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.

In view of the increasing demand for food due to population, high cost and scarcity of inorganic fertilizer due to government deregulation policy, and unavailability of high yielding crop varieties as planting materials; total reliance on inorganic fertilizer or organic materials alone as fertilizer may not be realistic. Complementary use of organic with inorganic fertilizer should be employed so as to sustain soil fertility management strategy for crop production. It is clear that the prospect of obtaining enough chemical fertilizer to meet the requirement of the teaming farming population in the tropic is remote. The current price of fertilizer calls for its economic utilization to meet specific requirements of crops. The current world-wide shortage of fertilizer and its anticipated adverse effect on food production has made many countries to explore the manorial value of organic manure to reduce pressure on the demand for mineral fertilizer as complementary use. This chapter is expected to discuss the use of inorganic fertilizer in combination with organic materials to be able to give the desired higher and sustainable crop yields than the sole use of inorganic fertilizer or animal manure. It is concluded that there are abundant organic wastes that could be used alone or combined with mineral fertilizers. The presence of organic manures in organomineral fertilizers ensures more residual effect, balanced nutrition and improvement in soil physicochemical properties. The use of the two sources has synergistic effect and reduces expenditure on scarce and expensive mineral fertilizers. It is a sustainable approach to ensuring high soil productivity and crop yield.

Foodborne disease presents a substantial challenge to researchers, as foliar water intake greatly influences pathogen internalization via stomata. Comprehending plant–pathogen interactions, especially under fluctuating humidity and temperature circumstances, is crucial for formulating ways to prevent pathogen ingress and diminish foodborne hazards. This study introduces a computational model utilizing neural networks to anticipate pathogen internalization via stomata, contrasting with previous research that emphasized biocontrol techniques. Computational modeling assesses the likelihood and duration of internalization for bacterial pathogens such as Salmonella enterica (S. enterica), considering various environmental factors including humidity and temperature. The estimated likelihood ranges from 0.6200 to 0.8820, while the internalization time varies from 4000 s to 5080 s, assessed at 50% and 100% humidity levels. The difference in internalization time, roughly 1042.73 s shorter at 100% humidity, correlates with a 26.2% increase in the likelihood of internalization, rising from 0.6200 to 0.8820. A neural network model has been developed to quantitatively predict these values, thereby enhancing the understanding of plant–microbe interactions. These methods will aid researchers in understanding plant–pathogen interactions, especially in environments characterized by varying humidity and temperature and are essential for formulating strategies to prevent pathogen ingress and tackle foodborne illnesses within a technologically advanced context.

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.