Freeze-Damage Detection in Lemons Using Electrochemical Impedance Spectroscopy

Integrating non-invasive VIS-NIR and bioimpedance spectroscopies for stress classification of sweet basil (Ocimum basilicum L.) with machine learning

Plant stress diagnosis is essential for efficient crop management and productivity increase. Under stress, plants undergo physiological and compositional changes. Vegetation indices obtained from leaf reflectance spectra and bioimpedance spectroscopy provide information about the external and internal aspects of plant responses, respectively. In this study, bioimpedance and vegetation indices were noninvasively acquired from sweet basil (Ocimum basilicum L.) leaves exposed to three types of stress (drought, salinity, and chilling). Integrating the vegetation index, a novel approach, contains information about the surface of plants and bioimpedance data, which indicates the internal changes of plants. The fusion of these two datasets was examined to classify the types and severity of stress. Among the eight supervised machine learning models (three linear and five non-linear), the support vector machine (SVM) exhibited the highest accuracy in classifying stress types. Bioimpedance spectroscopy alone exhibited an accuracy of 0.86 and improved to 0.90 when fused with vegetation indices. Additionally, for drought and salinity stresses, it was possible to classify the early stage of stress with accuracies of 0.95 and 0.93, respectively. This study will allow us to classify the different types and severity of plant stress, prescribe appropriate treatment methods for efficient cost and time management of crop production, and potentially apply them to low-cost field measurement systems.

Experimental investigation of freeze injury temperatures in trees and their contributing factors based on electrical impedance spectroscopy

In trees, injuries resulting from subfreezing temperatures can cause damage to the cellular biofilm system, metabolic functions, and fibrous reticulum, and even cell death. Investigating the occurrence of freezing damage and its contributing factors could help understand the mechanisms underlying freezing injury and prevent the subsequent damage in trees. To achieve this, a laboratory experiment was conducted using cut wood samples from Korean pine (Pinus koraiensis Siebold & Zucc) and Simon poplar (Populus simonii Carr.), and the effects of environmental freezing factors, including freezing temperatures, freezing duration, and cooling rate, on the temperature at which freezing injuries occur were examined using the electrical impedance spectroscopy (EIS) method. The semi-lethal temperature (LT50), as an indicator of freezing injury in wood tissue, was theoretically deduced based on the measured extracellular resistance (r e) using EIS. The contributory factors to changes in LT50 were determined and their relationship was established. The results revealed that all freezing factors exhibited significant effects on electrical impedance characteristics (r e, r i, and τ), significantly influencing the LT50 of the wood. Random forest (RF) and support vector machine (SVM) models were used to assess the contribution of the freezing factors and moisture content (MC). Among the factors examined, freezing duration had the greatest impact on LT50, followed by the MC, whereas the contribution of the cooling rate was minimal. The model accuracies were 0.89 and 0.86 for Korean pine and Simon poplar, respectively. The findings of our study illustrate that the occurrence of freezing injury in trees is primarily influenced by the duration of freezing at specific subzero temperatures. Slow cooling combined with prolonged freezing at low subzero temperatures leads to earlier and more severe freezing damage.

A wearable and capacitive sensor for leaf moisture status monitoring

The real-time and precise monitoring of plant physiological information, such as leaf capacitance, is important in agricultural production. However, current approaches for leaf capacitance monitoring are easy to cause damage to plants, which would decrease the accuracy of monitoring. In this study, we proposed the wearable electrodes for real-time monitoring of leaf capacitance. Gold nanoparticles were magnetron sputterred on polyethylene terephthalate (PET) membrane to form the wearable Au@PET electrodes. Due to their excellent flexibility, the electrodes showed good stability in both conductivity and capacitance sensing. The electrodes could be conformally attached to the leaf surface to form leaf capacitance sensor. It was found that capacitance value was positively correlated with leaf moisture content. Additionally, leaf capacitance showed higher value at night than daytime, with an extent of 12.02% and the results obtained from Au@PET electrodes were similar to the ones from traditional rigid electrodes. Besides, the growth and physiological parameters of Epipremnum aureum were not significantly affected during capacitance monitoring by Au@PET electrodes. Such results demonstrated the potential of wearable electrodes for real-time and precise monitoring of plant physiological information in the future.

Plant impedance spectroscopy: a review of modeling approaches and applications

Electrochemical impedance spectroscopy has emerged over the past decade as an efficient, non-destructive method to investigate various (eco-)physiological and morphological properties of plants. This work reviews the state-of-the-art of impedance spectra modeling for plant applications. In addition to covering the traditional, widely-used representations of electrochemical impedance spectra, we also consider the more recent machine-learning-based approaches.

Electrical Impedance Spectroscopy for Moisture and Oil Content Prediction in Oil Palm (Elaeis guineensis Jacq.) Fruitlets

The global palm oil industry is targeting an increased oil extraction rate in oil palm milling to meet global demand. This can be achieved through the certification of mills and adherence to bunch grading as part of ensuring that only high-quality and ripe fresh fruit bunches are accepted and processed at all mills. However, the current grading process requires the analysis of oil palm bunches, which is laborious and tedious or prone to error due to human subjectivity. This paper introduces a non-destructive technique to predict the moisture and oil content in oil palm fruitlets using electrical impedance spectroscopy. In total, 90 samples of oil palm fruitlets at different stages of ripeness were acquired. Electrical impedance measurement of each fruitlet was done using electrocardiogram (ECG) electrodes connected to an LCR meter at frequencies of 1 kHz, 10 kHz, 20 kHz, and 100 kHz. The actual oil content in the fruitlets was determined using the Soxhlet extraction method, while the actual moisture content was determined using a standard oven-drying method. The variation of electrical impedance values at each frequency was analyzed. At 100 kHz, the correlation coefficients relating the electrical impedance to the moisture and oil content were around -0.84 and 0.80, respectively. Predictions of the moisture and oil content using linear regression of the impedance measurements at 100 kHz gave RMSE values of 5.85% and 5.71%, respectively. This information is useful for oil palm fruit grading and oil yield production estimation in the palm oil industry.

Improving FPGA Based Impedance Spectroscopy Measurement Equipment by Means of HLS Described Neural Networks to Apply Edge AI

The artificial intelligence (AI) application in instruments such as impedance spectroscopy highlights the difficulty to choose an electronic technology that correctly solves the basic performance problems, adaptation to the context, flexibility, precision, autonomy, and speed of design. Present work demonstrates that FPGAs, in conjunction with an optimized high-level synthesis (HLS), allow us to have an efficient connection between the signals sensed by the instrument and the artificial neural network-based AI computing block that will analyze them. State-of-the-art comparisons and experimental results also demonstrate that our designed and developed architectures offer the best compromise between performance, efficiency, and system costs in terms of artificial neural networks implementation. In the present work, computational efficiency above 21 Mps/DSP and power efficiency below 1.24 mW/Mps are achieved. It is important to remark that these results are more relevant because the system can be implemented on a low-cost FPGA.

A Portable Battery-Operated Sensor System for Simple and Rapid Assessment of Virgin Olive Oil Quality Grade

Virgin olive oil quality is assessed by chemical as well as sensory analysis. Two of the most important parameters that define the quality of virgin olive oils are the free acidity and the peroxide index. These chemical parameters are usually determined by manual titration procedures that must be carried out in a laboratory by trained personnel. In this paper, a portable sensor system to support the quality grade assessment of virgin olive oil is presented. The system is battery operated and characterized by small dimensions, light weight and quick measurement response (about 30 s). The working principle is based on the measurement of the electrical conductance of an emulsion between a chemical reagent and the olive oil sample. Two different chemical reagents have been investigated: (1) a hydro-alcoholic solution (HAS), made of 60% ethanol and 40% distilled water; (2) 100% distilled water (DW). Tests have been carried out on a set of 40 olive oil samples. The results have shown how, for most of the fresh virgin olive oil samples (31 samples out of 40), the free acidity can be estimated with good accuracy from the electrical conductance of the emulsion using HAS as the reagent. In the case of the full set of samples, the emulsion electrical conductance, using HAS as the reagent, is a function of both the sample free acidity as well as the compounds produced by oil oxidation, and a compensation method based on the measured electrical conductance, using DW as the reagent, has been introduced to improve the accuracy in the estimated free acidity. Tests have also been carried out on the full set of samples, using a k-nearest neighbors algorithm, to demonstrate the feasibility of olive oil classification according to the quality grade. The results have shown how measurements carried out using only the HAS reagent provide better classification accuracy than measurements carried out using both the HAS and DW reagents. The proposed system can be a low-cost alternative to standard laboratory analyses to evaluate the quality grade of virgin olive oil.

Monitoring Freeze-Damage in Grapefruit by Electric Bioimpedance Spectroscopy and Electric Equivalent Models

Grapefruit is a cold-sensitive citrus fruit, and freezing can spoil the harvest when the fruit is still on the tree and even later during manufacturing and transport due to inappropriate postharvest management. This study performed a specific Electric Impedance Spectroscopy (EIS) analysis and statistical data treatment to obtain an EIS and Artificial Neural Networks (ANN)-based model for early freeze-damage detection in grapefruit showing a Correct Correlation Rate of 100%. Additionally, Cryo-Field Emission Scanning Electron Microscopy observations were conducted on both fresh and frozen/thawed samples, analyzing the different impedance responses in order to understand the biological changes in the tissue. Finally, a modified Hayden electric equivalent model was parameterized to simulate the impedance response electrically and link the electric behavior of biological tissue to the change in its properties due to freezing. The developed technique is introduced as an alternative to the traditional ones, as it is fast, economic, and easy to carry out.

LbCas12a-D156R Efficiently Edits LOB1 Effector Binding Elements to Generate Canker-Resistant Citrus Plants

Citrus canker caused by Xanthomonas citri subsp. citri (Xcc) is an economically important disease in most citrus production regions worldwide. Xcc secretes a transcriptional activator like effector (TALE) PthA4 to bind to the effector binding elements (EBEs) in the promoter region of canker susceptibility gene LOB1 to activate its expression, which in turn causes canker symptoms. Editing the EBE region with Cas9/gRNA has been used to generate canker resistant citrus plants. However, most of the EBE-edited lines generated contain indels of 1–2 bp, which has higher possibility to be overcome by PthA4 adaptation. The adaptation capacity of TALEs inversely correlates with the number of mismatches with the EBE. LbCas12a/crRNA is known to generate longer deletion than Cas9. In this study, we used a temperature-tolerant and more efficient LbCas12a variant (ttLbCas12a), harboring the single substitution D156R, to modify the EBE region of LOB1. We first constructed GFP-p1380N-ttLbCas12a:LOBP, which was shown to be functional via Xcc-facilitated agroinfiltration in Pummelo (Citrus maxima) leaves. Subsequently, we stably expressed ttLbCas12a:LOBP in Pummelo. Eight transgenic lines were generated, with seven lines showing 100% mutations of the EBE, among which one line is homozygous. The EBE-edited lines had the ttLbCas12a-mediated deletions of up to 10 bp. Importantly, the seven lines were canker resistant and no off-targets were detected. In summary, ttLbCas12a can be used to efficiently generate biallelic/homozygous citrus mutant lines with short deletions, thus providing a useful tool for the functional study and breeding of citrus.

Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data

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[Formula: see text], F[Formula: see text] and F[Formula: see text]-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.

A CMOS Low Pass Filter for SoC Lock-in-Based Measurement Devices

This paper presents a fully integrated G_m–C low pass filter (LPF) based on a current steering G_m reduction-tuning technique, specifically designed to operate as the output stage of a SoC lock-in amplifier. To validate this proposal, a first-order and a second-order single-ended topology were integrated into a 1.8 V to 0.18 µm CMOS (Complementary Metal-Oxide-Semiconductor) process, showing experimentally a tuneable cutoff frequency that spanned five orders of magnitude, from tens of mHz to kHz, with a constant current consumption (below 3 µA/pole), compact size (<0.0140 mm²/pole), and a dynamic range better than 70 dB. Compared to state-of-the-art solutions, the proposed approach exhibited very competitive performances while simultaneously fully satisfying the demanding requirements of on-chip portable measurement systems in terms of highly efficient area and power. This is of special relevance, taking into account the current trend towards multichannel instruments to process sensor arrays, as the total area and power consumption will be proportional to the number of channels.