Agriculture has always been a cornerstone of human civilization, providing food, raw materials, and employment. However, with the global population projected to reach nearly 10 billion by 2050, the demand for food production is expected to increase substantially (Godfray et al., 2010). Traditional farming methods are increasingly challenged by climate change, resource limitations, and environmental concerns. To address these challenges, upcoming technologies in agriculture promise to revolutionize farming practices by enhancing productivity, sustainability, and resilience. This article reviews key emerging technologies, including precision agriculture, artificial intelligence (AI), gene editing, drone and robotic systems, Internet of Things (IoT) sensors, and sustainable farming innovations. The integration of these technologies is expected to transform agriculture into a more efficient, data-driven, and environmentally friendly sector.

The prevalence of micropollutants is growing, necessitating the development of ecologically acceptable bioremediation techniques. Researchers are investigating the use of microbial enzymes as biocatalysts for novel environmental cleaning solutions, driven by their extraordinary diversity and powerful catalytic activity. Microbial enzymes are effective bioremediation agents because they are substrate-specific, biodegradable and can degrade a wide spectrum of contaminants. However, the stability and reusability of enzymes often restrict their practical utility. To address this issue, enzyme immobilization approaches have emerged as revolutionary tools. These methods immobilize enzymes on inert or active supports, increasing their stability and reusability. These developments result in more economical bioremediation procedures and more effective pollutant breakdown. This review examines immobilization techniques and their effectiveness in degradation of environmental pollutants, such as dyes and pesticides. It demonstrates the groundbreaking potential of microbially immobilized enzyme biocatalysts in reducing micropollutant pollution and achieving a cleaner, more sustainable future. The review also emphasizes how the unique properties of nanomaterials and the catalytic activity of enzymes can be combined to provide cutting-edge, environmentally friendly remediation solutions. It suggests that future technological developments in environmental cleanup may be facilitated by this research.

Wildfires are no longer seasonal threats; they are now persistent, transboundary health hazards. As landscape fire-sourced (LFS) air pollution intensifies across the globe, the scale of human exposure is both staggering and uneven. While recent global estimates suggest that over 1.5 million deaths annually may be attributed to wildfire-related air pollution (Xu et al., 2024). The actual burden likely extends far beyond mortality figures. This Perspective argues that the world must expand its lens to include the chronic, cumulative, and inequitable health impacts of LFS exposure, particularly in under-monitored and climate-vulnerable regions

Global pressures from urbanization, shrinking arable land, and climate volatility necessitate efficient food production methods. This paper presents an Intelligent Hydroponic System (IHS) designed for autonomy and resource efficiency, integrating edge-based TinyML for closed-loop environmental control with a computervision layer for periodic plant-health diagnostics. The system employs an ESP32 microcontroller to host a lightweight Multi-Layer Perceptron (MLP) model that processes real-time sensor data—pH, electrical conductivity/total dissolved solids (EC/TDS), temperature, and humidity—to automate nutrient dosing and LED lighting. A secondary diagnostic module uses an NVIDIA Jetson Nano running a YOLO-based object detection model for twice-daily visual health assessments. In an eight-week Nutrient Film Technique (NFT) trial with lettuce (Lactuca sativa) and mustard greens (Brassica juncea), the IHS maintained target nutrient parameters with 96.3% accuracy and achieved 18–21% higher fresh biomass compared to a conventional, manually managed hydroponic system. The system also demonstrated a 21% reduction in water consumption and improved nutrient-use efficiency from 72% to 87%. Automated LED and pump scheduling further reduced total energy consumption by 8%. With a core smart component cost of approximately RM 244.25, the IHS offers a scalable, low-cost, and connectivity-resilient solution for precision agriculture in urban and resource-limited settings. These results establish a compelling case for decentralized, edge-native intelligence as a practical alternative to cloud-dependent smart farming platforms.

Onion is one of the most economically significant horticultural crops, widely consumed and traded globally. However, post-harvest losses in onion storage remain a major challenge, particularly in developing countries such as India, where traditional storage structures often result in 20–40% losses due to sprouting, rotting, microbial infection, and excessive moisture accumulation. These losses not only reduce farmer income but also disturb national market stability. In recent years, the integration of advanced technologies—especially the Internet of Things (IoT) and sensor-based monitoring systems—has emerged as a promising solution for improving onion storage efficiency. This review paper provides a comprehensive analysis of the current state, technological advancements, and future potential of IoT-enabled smart onion storage systems. Within this paper, principal storage conditions such as temperature, relative humidity, airflow, gas accumulation, and weight loss are examined for their critical role in maintaining onion quality and maximizing shelf life. Various sensor technologies, including temperature, humidity, CO₂, ethylene, airflow, and load sensors, are evaluated for their relevance in onion storage environments

Measuring thermal conductivity of a jute-based product is an important aspect to choose the best product to fit for right purpose. For this reason, it is important to measure thermal conductivity with accuracy. In the existing thermal conductivity machine of Physics Department, temperature is measured with analog thermometer. Reading analog thermometer is not precise and accurate enough. Therefore, it is important to measure temperature reading more accurately, that means digitally. Temperature sensor DB18B20, Arduino Nano and a display unit were used to show the temperature digitally. Thermal conductivity of PP, Jute-PP composite, Jute-Polyester composite, and Jute-Wool composite samples was measured both conventionally and digitally. For both conventional and digital approaches, the thermal conductivity values of the samples mentioned above differed by 0.04%, 2.84%, 6.01%, and 5.54%. Considering the heating rate, there is an observable trend: a decrease in specimen width correlates with an increase in heating rate. Additionally, specimens with the same width show varying heating rates depending on the material composition.