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Anthropogenic ammonia (NH3) emissions are primarily derived from agricultural activities. NH3 emissions from synthetic nitrogen (N) fertilizers are associated with the annual consumption of various N fertilizers. In this study, the annual consumption of different types of synthetic N fertilizers in the EU countries, derived from the database of the International Fertilizer Association, was analyzed, and the implementation of mitigation measures for NH3 emissions at the national level was assessed. NH3 emissions were estimated using the emission factors of different N fertilizers in cool, temperate and hot climates, and low- and high-pH soils, as the European Environment Agency proposed. The results revealed that the annual consumption of various synthetic N fertilizers (average values from 2016 to 2020) varied markedly in different EU countries. France, Germany, Poland, Spain, and the United Kingdom had the largest annual consumption of synthetic N fertilizers. NH3 emissions from urea-based fertilizers are the highest in most EU countries. The annual NH3 emissions were associated with the type of synthetic N fertilizer consumed. For example, although the annual synthetic N fertilizer consumption in Germany was 38% and 41.5% higher than those in Spain and Italy, the annual NH3 emissions in Germany were 63% and 20% lower than those in Spain and Italy, respectively, due to the high proportion of urea-based fertilizers with the highest emission factor among the synthetic N fertilizers in Spain and Italy. Thus, mitigation strategies should be tailored to specific types of N fertilizers with high emission factors and proportions to minimize NH3 emissions. Since the NH3 emissions from urea-based fertilizers are the highest in the EU countries, especially in the top five countries, we recommend the addition of urease inhibitors to urea fertilizers as one important strategy for mitigating NH3 emissions.

In recent years, computer vision (CV) has made enormous progress and is providing great possibilities in analyzing images for object detection, especially with the application of machine learning (ML). Unmanned Aerial Vehicle (UAV) based high-resolution images allow to apply CV and ML methods for the detection of plants or their organs of interest. Thus, this study presents a practical workflow based on the You Only Look Once version 5 (YOLOv5) and UAV images to detect maize plants for counting their numbers in contrasting development stages, including the application of a semi-auto-labeling method based on the Segment Anything Model (SAM) to reduce the burden of labeling. Results showed that the trained model achieved a mean average precision (mAP@0.5) of 0.828 and 0.863 for the 3-leaf stage and 7-leaf stage, respectively. YOLOv5 achieved the best performance under the conditions of overgrown weeds, leaf occlusion, and blurry images, suggesting that YOLOv5 plays a practical role in obtaining excellent performance under realistic field conditions. Furthermore, introducing image-rotation augmentation and low noise weight enhanced model accuracy, with an increase of 0.024 and 0.016 mAP@0.5, respectively, compared to the original model of the 3-leaf stage. This work provides a practical reference for applying lightweight ML and deep learning methods to UAV images for automated object detection and characterization of plant growth under realistic environments.

Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health across multiple spectral domains. However, this technology’s precision in estimating plant traits associated with Nitrogen Use Efficiency (NUE), and the factors affecting this precision, are not well-documented. This review examines the capabilities of UASs in assessing NUE in crops. Our analysis specifically highlights how different growth stages critically influence NUE and biomass assessments in crops and reveals a significant impact of specific signal processing techniques and sensor types on the accuracy of remote sensing data. Optimized flight parameters and precise sensor calibration are underscored as key for ensuring the reliability and validity of collected data. Additionally, the review delves into how different canopy structures, like planophile and erect leaf orientations, uniquely influence spectral data interpretation. The study also recognizes the untapped potential of image texture features in UAV-based remote sensing for detailed analysis of canopy micro-architecture. Overall, this research not only underscores the transformative impact of UAS technology on agricultural productivity and sustainability but also demonstrates its potential in providing more accurate and comprehensive insights for effective crop health and nutrient management strategies.