crop diseases simultaneously across a variety of crops. We are developing it to be used in the real world: we load in data fields, laboratories, and the internet, perform preprocessing, transfer learning, and make the inference to be lightweight enough to execute on edge computers. The introduction of agricultural monitoring systems based on the use of UAVs builds on the peculiarities of quadcopters and fixed-wing UAVs. Quadcopters are used when conducting detailed field surveys or spot checks, allowing high-resolution imaging to be used in order to complete precise inspections, whereas fixed-wing UAVs are used when it comes to covering extensive areas and long-range capabilities. These UAVs can gather extensive data and conduct biological and chemical analyses due to sophisticated IoT devices and sensors, such as multispectral and hyperspectral cameras, GPS modules, and real-time communication tools. Our hybrid machine learning model (HMLM) has more accuracy and predictive capabilities, with an amazing score of 98.74 and hence, our machine learning model is doing the right job of 98.74 accurate classification and thereby yielding high accurate yields by predicting crop management. This research will contribute to the sustainability of agricultural practices as well as yield protection by providing timely, precise and scalable detection. The model proposed can potentially enable farmers with action-oriented insights, losses can be alleviated, and food security objectives can be achieved in areas where there are high susceptibility rates to pests and diseases.