Advancing food security through drone-based hyperspectral imaging: applications in precision agriculture and post-harvest management

Ensuring global food security in the face of growing population, climate change, and resource limitations is a critical challenge. Hyperspectral imaging (HSI), particularly when combined with drone technology, offers innovative solutions to enhance agricultural productivity and food quality by providing detailed, real-time data on crop health, disease detection, water and nutrient management, and post-harvest quality control. This review highlights the applications of drone-based HSI in precision agriculture, where it enables early detection of crop stress, accurate yield prediction, and soil health assessment. In post-harvest management, HSI is utilized to monitor food freshness and ripeness and detect potential contaminants, improving food safety and reducing waste. While the benefits of HSI are significant, challenges such as managing large volumes of data, translating spectral information into actionable insights, and ensuring cost-effective access for smallholder farmers remain barriers to its widespread adoption. Looking forward, future directions include advancements in miniaturized sensors, integration with Internet of Things (IoT) devices and satellite data for comprehensive agricultural monitoring, and expanding HSI applications to precision animal sciences. Collaboration among researchers, policymakers, and industry will be crucial to scaling the impact of HSI on global food systems, ensuring sustainable and equitable access to technology.

Cold atmospheric plasma (CAP): a revolutionary approach in dermatology and skincare

Cold atmospheric plasma (CAP) technology has emerged as a revolutionary therapeutic technology in dermatology, recognized for its safety, effectiveness, and minimal side effects. CAP demonstrates substantial antimicrobial properties against bacteria, viruses, and fungi, promotes tissue proliferation and wound healing, and inhibits the growth and migration of tumor cells. This paper explores the versatile applications of CAP in dermatology, skin health, and skincare. It provides an in-depth analysis of plasma technology, medical plasma applications, and CAP. The review covers the classification of CAP, its direct and indirect applications, and the penetration and mechanisms of action of its active components in the skin. Briefly introduce CAP's suppressive effects on microbial infections, detailing its impact on infectious skin diseases and its specific effects on bacteria, fungi, viruses, and parasites. It also highlights CAP's role in promoting tissue proliferation and wound healing and its effectiveness in treating inflammatory skin diseases such as psoriasis, atopic dermatitis, and vitiligo. Additionally, the review examines CAP's potential in suppressing tumor cell proliferation and migration and its applications in cosmetic and skincare treatments. The therapeutic potential of CAP in treating immune-mediated skin diseases is also discussed. CAP presents significant promise as a dermatological treatment, offering a safe and effective approach for various skin conditions. Its ability to operate at room temperature and its broad spectrum of applications make it a valuable tool in dermatology. Finally, introduce further research is required to fully elucidate its mechanisms, optimize its use, and expand its clinical applications.

Bayesian Optimization of Low-Temperature Nonthermal Plasma Jet Sintering of Nanoinks

In response to the escalating demand for flexible devices in applications such as wearables, sensors, and touch panels, there is a need for innovative fabrication approaches for devices made from nanomaterial-based inks. Subsequent to ink deposition, a pivotal stage in device manufacturing typically involves high-temperature sintering, posing challenges for heat-sensitive substrates. Nonthermal plasma jet sintering utilizing an atmospheric pressure dielectric barrier discharge (DBD) plasma jet enables sintering at room temperature and standard pressure, facilitating the sintering of printed nanoparticle films without compromising substrate or film surface integrity. However, determining optimal plasma jet sintering conditions can be challenging due to multiple processing variables with intricate interrelationships. This work employed Bayesian optimization (BO) and machine learning (ML) to identify optimal values for seven primary plasma jet sintering variables. Optimization yielded a 99.2% increase in the measured electrical conductivity for plasma jet-sintered indium tin oxide (ITO) films after five rounds of experiments. Moreover, the optimal sintering conditions achieved an electrical conductivity that was 81.4% of conventional furnace sintering at 300 °C, but was three times faster and with a peak substrate temperature below 47 °C. This result demonstrates the prospect of applying BO to optimize processing techniques for emerging low-temperature requirements.

Plasma Control: A Review of Developments and Applications of Plasma Medicine Control Mechanisms

Cold atmospheric plasmas (CAPs) within recent years have shown great promise in the field of plasma medicine, encompassing a variety of treatments from wound healing to the treatment of cancerous tumors. For each subsequent treatment, a different application of CAPs has been postulated and attempted to best treat the target for the most effective results. These treatments have varied through the implementation of control parameters such as applied settings, electrode geometries, gas flow, and the duration of the treatment. However, with such an extensive number of variables to consider, scientists and engineers have sought a means to accurately control CAPs for the best-desired effects in medical applications. This paper seeks to investigate and characterize the historical precedent for the use of plasma control mechanisms within the field of plasma medicine. Current control strategies, plasma parameters, and control schemes will be extrapolated through recent developments and successes to gain better insight into the future of the field and the challenges that are still present in the overall implementation of such devices. Proposed approaches, such as data-driven machine learning, and the use of closed-loop feedback controls, will be showcased as the next steps toward application.

Machine learning-aided evaluation of oxidative strength of cold atmospheric plasma-treated water

Plasma medicine is gaining attraction in the medical field, particularly the use of cold atmospheric plasma (CAP) in biomedicine. The chemistry of the plasma is complex, and the reactive oxygen species (ROS) within it are the basis for the biological effect of CAP on the target. Understanding how the oxidative power of ROS responds to diverse plasma parameters is vital for standardizing the effective application of CAP. The proven applicability of machine learning (ML) in the field of medicine is encouraging, as it can also be applied in the field of plasma medicine to correlate the oxidative strength of plasma-treated water (PTW) according to different parameters. In this study, plasma-treated water was mixed with potassium iodide-starch reagent for color formation that could be linked to the oxidative capacity of PTW. Corresponding images were captured resulting from the exposure of the color-forming agent to water treated with plasma for different time points. Several ML models were trained to distinguish the color changes sourced by the oxidative strength of ROS. The AdaBoost Classifier (ABC) algorithm demonstrated better performance among the classification models used by extracting color-based features from the images. Our results, with a test accuracy of 63.5%, might carry a potential for future standardization in the field of plasma medicine with an automated system that can be created to interpret the oxidative properties of ROS in different plasma treatment parameters via ML.

Recognizing Cold Atmospheric Plasma Plume Using Computer Vision

Over the last three decades, cold atmospheric plasma (CAP) has been heavily investigated in a wide range of biological applications, including wound healing, microorganism sterilization, and cancer treatment. Atmospheric pressure plasma jets (APPJs) are the most common plasma sources in plasma medicine. An APPJ’s size determines its application range and approach in treatment. In this study, we demonstrated the real-time recognition of an APPJ’s plasma plume output using computer vision (CV), dramatically improving the measurement speed compared to the traditional method of using the naked eye. Our work provides a framework to monitor an aspect of an APPJ’s performance in real time, which is a necessary step to achieving an intelligent CAP source.