The precise monitoring of environmental contaminants and agricultural plant stress factors, respectively responsible for damages to our ecosystems and crop losses, has nowadays become a topic of uttermost importance. This is also highlighted by the recent introduction of the so-called “Sustainable Development Goals” of the United Nations, which aim at reducing pollutants while implementing more sustainable food production practices, leading to a reduced impact on all ecosystems. In this context, the standard methods currently used in these fields represent a sub-optimal solution, being expensive, laboratory-based techniques, and typically requiring trained personnel with high expertise. Recent advances in both biotechnology and material science have led to the emergence of new sensing (and biosensing) technologies, enabling low-cost, precise, and real-time detection. An especially interesting category of biosensors is represented by field-effect transistor-based biosensors (bio-FETs), which enable the possibility of performing in situ, continuous, selective, and sensitive measurements of a wide palette of different parameters of interest. Furthermore, bio-FETs offer the possibility of being fabricated using innovative and sustainable materials, employing various device configurations, each customized for a specific application. In the specific field of environmental and agricultural monitoring, the exploitation of these devices is particularly attractive as it paves the way to early detection and intervention strategies useful to limit, or even completely avoid negative outcomes (such as diseases to animals or ecosystems losses). This review focuses exactly on bio-FETs for environmental and agricultural monitoring, highlighting the recent and most relevant studies. First, bio-FET technology is introduced, followed by a detailed description of the the most commonly employed configurations, the available device fabrication techniques, as well as the specific materials and recognition elements. Then, examples of studies employing bio-FETs for environmental and agricultural monitoring are presented, highlighting in detail advantages and disadvantages of available examples. Finally, in the discussion, the major challenges to be overcome (e.g., short device lifetime, small sensitivity and selectivity in complex media) are critically presented. Despite the current limitations and challenges, this review clearly shows that bio-FETs are extremely promising for new and disruptive innovations in these areas and others.

Strawberry is one of the most popular fruits in the market. To meet the demanding consumer and market quality standards, there is a strong need for an on-site, accurate and reliable grading system during the whole harvesting process. In this work, a total of 923 strawberry fruit were measured directly on-plant at different ripening stages by means of bioimpedance data, collected at frequencies between 20 Hz and 300 kHz. The fruit batch was then splitted in 2 classes (i.e. ripe and unripe) based on surface color data. Starting from these data, six of the most commonly used supervised machine learning classification techniques, i.e. Logistic Regression (LR), Binary Decision Trees (DT), Naive Bayes Classifiers (NBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron Networks (MLP), were employed, optimized, tested and compared in view of their performance in predicting the strawberry fruit ripening stage. Such models were trained to develop a complete feature selection and optimization pipeline, not yet available for bioimpedance data analysis of fruit. The classification results highlighted that, among all the tested methods, MLP networks had the best performances on the test set, with 0.72, 0.82 and 0.73 for the F $$_1$$1 , F $$_{0.5}$$0.5 and F $$_2$$2 -score, respectively, and improved the training results, showing good generalization capability, adapting well to new, previously unseen data. Consequently, the MLP models, trained with bioimpedance data, are a promising alternative for real-time estimation of strawberry ripeness directly on-field, which could be a potential application technique for evaluating the harvesting time management for farmers and producers.

Detection of mycotoxins, especially aflatoxin M1 (AFM1), in milk is crucial to be able to guarantee food quality and safety. In recent years, biosensors have been emerging as a fast, reliable and low-cost technique for the detection of this toxin. In this work, flexible biosensors were fabricated using dispense-printed electrodes, which were functionalized with single-walled carbon nanotubes (SWCNTs) and subsequently coated with specific antibodies to improve their sensitivity. Next, the immunosensor was tested for the detection of AFM1 in buffer solution and a spiked milk sample using a chronoamperometric technique. Results showed that the working range of the sensors was 0.01 µg/L at minimum and 1 µg/L at maximum in both buffer and spiked milk. The lower limit of detection of the SWCNT-functionalized sensor was 0.02 µg/L, which indicates an improved sensitivity compared to the sensors reported so far. The sensitivity and detection range were in accordance with the limitation values imposed by regulations on milk and its products. Therefore, considering the low fabrication cost, the ease of operation, and the rapid read-out, the use of this sensor could contribute to safeguarding consumers’ health.

This paper presents a portable impedance analyzer based on the AD5933 integrated circuit, specifically designed for fruit quality monitoring throughout its entire supply chain. The custom designed system allows to flexibly tune the parameters (i.e. excitation voltage, feedback resistor, settling time) for each AD5933 frequency range from 10Hz to 100kHz, leading to a precise system calibration and consequently, measurement. FruitMeter runs MicroPython on an STM32 microcontroller, allowing ease of development, advanced low-power mode, and both on-demand and scheduled measurements. The system employs Low-Energy Bluetooth and USB communication with a micro SD data storage which, together with its small size (7.5 ×2 ×4cm in volume) and the 300mAh rechargeable battery with USB recharge, increases the system portability and connectivity, critical for smart agriculture on-field applications. The developed portable system is validated by comparing its performance with a benchtop impedance analyzer with both passive components and fruit samples (apple, avocado, banana, mango and tomato). The comparison resulted in an average Root Mean Square Error (RMSE) of 1.0% and 4.3% for the passive components and of 1.36% and 6.8% for the fruit samples, for impedance magnitude and phase, respectively. We finally validated the system for a real use-case regarding the evaluation of banana aging evolution.

Novel non-destructive sensing techniques for fast and reliable fruit quality assessment are strongly needed to meet both production and consumer demands. In this direction, Electrical Impedance Spectroscopy (EIS) has shown a great potential in correlating the electrical parameters of fruits with their quality attributes. In this paper, the effect of aging on 9 bananas during a 12 days period was studied using a microcontroller-based EIS system. The bio-impedance was measured in the 100 Hz - 85 kHz frequency range and then correlated to the image analysis of the color evolution of such fruits. The results showed a good potential in monitoring the ripening process of bananas, with a good correlation of the low frequency points with the impedance evolution. Finally, both the impedance and the image analysis indicated the presence of a turning point in the ripening process.