Nondestructive detection for moisture content in green tea based on dielectric properties and VISSA‐GWO‐SVR algorithm

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.

Integrating spectral and image information for prediction of cottonseed vitality

Cotton plays a significant role in people's lives, and cottonseeds serve as a vital assurance for successful cotton cultivation and production. Premium-quality cottonseeds can significantly enhance the germination rate of cottonseeds, resulting in increased cotton yields. The vitality of cottonseeds is a crucial metric that reflects the quality of the seeds. However, currently, the industry lacks a non-destructive method to directly assess cottonseed vitality without compromising the integrity of the seeds. To address this challenge, this study employed a hyperspectral imaging acquisition system to gather hyperspectral data on cottonseeds. This system enables the simultaneous collection of hyperspectral data from 25 cottonseeds. This study extracted spectral and image information from the hyperspectral data of cottonseeds to predict their vitality. SG, SNV, and MSC methods were utilized to preprocess the spectral data of cottonseeds. Following this preprocessing step, feature wavelength points of the cottonseeds were extracted using SPA and CARS algorithms. Subsequently, GLCM was employed to extract texture features from images corresponding to these feature wavelength points, including attributes such as Contrast, Correlation, Energy, and Entropy. Finally, the vitality of cottonseeds was predicted using PLSR, SVR, and a self-built 1D-CNN model. For spectral data analysis, the 1D-CNN model constructed after MSC+CARS preprocessing demonstrated the highest performance, achieving a test set correlation coefficient of 0.9214 and an RMSE of 0.7017. For image data analysis, the 1D-CNN model constructed after SG+CARS preprocessing outperformed the others, yielding a test set correlation coefficient of 0.8032 and an RMSE of 0.9683. In the case of fused spectral and image data, the 1D-CNN model built after SG+SPA preprocessing displayed the best performance, attaining a test set correlation coefficient of 0.9427 and an RMSE of 0.6872. These findings highlight the effectiveness of the 1D-CNN model and the fusion of spectral and image features for cottonseed vitality prediction. This research contributes significantly to the development of automated detection devices for assessing cottonseed vitality.

In-Time Detection of Plant Water Status Change by Self-Adhesive, Water-Proof, and Gas-Permeable Electrodes

Leaf capacitance can reflect plant water content. However, the rigid electrodes used in leaf capacitance monitoring may affect plant health status. Herein, we report a self-adhesive, water-proof, and gas-permeable electrode fabricated by in situ electrospinning of a polylactic acid nanofiber membrane (PLANFM) on a leaf, spraying a layer of the carbon nanotube membrane (CNTM) on PLANFM, and in situ electrospinning of PLANFM on CNTM. The electrodes could be self-adhered to the leaf via electrostatic adhesion due to the charges on PLANFM and the leaf, thus forming a capacitance sensor. Compared with the electrode fabricated by a transferring approach, the in situ fabricated one did not show obvious influence on plant physiological parameters. On that basis, a wireless leaf capacitance sensing system was developed, and the change of plant water status was detected in the first day of drought stress, which was much earlier than direct observation of the plant appearance. This work paved a useful way to realize noninvasive and real-time detection of stress using plant wearable electronics.

Development and Validation of Near-Infrared Methods for the Quantitation of Caffeine, Epigallocatechin-3-gallate, and Moisture in Green Tea Production

The quality of tea leaves (e.g., their color, appearance, and taste) can be directly influenced by the tea production process, which is closely connected with the content of a number of chemical components formed during the production of the tea leaves. However, the production process is now controlled by people's experience, making its quality significantly different. NIRS is a time-saving, cost-saving, and nondestructive method. Therefore, it is necessary to introduce NIRS technology into the quality control of the tea production process. In this study, a quantitative analysis model of caffeine, epigallocatechin-3-gallate (EGCG), and moisture content was established by near-infrared spectroscopy (NIRS) which was united simultaneously with partial least squares (PLSR) for online process monitoring of tea production. The model parameters show that the established model has fine robustness and outstanding measuring accuracy. Then, the feasibility of the established method is verified by the traditional method. Through the verification of the precision of the instrument and the stability of the sample, it is clarified that the model can be further utilized to monitor tea product quality online in a productive process.