Efficient and Cost-effective Drone – NDVI system for Precision Farming

Abstract

The motive of the project is to develop a under budget system that automates the process of farming by interfacing cutting edge technologies like Drones and ‘NDVI’ to improve the level of productivity in Agriculture. Humans and satellites have a hard time beating a drone’s eye for detail in scanning farming systems from above. Flying below the clouds, collecting and sending images in almost real-time, unmanned aerial vehicles (UAVs) gained ground quickly in agriculture in the last decade as part of so-called precision agriculture. Among their wide range of applications, they can help farmers check crops’ health, track livestock, plan fertilization, assess damages, and map fields at high-resolution. But all this comes with a cost. Currently the models of drones used for such applications cost extensively higher, which makes it unfeasible for the small farmers, especially in India. The projects aim on designing a drone system that can work on both autonomous as well as manual mode and perform mapping, inspecting and spraying processes with efficiency accuracy and considerably good speed which can help boosting the profits of the farmers with large as well as small agricultural lands. As there are lots of restrictions on Drone flight in India. The project aims to follow and implements all the norms stated by the government. (e.g., Permission before flight). The project aims towards overcoming all the above-mentioned problems by automating the procedure.

Construction of an Orthophoto-Draped 3D Model and Classification of Intertidal Habitats Using UAV Imagery in the Galapagos Archipelago

“…Moreover, human intervention, including drone take off and landing, data calibration, and verification, could also affect the autonomy of the technology [59]. Additionally, issues such as battery capacity, high cost, and insufficient computing power due to large data samples should also be fully considered in future research [60,61]. Future validation could be made via underwater monitoring based on camera systems on underwater vehicles, autonomous underwater vehicles (AUVs), or remotely operated underwater vehicles (ROV), and snorkeling [12,58].…”

Deployment of an autonomous fleet of UAVs for Assessing the NDVI of Regenerative Farming

“…A core farming process is the calculation of a Normalized Difference Vegetation Index (NDVI) which may be carried out by a fleet of autonomous UAVs synchronizing in flight over different zones and taking a sequence of high-resolution pictures to evaluate the health of trees and leaves [34][35][36]. The NDVI verifies and quantifies the presence of live green vegetation using a reflected light in the visible and near-infrared bands.…”

Deep Neural Network Solution for Detecting Intrusion in Network

“…However, the effectiveness of several deep learning-based systems is constrained by time complexity. [25] In this paper, we explore the capabilities and limitations of deep neural networks in detecting network intrusions. We present a comprehensive analysis of various DNN architectures, training methodologies, and evaluation metrics used in the field of intrusion detection.…”