Development and deployment of sensing technologies is one of the main steps in achieving sustainability in crop production through precision agriculture. Key sensing methodologies developed for monitoring soil moisture and nutrients with recent advances in the sensing devices reported in literature using those techniques are overviewed in this article. The soil moisture determination has been divided into four main sections describing soil moisture measurement metrics and laboratory-based testing, followed by in-situ, remote and proximal sensing techniques. The application, advantages and limitations for each of the mentioned technologies are discussed. The nutrient monitoring methods are reviewed beginning with laboratory-based methods, ion-selective membrane based sensors, bio-sensors, spectroscopy-based methods, and capillary electrophoresis-based systems for inorganic ion detection. Attention has been given to the core principle of detection while reporting recent sensors developed using the mentioned concepts. The latest works reported on the different sensing methodologies point towards the trend of developing low-cost, easy to use, field-deployable or portable sensing systems aimed towards improving technology adoption in crop production leading to efficient site-specific soil and crop management which in turn will bring us closer to reaching sustainability in the practice of agriculture.

Reducing agricultural losses is an effective way to sustainably increase agricultural output efficiency to meet our present and future needs for food, fiber, fodder, and fuel. Our ever-improving understanding of the ways in which plants respond to stress, biotic and abiotic, has led to the development of innovative sensing technologies for detecting crop stresses/stressors and deploying efficient measures. This article aims to present the current state of the methodologies applied in the field of agriculture towards the detection of biotic stress in crops. Key sensing methodologies for plant pathogen (or phytopathogen), as well as herbivorous insects/pests are presented, where the working principles are described, and key recent works discussed. The detection methods overviewed for phytopathogen-related stress identification include nucleic acid-based methods, immunological methods, imaging-based techniques, spectroscopic methods, phytohormone biosensing methods, monitoring methods for plant volatiles, and active remote sensing technologies. Whereas the pest-related sensing techniques include machine-vision-based methods, pest acoustic-emission sensors, and volatile organic compound-based stress monitoring methods. Additionally, Comparisons have been made between different sensing techniques as well as recently reported works, where the strengths and limitations are identified. Finally, the prospective future directions for monitoring biotic stress in crops are discussed.

Level sensors find use in various applications for liquid-level monitoring in tanks, containers, and vessels. In this paper, an impedance spectroscopy-based sensing platform has been reported, employing a co-axial sensing probe. The supporting electronics was developed using the MSP 432 microcontroller unit (MCU) from Texas Instruments (TI) and a newly designed Impedance Analyzer-Analog Front End (IA-AFE), also developed at TI. Different signal processing and data processing methods were employed for coaxial probe impedance modeling and their results discussed. The sensor can be used for conductive as well as non-conductive liquids, including liquids with low dielectric constant. Successful measurements of liquid levels were shown for water (E ~ 80) as well as vegetable oil (E ~ 3). The developed sensing platform can measure water level with the accuracy of about 1.2 mm across a 400-mm-long sensing probe while exhibiting a maximum error under 0.6 mm for oil over 150-mm range. The proposed level sensing system provides an accurate and economical solution for level sensing, which is cheaper than the commercially available level sensors while outperforming the accuracy compared with the present state of the art, to the best of our knowledge.

Salicylic acid is an important chemical used in various industries including pharmaceutical, cosmetic, food and agriculture. It is a key phytohormone responsible for initiating and regulating defense mechanisms against biotic as well as environmental stress in plants. It is a major component of Aspirin, a common anti-inflammatory, anti-pyretic and analgesic drug whose overusage and mis-dosage can lead to various health problems in humans. In this work, a novel electro-reduced graphene oxide modified screen printed carbon electrode electrochemical sensor has been presented for the detection of salicylic acid based on its electro-oxidation. The proposed device offers an inexpensive, sensitive, selective, quick responding, rapid and easy to fabricate sensor to salicylic acid measurement with excellent performance. The proposed sensor is capable of measuring concentrations from 2 to 512 µM with a maximum sensitivity of 600 µA/mM/cm ² with high (piece-wise) linearity, and was used to determine salicylic acid level in fresh orange juice with 99% accuracy.

Salicylic acid (SA) is a primary phytohormone released in response to stress (particularly biotic infections) in plants. Monitoring SA levels in plants may provide a way for early crop disease detection and application of effective measures, resulting in higher agricultural efficiency. Additionally, SA is an important chemical in the pharmaceutical and healthcare industry due to its analgesic and anti-inflammatory properties. Developing a fast and accurate way for monitoring SA levels in human serum can have a life-saving impact for patients suffering from overdosing and/or mis-dosing. In this work, we present a low-cost, portable, and field-deployable electrochemical SA sensing system aimed towards achieving the above-mentioned goals. The developed sensor consists of a plug-and-play type device equipped with specially designed high accuracy sensing electronics and a novel procedure for robust data analysis. The developed sensor exhibits excellent linearity and sensitivity and selectivity. The practical applicability of the developed sensor was also demonstrated by measuring SA levels in real samples with good accuracy.