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.

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.

Plants have evolved intricate signaling pathways, which operate as networks governed by feedback to deal with stressors. Nevertheless, the sophisticated molecular mechanisms underlying these routes still need to be comprehended, and experimental validation poses significant challenges and expenses. Consequently, computational hypothesis evaluation gains prominence in understanding plant signaling dynamics. Biosensors are genetically modified to emit light when exposed to a particular hormone, such as abscisic acid (ABA), enabling quantification. We developed computational models to simulate the relationship between ABA concentrations and bioluminescent sensors utilizing the Hill equation and ordinary differential equations (ODEs), aiding better hypothesis development regarding plant signaling. Based on simulation results, the luminescence intensity was recorded for a concentration of 47.646 RLUs for 1.5 μmol, given the specified parameters and model assumptions. This method enhances our understanding of plant signaling pathways at the cellular level, offering significant benefits to the scientific community in a cost-effective manner. The alignment of these computational predictions with experimental results emphasizes the robustness of our approach, providing a cost-effective means to validate mathematical models empirically. The research intended to correlate the bioluminescence of biosensors with plant signaling and its mathematical models for quantified detection of specific plant hormone ABA.