Sustainable Farming through Precision Agriculture: Enhancing Nitrogen Use and Weed Management

Abstract

The integration of digital tools to agriculture became more important than ever because of food security concerns and climate change. Real-time soil and crop monitoring systems, such as field sensors, spectral cameras, decision-making platforms and autonomous robots have significant potential to determine anomalies and optimize crop management practices. For example, variable rate application methods consist of reliable vegetation cover maps, however, do not contain information about the underlying causes of variation. Thus, the benefits of precision management remain a subject of debate limiting the adoption of such technology by farmers. In this review, we discuss the underlying causes of lower success rates of variable rate application and the developing of new digital platforms which will improve the efficiency of digital farming tools to manage nitrogen. Furthermore, image-based weed detection (key milestone for digitalized weed management) that employs sophisticated algorithms and machine learning techniques to analyze images captured by drones or ground-based cameras to identify weed species, density, and its growth stages, enabling targeted weed control will be discussed. Adoption of upcoming digital tools not only contributes to a significant technological leap in agriculture, but we believe also be the most important drivers of sustainable agriculture.