Implantable Electrochemical Microsensors for In Vivo Monitoring of Animal Physiological Information

In vivo monitoring of animal physiological information plays a crucial role in promptly alerting humans to potential diseases in animals and aiding in the exploration of mechanisms underlying human diseases.Currently,implantable electrochemical microsensors have emerged as a prominent area of research.These microsensors not only fulfill the technical requirements for monitoring animal physiological information but also offer an ideal platform for integration.They have been extensively studied for their ability to monitor animal physiological information in a minimally invasive manner,characterized by their bloodless,painless features,and exceptional performance.The development of implantable electrochemical microsensors for in vivo monitoring of animal physiological information has witnessed significant scientific and technological advancements through dedicated efforts.This review commenced with a comprehensive discussion of the construction of microsensors,including the materials utilized and the methods employed for fabrication.Following this,we proceeded to explore the various implantation technologies employed for electrochemical microsensors.In addition,a comprehensive overview was provided of the various applications of implantable electrochemical microsensors,specifically in the monitoring of diseases and the investigation of disease mechanisms.Lastly,a concise conclusion was conducted on the recent advancements and significant obstacles pertaining to the practical implementation of implantable electrochemical microsensors.

Modeling method for SSC prediction in pomelo using Vis-NIRS with wavelength selection and latent variable updating

The aim of this study was in-line,rapid,and non-destructive detection for soluble solid content(SSC)in pomelos using visible and near-infrared spectroscopy(Vis-NIRS).However,the large size and thick rind of pomelo affect the stability of spectral acquisition and the biological variabilities affect the robustness of models.Given these issues,in this study,an efficient prototype in-line detection system in transmittance mode was designed and evaluated in comparison with an off-line detection system.Data from the years 2019 and 2020 were used for modeling and the external validation data were obtained by the inline detection system in 2021.The wavelength selection methods of changeable size moving window(CSMW),random frog(RF),and competitive adaptive reweighted sampling(CARS)were used to improve the prediction accuracy of partial least squares regression(PLSR)models.The best performance of internal prediction was obtained by CARS-PLSR and the determination coefficient of prediction(),root mean square error of prediction(RMSEP),and residual predictive deviation(RPD)were 0.958,0.204%,and 4.821,respectively.However,all models obtained large prediction biases in external validation.The latent variable updating(LVU)method was proposed to update models and improve the performance in external validation.Ten samples from the external validation set were randomly selected to update the models.Compared with the recalibration method,LVU could effectively modify the original models which matched the SSC range of the external validation set.The CSMW-PLSR models were more robust in external validations.The off-line model with LVU performed best with a root mean square error of validation(RMSEV)of 0.599%and the in-line model with recalibration obtained RMSEV of 0.864%.These results demonstrated the application potential of the transmittance Vis-NIRS for in-line rapid prediction of SSC in pomelos and the modeling and updating methods could be applied to samples with biological variabilities.

Geometric based apple suction strategy for robotic packaging

Packaging is one of the least automated steps among all the fruit postharvest processes,which is time-consuming and labor-intensive.Therefore,a robust suction strategy for robotic manipulation needs to be developed.In this research,a geometric-based apple suction strategy for robotic packaging was studied,including suction cup design,optimal suction region selection algorithm,and robot system integration.In the first place,on the basis of the geometric features of the spheroid fruit,the structure of the suction cups was designed to provide reliable suction force.Then,suction force measurement experiments on both acrylic balls and apples were conducted.Based on the results,the parameters of the suction cup were finally determined.The results also indicated that the curvature radius of the suction region is supposed to larger than that of the suction cups.Furthermore,a robust suction region selection algorithm was developed,which involves four steps:RGB-D information acquisition,object detection and point cloud generation,spherical fitting,and suction region selection.Finally,the above methods were integrated into a robotic packaging system.In addition,on the basis of spatial-frequency domain imaging(SFDI)technology,early stage bruise was detected for validation.The results showed that,the proposed suction strategy and system is potential for robust robotic apple packaging.

Triboelectric nanogenerators for smart agriculture

Rapid iterations of sensing,energy,and communication technologies transform traditional agriculture into standardized,intensive,and smart modern agriculture.However,the energy supply challenge for the plentiful sensors or other microdevices constraints the extensive application of intelligent technologies in agriculture.Triboelectric nanogenerator(TENG),which efficiently converts mechanical energy into electrical energy through contact electrification and electrostatic induction,is considered a promising way to build next-generation intelligent energy supply networks.By efficiently harvesting low-frequency mechanical energy from the agricultural environment,including wind,rain,and water flow energy,TENGs can be a strong contender for distributed power for microdevice networks in smart agriculture.In addition,highly customizable TENGs can be combined with microdevices in agriculture to enable self-powered agricultural monitoring and production strategy adjustment.By deeply exploring the application potential of TENG in agriculture,it is conducive to further promoting unmanned production,refinement,and intelligence of agricultural production and enhancing agriculture's ability to combat natural risks.

Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security.While the data-driven deep learning approach has shown great capacity in predicting yield patterns,its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown.In this study,we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018,with a special focus on the extreme yield loss in 2012.We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018(R^(2)=0.81)and well reproduced the extreme yield anomalies in 2012(R^(2)=0.79).Further attribution analysis indicated that extreme heat stress was the major cause for yield loss,contributing to 72.5%of the yield loss,followed by anomalies of vapor pressure deficit(17.6%)and precipitation(10.8%).Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012.Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.

Factors influencing near infrared spectroscopy analysis of agro-products: a review

The near infrared (NIR) spectroscopy technique has wide applications in agriculture with the advantages of being nondestructive, sensitive, safe and rapid. However, there are still more than 40 error sources influencing the robustness and accuracy of its calibration and operation. Environmental, sample and instrument factors that influence the analysis are discussed in this review, including temperature, humidity and other factors that introduce uncertainty. Error sources from livestock products, fruit and vegetables, which are the most common objects in the field of NIR analysis, are also emphasized in the second part. In addition, studies utilizing different instruments, spectral pretreatments, variable selection methods, wavelength ranges, detection modes and calibration methods are tabulated to illustrate the complications they introduce and how they influence NIR analysis. It is suggested that large scale of data with abundant varieties can be used to build a more robust calibration model, in order to improve the robustness and accuracy of the NIR analytical model, and overcome problems caused by confining analysis to too many uniform samples.

Plant-protein-enabled biodegradable triboelectric nanogenerator for sustainable agriculture

As the use of triboelectric nanogenerators(TENGs)increases,the generation of related electronic waste has been a major challenge.Therefore,the development of environmentally friendly,biodegradable,and low-cost TENGs must be prioritized.Having discovered that plant proteins,by-products of grain processing,possess excellent triboelectric properties,we explore these properties by evaluating the protein structure.The proteins are recycled to fabricate triboelectric layers,and the triboelectric series according to electrical properties is determined for the first time.Using a special structure design,we construct a plant-protein-enabled biodegradable TENG by integrating a polylactic acid film,which is used as a new type of mulch film to construct a growth-promoting system that generates space electric fields for agriculture.Thus,from the plant protein to the crop,a sustainable recycling loop is implemented.Using bean seedlings as a model to confirm the feasibility of the mulch film,we further use it in the cultivation of greenhouse vegetables.Experimental results demonstrate the applicability of the proposed plant-protein-enabled biodegradable TENG in sustainable agriculture.

An integrated and robust plant pulse monitoring system based on biomimetic wearable sensor

Plant wearable sensors have potential to provide continuous measurements of plant physiological information.However,stable and high-fidelity monitoring of plants with glandular hairs and wax is challenging,due to lacking interface adaptability of conventional plant wearable sensors.Here,inspired by adaptive winding plant tendrils,an integrated plant wearable system(IPWS)based on adaptive winding strain(AWS)sensor for plant pulse monitoring was developed.The IPWS consists of three modules,i.e.an AWS sensor,a flexible printed circuit,and a smart phone APP display interface.As the key element,the AWS sensor can adaptively wrap around the tomato stem.Importantly,with the serpentine-patterned laser-induced graphene,the AWS sensor exhibits excellent resistance to temperature interference with a temperature resistance coefficient of 0.17/℃.The IPWS is demonstrated to be stable and high-fidelity monitoring the plant pulse,which can reflect the growth and water state of tomato plant in real time.

Precautionary analysis of sprouting potato eyes using hyperspectral imaging technology

Sprouted potatoes are not allowed for healthy diet.A good knowledge of the sprouting stage of potatoes can help manage the storage conditions and guide market distribution,thus enabling the quality assurance of potatoes on table.This article presented an intelligent method for precautionary analysis of potato eyes based on hyperspectral imaging technique.Potential potato eyes were classified into two categories according to the time gap to the sprouting date,i.e.by-sprouting and pre-sprouting potato eyes,representing eyes about to sprout and eyes that will take a while to sprout.Features used for classification were extracted by two methods,including successive projections algorithm(SPA)and a newly-developed sine fit algorithm(SFA).Then classifiers of fisher discriminant analysis(FDA)and least square support vector machine(LSSVM)were utilized for classification of potential sprouting potato eyes.Results showed that FDA was more effective than LSSVM in classifying pre-sprouting and by-sprouting potato eyes,and SFA performed well in FDA classifier with the recognition accuracy of 95.3%for prediction set.It is concluded that hyperspectral imaging has the potential for predicting the sprouting stages of potato eyes.