Smart Farming: Drone-Based Crop Health Monitoring

Abstract

Smart farming has become a paradigm shift within the agricultural sphere of contemporary society by using high-tech sensing, communication, and data analytics solutions to improve productivity-sustainability and decision-making. One of such technologies is unmanned aerial vehicles (UAVs also known as drones) which have received so much popularity in the field of crop health monitoring because it can capture high-resolution and real-time data on large pieces of Agriculture. This paper has provided an analytic research on the drone-based crop health monitoring systems in terms of their architecture, sensing modalities, data processing methods and the performance evaluation. Multispectral and hyperspectral imaging sensor systems introduced with the UAV systems allow accurate determination of the state of crops in terms of their vigor, herbal deficiencies, water stress, and disease epidemiology. The interpretation of aerial data is also improved with the help of machine learning and deep learning algorithms that allow automatizing the process of organizing crops according to their condition and predicting them. The paper examines the literature that is available, some of the gaps in research, and finally suggests an orderly approach to the implementation of a drone-based crop health monitoring system. The outcomes of experimental study based on simulated and real field experiments indicate the greater accuracy of assessment of vegetation health when compared to the conventional terrestrial techniques. The results suggest that drone-based monitoring can help to significantly decrease the costs of labor force, enhance the accuracy of yield forecasting, and facilitate agricultural intervention. The paper concludes by explaining the constraints in regulatory, data management, and scalability and discusses future research directions based on the autonomous and intelligent smart farming ecosystems.