The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

Mapping soil trace metal distribution using remote sensing and multivariate analysis

Trace metal soil contamination poses significant risks to human health and ecosystems, necessitating thorough investigation and management strategies. Researchers have increasingly utilized advanced techniques like remote sensing (RS), geographic information systems (GIS), geostatistical analysis, and multivariate analysis to address this issue. RS tools play a crucial role in collecting spectral data aiding in the analysis of trace metal distribution in soil. Spectroscopy offers an effective understanding of environmental contamination by analyzing trace metal distribution in soil. The spatial distribution of trace metals in soil has been a key focus of these studies, with factors influencing this distribution identified as soil type, pH levels, organic matter content, land use patterns, and concentrations of trace metals. While progress has been made, further research is needed to fully recognize the potential of integrated geospatial imaging spectroscopy and multivariate statistical analysis for assessing trace metal distribution in soils. Future directions include mapping multivariate results in GIS, identifying specific anthropogenic sources, analyzing temporal trends, and exploring alternative multivariate analysis tools. In conclusion, this review highlights the significance of integrated GIS and multivariate analysis in addressing trace metal contamination in soils, advocating for continued research to enhance assessment and management strategies.

Insights into the Potential of Biopolymeric Aerogels as an Advanced Soil-Fertilizer Delivery Systems

Soil fertilizers have the potential to significantly increase crop yields and improve plant health by providing essential nutrients to the soil. The use of fertilizers can also help to improve soil structure and fertility, leading to more resilient and sustainable agricultural systems. However, overuse or improper use of fertilizers can lead to soil degradation, which can reduce soil fertility, decrease crop yields, and damage ecosystems. Thus, several attempts have been made to overcome the issues related to the drawbacks of fertilizers, including the development of an advanced fertilizer delivery system. Biopolymer aerogels show promise as an innovative solution to improve the efficiency and effectiveness of soil-fertilizer delivery systems. Further research and development in this area could lead to the widespread adoption of biopolymer aerogels in agriculture, promoting sustainable farming practices and helping to address global food-security challenges. This review discusses for the first time the potential of biopolymer-based aerogels in soil-fertilizer delivery, going through the types of soil fertilizer and the advert health and environmental effects of overuse or misuse of soil fertilizers. Different types of biopolymer-based aerogels were discussed in terms of their potential in fertilizer delivery and, finally, the review addresses the challenges and future directions of biopolymer aerogels in soil-fertilizer delivery.

IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management

The Internet of Things (IoT) has the potential to revolutionize agriculture by providing real-time data on crop and livestock conditions. This study aims to evaluate the performance scalability of wireless sensor networks (WSNs) in agriculture, specifically in two scenarios: monitoring olive tree farms and stables for horse training. The study proposes a new classification approach of IoT in agriculture based on several factors and introduces performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The study utilizes COOJA, a realistic WSN simulator, to model and simulate the performance of the 6LowPAN and Routing protocol for low-power and lossy networks (RPL) in the two farming scenarios. The simulation settings for both fixed and mobile nodes are shared, with the main difference being node mobility. The study characterizes different aspects of the performance requirements in the two farming scenarios by comparing the average power consumption, radio duty cycle, and sensor network graph connectivity degrees. A new approach is proposed to model and simulate moving animals within the COOJA simulator, adopting the random waypoint model (RWP) to represent horse movements. The results show the advantages of using the RPL protocol for routing in mobile and fixed sensor networks, which supports dynamic topologies and improves the overall network performance. The proposed framework is experimentally validated and tested through simulation, demonstrating the suitability of the proposed framework for both fixed and mobile scenarios, providing efficient communication performance and low latency. The results have several practical implications for precision agriculture by providing an efficient monitoring and management solution for agricultural and livestock farms. Overall, this study provides a comprehensive evaluation of the performance scalability of WSNs in the agriculture sector, offering a new classification approach and performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The results demonstrate the suitability of the proposed framework for precision agriculture, providing efficient communication performance and low latency.

Achieving the nutrient reduction objective of the Farm to Fork Strategy. An assessment of CAP subsidies for precision fertilization and sustainable agricultural practices in Germany

The Farm to Fork Strategy of the EU aims at sustainable food systems. One objective of the Strategy is to reduce nutrient losses by at least 50% resulting in at least 20% less fertilizer use by 2030. To this end, Member States are expected to extend digital precision fertilization and sustainable agricultural practices through the Common Agricultural Policy. In this context, this article applies a qualitative governance analysis which aims to assess the extent to which the measures proposed by the Farm to Fork Strategy, i.e., digital precision fertilization and sustainable agricultural practices, contribute to the nutrient objective of the Farm to Fork Strategy. The article analyses how these measures are implemented through the Common Agricultural Policy in Germany and Saxony. Results show that the nutrient objective of the Farm to Fork Strategy itself offers shortcomings. Germany offers some, yet overall limited, support for sustainable agricultural practices and digital precision fertilization. Hence, the Common Agricultural Policy will to a limited extend only contribute to the objective of the Strategy. The results furthermore highlight some general shortcomings of digitalization as sustainability strategy in the agricultural sector including typical governance issues (rebound and enforcement problems), and point to the advantages of quantity-based policy instruments.

Dimensionality reduction strategies for land use land cover classification based on airborne hyperspectral imagery: a survey

Hyperspectral image (HSI) contains hundreds of adjacent spectral bands, which can effectively differentiate the region of interest. Nevertheless, many irrelevant and highly correlated spectral bands lead to the Hughes phenomenon. Consequently, hyperspectral image dimensionality reduction is necessary to select the most informative and significant spectral band and eliminate the redundant spectral band. To this end, this paper represents an extensive and systematic survey of hyperspectral dimensionality reduction approaches for land use land cover (LULC) classification. Moreover, this paper reviewed the following important points: (1) hyperspectral imaging data acquisition methods, (2) the difference between hyperspectral and multispectral images, (3) hyperspectral image dimensionality reduction based on machine learning (ML) and deep learning (DL) techniques, (4) the popular benchmark hyperspectral datasets with the performance metrics for LULC classification, and (5) the significant challenges with the future trends for hyperspectral dimensionality reduction.

The Comparison Analysis of Uniform-and Variable-Rate Fertilizations on Winter Wheat Yield Parameters Using Site-Specific Seeding

Wheat is among the world’s most important agricultural crops, with winter wheat accounting for approximately 25.5% of the total agricultural crop in Lithuania. The unchangeable goal of crop production is to achieve good and economically beneficial crop yield, but such efforts are often based on conventional agrotechnological solutions, and excessive fertilization, which is uneconomical and negatively affects the soil, the environment, and human health. In order to produce a rich and high-quality cereal crop, scientists and farmers are increasingly focusing on managing the sowing and fertilization processes. Precision technologies based on spectrometric methods of soil and plant characterization can be used to influence the optimization of sowing and fertilizer application rates without compromising crop yield and quality. The aim of this study was to investigate the effect of site-specific seeding and variable-rate precision fertilization technologies on the growth, yield, and quality indicators of winter wheat. Experimental studies were carried out on a 22.4 ha field in two treatments: first (control)—SSS (site-specific seeding) + URF (uniform-rate fertilization); second—SSS + VRF (variable-rate precision fertilization) and 4 repetitions. Before the start of this study, the variability of the soil apparent electrical conductivity (ECa) was determined and the field was divided into five soil fertility zones (FZ-1, FZ-2, FZ-3, FZ-4, and FZ-5). Digital maps of potassium and phosphorus precision fertilization were created based on the soil samples. Optical nitrogen sensors were used for variable-rate supplementary nitrogen fertilization. The variable-rate precision fertilization method in individual soil fertility zones showed a higher (up to 6.74%) tillering coefficient, (up to 14.55%) grain yield, number of ears per square meter (up to 27.6%), grain number in the ear (up to 6.2%), and grain protein content (up to 12.56%), and a lower (up to 8.61%) 1000-grain weight on average than the conventional flat-rate fertilization. In addition, the use of the SSS + VRF method saved approximately 14 kg N ha−1 of fertilizer compared to the conventional SSS + URF method.