The use of sensors and the Internet of Things (IoT) is key to moving the world’s agriculture to a more productive and sustainable path. Recent advancements in IoT, Wireless Sensor Networks (WSN), and Information and Communication Technology (ICT) have the potential to address some of the environmental, economic, and technical challenges as well as opportunities in this sector. As the number of interconnected devices continues to grow, this generates more big data with multiple modalities and spatial and temporal variations. Intelligent processing and analysis of this big data are necessary to developing a higher level of knowledge base and insights that results in better decision making, forecasting, and reliable management of sensors. This paper is a comprehensive review of the application of different machine learning algorithms in sensor data analytics within the agricultural ecosystem. It further discusses a case study on an IoT based data-driven smart farm prototype as an integrated food, energy, and water (FEW) system.

Wearable sensors are becoming an important tool for healthcare monitoring as they can provide continuous monitoring of clinically important biomarkers released in the biofluids such as sweat, saliva, tears, urine and interstitial fluids (ISF). Development of smart sensing technologies for stress detection in daily life is find applications in various field including healthcare, defence and sports industries. Although different modalities for stress detection are performed in the clinical laboratories, the measurement of hormone release from glands of the endocrine system is receiving wide attention. Cortisol is considered as a biomarker of stress as the levels are spike in response to stressors. Although most of the cortisol released from adrenaline glands bound by corticosteroid-binding globulin (CBG), active form of free cortisol is also appearing in the biofluids (sweat, saliva, urine, tear, and ISF). Electrochemical biosensors, due to their relative small farm factors and sensitivity are focused for developing wearable biosensors for non-invasive monitoring of biochemical markers. Recent technological advancements in the field of electrochemical sensors and material engineering have contributed widely to the growth of new generation wearable point of care (POC) systems that can allow for early diagnosis. Receptor molecules including antibodies, enzyme fragments, molecularly imprinted polymers, and other biomimetic materials have been explored for electrochemical sensing of cortisol. Interfacing of theses sensing strategies into fabrics and flexible wearable patches for sweat cortisol analysis are also attempted. This mini review focuses on development made in the area of cortisol sensors using fabrics and flexible substrates potential for wearable sensor applications. An overview of cortisol secretion in the body and its availability in different biological fluids are also discussed. The issues associated with realizing complete wearable sensor devices are also explored.

Wound management involves repeated clinical trips and procedures of lab tests over days. To eliminate this time lag and provide real-time monitoring of a wound's progress, we have designed an enzymatic biosensor for determining uric acid (UA) in wound fluid. Uric Acid is a biomarker, having an established correlation with wounds and their healing. This electrochemical biosensor comprises enzyme urate oxidase (uricase, UOx) entrapped in a polyvinyl alcohol based cationic polymer for enhanced stability. Results show that the use of a redox electron shuttle, ferrocene carboxylic acid (FCA), enabled electron transfer between the enzyme and the transducer. The immobilized uricase in the polymer matrix provided stable continuous measurements at body temperature for a week with minimal deviation. Detection of uric acid in wound fluid has been determined from volumes as low as 0.5–50μL. Studies from different wound samples have shown an average recovery of 107%. The sensor has been interfaced with LMP91000 potentiostat and controlled by CC2650 microcontroller on a Kapton tape-based miniaturized flexible platform.

Polyaniline nanowires (PANI) and gold nanoparticle (AuNPs) based hybrid nanocomposite film fabricated onto a Au substrate has been used to fabricate a mediator free immunosensor for the detection of PSA. Microscopic studies reveal uniform distribution of the AuNPs on the PANI backbone resulting in a nanoporous morphology that provides the desired microenvironment for antibody immobilization. The results of electrochemical studies suggest that AuNPs increase the electro-active surface area of PANI and result in high electron transport in a mediator free electrolyte. The AuNP–PANI hybrid nanocomposite also provides a very effective surface for the immobilization of anti-PSA revealing an increase in electron transport that results in improved sensing performance. The electrochemical response of the anti-PSA/AuNP–PANI/Au immunoelectrode as a function of PSA concentration exhibits a linear range of 1 pg mL⁻¹ to 100 ng mL⁻¹, a detection limit of 0.6 pg mL⁻¹ (lower than ELISA), a sensitivity of 1.4 μA M⁻¹ (higher than ELISA) along with a regression coefficient of 98.99%.