Rida Batool

Plasma medicine and agriculture are rapidly advancing interdisciplinary fields that exploit the physicochemical properties of cold atmospheric plasma (CAP). As the fourth state of matter, plasma generates reactive oxygen and nitrogen species, UV photons, charged particles, and transient electric fields, offering antimicrobial, immunomodulatory, and growth-enhancing effects. In medicine, CAP supports wound healing, cancer therapy, sterilization, and control of multidrug-resistant pathogens with minimal damage to healthy tissues, including selective cancer cell apoptosis and improved angiogenesis. In agriculture, it enhances seed germination, plant growth, and pathogen control, while plasma-activated water boosts nutrient availability and suppresses microbes. It also aids food safety and sustainable fertilizer development. However, challenges in device standardization, optimization, long-term safety, regulation, and economic scalability remain for widespread adoption.

Industrialization has significantly increased the productivity of agriculture but industrial wastes have also increased the contamination of agricultural soils. This is an increasing threat to the food safety, the stability of ecosystems and human health. Certain industrial by-products can provide short-term benefits to soil fertility and crop yields but can also introduce hazardous contaminants to agroecosystems, including heavy metals, persistent organic pollutants (POPs), microplastics, per- and polyfluoroalkyl substances (PFAS), and engineered nanoparticles. This review critically synthesizes the global evidence of sources, transport pathways, environmental fate and transfer to the food chain of industrial contaminants in agricultural soils. It emphasizes the paradox of high crop yields in spite of the clandestine build-up of toxic substances that deteriorate soil health, microbial diversity, crop safety and the long term sustainability of agriculture. The major challenges are poor management of industrial waste, lack of monitoring systems, interactions among pollutants, weak regulatory frameworks and limited knowledge about emerging contaminants under changing climatic conditions. The review also includes integrated mitigation measures such as sustainable waste management, innovative soil remediation techniques, phytoremediation, biochar use, precision agriculture and other stronger environmental policies to reduce the accumulation of contaminants and human exposure. Future perspectives emphasize the need for high-throughput monitoring tools, remote sensing, biosensors, digital soil mapping, improved risk assessment frameworks, and interdisciplinary research to better understand contaminant dynamics and develop resilient soil management strategies. In summary, the review emphasizes that sustainable agriculture should be assessed not only by crop productivity but also by soil health, environmental integrity and food safety. In the context of an increasingly industrialized world it is crucial to take a holistic and integrated approach combining scientific innovation with efficient policy implementation and continuous environmental surveillance for the protection of agricultural ecosystems and sustainable food production.

This chapter addresses the worldwide issues of water shortage, climatic variability, and agricultural sustainability by examining the revolutionary role of artificial intelligence (AI) in contemporary irrigation systems. It highlights the integration of artificial intelligence (AI), the Internet of Things (IoT), and sensor-based infrastructures while tracing the development of irrigation from conventional techniques to cutting-edge precision technology. The chapter describes how AI-driven models

Increasing climatic variability and the intensifying biotic and abiotic stresses pose major challenges to global crop productivity, emphasising the urgent need for resource-efficient and sustainable agricultural strategies. Artificial intelligence (AI) and nanotechnology have emerged as complementary technologies capable of enhancing precision agriculture through early stress detection, predictive analytics, and targeted agro-input delivery. This review uniquely integrates recent advances in AI-driven predictive analytics and nano-enabled delivery systems within an agricultural framework of closed-loop precision. It highlights their synergistic role in improving plant stress tolerance and climate-resilient crop production. AI approaches to integrating machine learning, deep learning, and multimodal data fusion enable real-time stress monitoring, hotspot detection, and prediction of stress–risk windows for timely site-specific interventions. Concurrently, nano-engineered formulations enhance the efficiency of the use of nutrients, stabilise bioactive compounds, and enable controlled release mechanisms while reducing environmental losses. The convergence of these technologies supports adaptive crop management systems that integrate stress sensing, predictive decision-making, targeted delivery, and response verification. Despite significant progress, challenges remain regarding field-scale validation, nanoparticle environmental fate, AI model generalisation, and regulatory governance. Future priorities include biodegradable nano formulations, uncertainty-aware AI systems, standardised dosimetry, and adaptive multimodal AI platforms for diverse agroecosystems. Overall, the integration of AI and nanotechnology offers a scalable and sustainable pathway towards climate-resilient agriculture by improving yield stability, optimising resource usage, and minimising ecological impacts.