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1. The 3D Printing of Nanocomposites for Wearable Biosensors: Recent Advances, Challenges, and Prospects

   https://doi.org/10.3390/bioengineering11010032  

   Parupelli, Santosh Kumar; Desai, Salil (January 2024, Bioengineering)

   Notably, 3D-printed flexible and wearable biosensors have immense potential to interact with the human body noninvasively for the real-time and continuous health monitoring of physiological parameters. This paper comprehensively reviews the progress in 3D-printed wearable biosensors. The review also explores the incorporation of nanocomposites in 3D printing for biosensors. A detailed analysis of various 3D printing processes for fabricating wearable biosensors is reported. Besides this, recent advances in various 3D-printed wearable biosensors platforms such as sweat sensors, glucose sensors, electrocardiography sensors, electroencephalography sensors, tactile sensors, wearable oximeters, tattoo sensors, and respiratory sensors are discussed. Furthermore, the challenges and prospects associated with 3D-printed wearable biosensors are presented. This review is an invaluable resource for engineers, researchers, and healthcare clinicians, providing insights into the advancements and capabilities of 3D printing in the wearable biosensor domain. 

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2. Using Machine Learning to Integrate On-Farm Sensors and Agro-Meteorology Networks into Site-Specific Decision Support

   https://doi.org/10.13031/trans.13917  

   Kelley, Jason; McCauley, Dalyn; Alexander, G. Aaron; Gray, Wilton F.; Siegfried, Rylie; Oldroyd, Holly J. (January 2020, Transactions of the ASABE)

   null (Ed.)

   Highlights Machine learning can incorporate a variety of data from low-cost sensors and estimate actual ET by comparison with short-term, higher-cost measurements. On-farm weather monitoring can be leveraged to estimate site-specific crop-water requirements. Expanding spatial coverage of weather and actual ET through on-farm monitoring will facilitate localization and leverage publicly available weather data to guide irrigation decisions and improve irrigation water management. Abstract . One of the basic challenges to adopting science-based irrigation scheduling is providing reliable, site-specific estimates of actual crop water demand. While agro-meteorology networks cover most agricultural production areas in the U.S., widely spaced stations represent regionally specific, rather than site-specific, conditions. A variety of low to moderate cost commercial weather stations are available but do not provide directly useful information, such as actual evapotranspiration (ETa), or the ability to incorporate additional sensors. We demonstrate that machine learning methods can provide real-time, site-specific information about ETa and crop water demand using on-farm sensors and public weather information. Two years of field experiments were conducted at four irrigated field sites with crops including snap beans, alfalfa, and pasture. On-farm data were compared to publicly available data originating at nearby agro-meteorology network stations. The machine learning procedure can robustly estimate ETa using data from a few basic sensors, but the resulting estimate is sensitive to the range of conditions that are used as training data. The results demonstrate that machine learning can be used with affordable sensors and publicly available data to improve local estimates of crop water demand when high-quality measurements can be co-located for short periods of time. Supplementary sensors can also be integrated into a tailored monitoring plan to estimate crop stress and other operational considerations. Keywords: Agro-meteorology, Irrigation requirement, Machine learning, Site-specific Irrigation. 

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3. Review: can bioelectrochemical sensors be used to monitor soil microbiome activity and fertility?

   https://doi.org/10.1016/j.copbio.2024.103222  

   Webster, Christina F; Kim, Won-Jun; Reguera, Gemma; Friesen, Maren L; Beyenal, Haluk (December 2024, Current Opinion in Biotechnology)

   This review presents current knowledge on applying bioelectrochemical sensors to monitor soil fertility through microbial activity and discusses future perspectives. Soil microbial activity is considered an indicator of soil fertility due to the interconnected relationship between soil nutrient composition, microbiome, and plant productivity. Similarities between soils and bioelectrochemical reactors provide the foundation for the design of bioelectrochemical sensors driven by microorganisms enriched as electrochemically active biofilms on polarized electrodes. The biofilm can exchange electrons with electrodes and metabolites with the nearby microbiome to generate electrochemical signals that inform of microbiome functions and nutrient bioavailability. Such mechanisms can be used as a bioelectrochemical sensor for proxy monitoring of soil fertility to address the need for real-time monitoring of soils. 

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4. Data Acquisition and Analysis for Improving the Utility of Low Cost Soil Moisture Sensors

   https://doi.org/10.1109/SMARTCOMP58114.2023.00087  

   Mundewadi, Gautam; Wolski, Rich; Krintz, Chandra (June 2023, IEEE International Conference on Smart Computing)

   To cultivate healthy plants and high crop yields, growers must be able to measure soil moisture and irrigate accordingly. Errors in soil moisture measurements can lead to irrigation mismanagement with costly consequences. In this paper, we present a new approach to smart computing for irrigation management to address these challenges at a lower cost. We calibrate low cost, low precision soil moisture sensors to more accurately distinguish wet from dry soils using high cost, high precision Davis Instrument sensors. We investigate different modeling techniques including the natural log of the odds ratio (Log-odds), Monte Carlo simulation, and linear regression to distinguish between wet and moist soils and to establish a trustworthy threshold between these two moisture states. We have also developed a new smartphone application that simplifies the process of data collection and implements our analysis approach. The application is extensible by others and provides growers with low cost, data-driven decision support for irrigation. We implement our approach for UCSB’s Edible Campus student farm and empirically evaluate it using multiple test beds. Our results show an accuracy rate of 91% and lowers costs by 4x per deployment, making it useful for gardeners and farmers alike. 

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5. Printed microfluidic sweat sensing platform for cortisol and glucose detection

   https://doi.org/10.1039/D1LC00633A  

   Naik, Aditi R.; Zhou, Yiliang; Dey, Anita A.; Arellano, D. Leonardo; Okoroanyanwu, Uzodinma; Secor, Ethan B.; Hersam, Mark C.; Morse, Jeffrey; Rothstein, Jonathan P.; Carter, Kenneth R.; et al (December 2021, Lab on a Chip)

   Wearable sweat biosensors offer compelling opportunities for improved personal health monitoring and non-invasive measurements of key biomarkers. Inexpensive device fabrication methods are necessary for scalable manufacturing of portable, disposable, and flexible sweat sensors. Furthermore, real-time sweat assessment must be analyzed to validate measurement reliability at various sweating rates. Here, we demonstrate a “smart bandage” microfluidic platform for cortisol detection and continuous glucose monitoring integrated with a synthetic skin. The low-cost, laser-cut microfluidic device is composed of an adhesive-based microchannel and solution-processed electrochemical sensors fabricated from inkjet-printed graphene and silver solutions. An antibody-derived cortisol sensor achieved a limit of detection of 10 pM and included a low-voltage electrowetting valve, validating the microfluidic sensor design under typical physiological conditions. To understand effects of perspiration rate on sensor performance, a synthetic skin was developed using soft lithography to mimic human sweat pores and sweating rates. The enzymatic glucose sensor exhibited a range of 0.2 to 1.0 mM, a limit of detection of 10 μM, and reproducible response curves at flow rates of 2.0 μL min −1 and higher when integrated with the synthetic skin, validating its relevance for human health monitoring. These results demonstrate the potential of using printed microfluidic sweat sensors as a low-cost, real-time, multi-diagnostic device for human health monitoring. 

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