Agricultural non-point source pollution (ANPSP) significantly affects worldwide water quality, soil integrity, and ecosystems. Primary factors are nutrient runoff, pesticide leaching, and inadequate livestock waste management. Nonetheless, a thorough assessment of ANPSP sources and efficient control techniques is still lacking. This research delineates the origins and present state of ANPSP, emphasizing its influence on agricultural practices, livestock, and rural waste management. It assesses current evaluation models, encompassing field- and watershed-scale methodologies, and investigates novel technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) that possess the potential to enhance pollution monitoring and predictive precision. The research examines strategies designed to alleviate ANPSP, such as sustainable agricultural practices, fertilizer reduction, and waste management technology, highlighting the necessity for integrated, real-time monitoring systems. This report presents a comprehensive analysis of current tactics, finds significant gaps, and offers recommendations for enhancing both research and policy initiatives to tackle ANPSP and foster sustainable farming practices.

Dielectric properties are crucial in understanding the behavior of water within soil, particularly the soil water content (SWC), as they measure a material’s ability to store an electric charge and are influenced by water and other minerals in the soil. However, a comprehensive review paper is needed that synthesizes the latest developments in this field, identifies the key challenges and limitations, and outlines future research directions. In addition, various factors, such as soil salinity, temperature, texture, probing space, installation gap, density, clay content, sampling volume, and environmental factors, influence the measurement of the dielectric permittivity of the soil. Therefore, this review aims to address the research gap by critically analyzing the current state-of-the-art dielectric properties-based methods for SWC measurements. The motivation for this review is the increasing importance of precise SWC data for various applications such as agriculture, environmental monitoring, and hydrological studies. We examine time domain reflectometry (TDR), frequency domain reflectometry (FDR), ground-penetrating radar (GPR), remote sensing (RS), and capacitance, which are accurate and cost-effective, enabling real-time water resource management and soil health understanding through measuring the travel time of electromagnetic waves in soil and the reflection coefficient of these waves. SWC can be estimated using various approaches, such as TDR, FDR, GPR, and microwave-based techniques. These methods are made possible by increasing the dielectric permittivity and loss factor with SWC. The available dielectric properties are further synthesized on the basis of mathematical models relating apparent permittivity to water content, providing an updated understanding of their development, applications, and monitoring. It also analyzes recent mathematical calibration models, applications, algorithms, challenges, and trends in dielectric permittivity methods for estimating SWC. By consolidating recent advances and highlighting the remaining challenges, this review article aims to guide researchers and practitioners toward more effective strategies for SWC measurements.

Soil nitrate nitrogen (NO₃⁻-N) is a key indicator of agricultural non-point source pollution. The ultraviolet (UV) dual-wavelength method is widely used for NO₃⁻-N detection, but interference from complex soil organic matter affects its accuracy. This study investigated how organic matter influences NO₃⁻-N detection by optimizing UV dual-wavelength combinations. Density functional theory (DFT) calculations showed slight spectral broadening of fulvic and humic acids in the presence of NO₃⁻-N under UV spectrum. Standard solutions and soil samples were used to compare the detection performance of different wavelength pairs. The findings indicated that the dual-wavelength combination of 235 nm/275 nm is optimal rather than 220 nm/275 nm for measuring soil samples at NO₃⁻-N concentrations exceeding 5 mg·L⁻¹. The 235/275 nm method gave an average calibration coefficient of 1.57. Compared to the national standard and flow analysis methods, the average relative errors were 19.7% and 22.3% (p < 0.001), respectively, indicating its suitability for practical soil applications. These results demonstrate the method’s potential for rapid and accurate NO₃⁻-N detection in real soil samples, supporting its application in environmental monitoring and agricultural management.

The plant hormone abscisic acid (ABA) plays an important role in crop growth and development, so it is urgent to establish a simple and sensitive method for the detection of ABA. (1) As one of the most sensitive spectral detection methods, surface-enhanced Raman spectroscopy (SERS) has made some progress in the detection of ABA, but it involved a complicated modification process of noble metal nanoparticles and was time-consuming. (2) In this work, a SERS and (local surface plasmon resonance) LSPR dual-signal aptamer (Apt) sensor based on the aggregation of dispersed (gold nanoparticles) AuNPs and the improved plasmonic coupling with formed SERS was developed and applied to the detection of the plant hormone ABA. Through the specific recognition of Apt and ABA, the prepared crystal violet (CV) and Apt modified AuNPs tended to aggregate in a high concentration salt solution, resulting in changes in LSPR characteristics of the detection system and enhanced SERS intensity of CV signal molecules. Thus, the quantitative relationship between ABA concentration and SERS intensity of signal molecule CV and the degree of absorbance change of AuNPs were established. (3) The linear range detection of SERS was 0.04~40 µM, the detection limit lod (LOD) was 17.6 nM, the linear range detection of LSPR was 0.4~80 µM, and the LOD was 36 nM. (4) The sensor has a good ability to detect ABA in the samples of common plants such as cucumber and tomato and has the characteristics of no chemical bond modification, more reliable detection results, and a universal detection platform.