Plant stress is a significant challenge that affects the development, growth, and productivity of plants and causes an adverse environmental condition that disrupts normal physiological processes and hampers plant survival. Epigenetic regulation is a crucial mechanism for plants to respond and adapt to stress. Several studies have investigated the role of DNA methylation (DM), non-coding RNAs, and histone modifications in plant stress responses. However, there are various limitations or challenges in translating the research findings into practical applications. Hence, this review delves into the recent recovery, implications, and applications of epigenetic regulation in response to plant stress. To better understand plant epigenetic regulation under stress, we reviewed recent studies published in the last 5–10 years that made significant contributions, and we analyzed the novel techniques and technologies that have advanced the field, such as next-generation sequencing and genome-wide profiling of epigenetic modifications. We emphasized the breakthrough findings that have uncovered specific genes or pathways and the potential implications of understanding plant epigenetic regulation in response to stress for agriculture, crop improvement, and environmental sustainability. Finally, we concluded that plant epigenetic regulation in response to stress holds immense significance in agriculture, and understanding its mechanisms in stress tolerance can revolutionize crop breeding and genetic engineering strategies, leading to the evolution of stress-tolerant crops and ensuring sustainable food production in the face of climate change and other environmental challenges. Future research in this field will continue to unveil the intricacies of epigenetic regulation and its potential applications in crop improvement.

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.

Nitrogen is one of the most important nutrient indicators for the growth of crops, and is closely related to the chlorophyll content of leaves and thus influences the photosynthetic ability of the crops. In this study, five hybrid rice varieties were cultivated during one entire growing period in one experimental field supplied with six nitrogen fertilizer levels. Visible and near infrared (vis/NIR) reflectance spectroscopy combined with multivariate analysis was used to identify hybrid rice varieties and nitrogen fertilizer levels, as well as to detect chlorophyll content associated with nitrogen levels. The support vector machine (SVM) algorithm was applied to identify five varieties of hybrid rice and six levels of nitrogen fertilizer. The results demonstrated that different varieties of hybrid rice for each nitrogen level can be well distinguished except for the highest nitrogen level, and no nitrogen level for each rice variety can be completely identified from the other five nitrogen levels. Further, 12 spectral indices combined with partial least square (PLS) analysis were applied for estimating chlorophyll content of rice leaves from plants subjected to different nitrogen levels, and a root mean square error of cross-validation (RMSECV) of 0.506, a coefficient of determination ( R ² ) of 97.8% and a ratio of performance to deviation (RPD) of 4.6 for all rice varieties indicated this as a preferable procedure. This study demonstrates that Vis/NIR spectroscopy can have a great potential for identification of rice varieties and evaluation of nitrogen fertilizer levels.