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Abstract In point‐of‐care diagnostics, the continuous monitoring of sweat constituents provides a window into individual's physiological state. For species like horses, with abundant sweat glands, sweat composition can serve as an early health indicator. Considering the salience of such metrics in the domain of high‐value animal breeding, a sophisticated wearable sensor patch tailored is introduced for the dynamic assessment of equine sweat, offering insights into pH, potassium ion (K+), and temperature profiles during episodes of heat stress and under normal physiological conditions. The device integrates a laser‐engraved graphene (LEG) sensing electrode array, a non‐invasive iontophoretic module for stimulated sweat secretion, an adaptable signal processing unit, and an embedded wireless communication framework. Profiting from an admirable Truth Table capable of logical evaluation, the integrated system enabled the early and timely assessment for heat stress, with high accuracy, stability, and reproducibility. The sensor patch has been calibrated to align with the unique dermal and physiological contours of equine anatomy, thereby augmenting its applicability in practical settings. This real‐time analysis tool for equine perspiration stands to revolutionize personalized health management approaches for high‐value animals, marking a significant stride in the integration of smart technologies within the agricultural sector.

Abstract Wearable sensors hold immense potential for real‐time and non‐destructive sensing of volatile organic compounds (VOCs), requiring both efficient sensing performance and robust mechanical properties. However, conventional colorimetric sensor arrays, acting as artificial olfactory systems for highly selective VOC profiling, often fail to meet these requirements simultaneously. Here, a high‐performance wearable sensor array for VOC visual detection is proposed by extrusion printing of hybrid inks containing surface‐functionalized sensing materials. Surface‐modified hydrophobic polydimethylsiloxane (PDMS) improves the humidity resistance and VOC sensitivity of PDMS‐coated dye/metal‐organic frameworks (MOFs) composites. It also enhances their dispersion within liquid PDMS matrix, thereby promoting the hybrid liquid as high‐quality extrusion‐printing inks. The inks enable direct and precise printing on diverse substrates, forming a uniform and high particle‐loading (70 wt%) film. The printed film on a flexible PDMS substrate demonstrates satisfactory flexibility and stretchability while retaining excellent sensing performance from dye/MOFs@PDMS particles. Further, the printed sensor array exhibits enhanced sensitivity to sub‐ppm VOC levels, remarkable resistance to high relative humidity (RH) of 90%, and the differentiation ability for eight distinct VOCs. Finally, the wearable sensor proves practical by in situ monitoring of wheat scab‐related VOC biomarkers. This study presents a versatile strategy for designing effective wearable gas sensors with widespread applications.

Highlights The materials, fabrication methods, implantation technologies, and applications of implantable electrochemical microsensors for animals were summarized. The implantable electrochemical microsensors for monitoring diseases and exploring disease mechanisms were discussed. The current status, ongoing challenges, and future development prospects of implantable electrochemical microsensors in monitoring animal physiological information were highlighted.

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 (R2 = 0.81) and well reproduced the extreme yield anomalies in 2012 (R2 = 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.

Manipulating cells at a small scale is widely acknowledged as a complex and challenging task, especially when it comes to cell grasping and transportation. Various precise methods have been developed to remotely control the movement of microrobots. However, the manipulation of micro-objects necessitates the use of end-effectors. This paper presents a study on the control of movement and grasping operations of a magnetic microrobot, utilizing only 3 pairs of electromagnetic coils. A specially designed microgripper is employed on the microrobot for efficient cell grasping and transportation. To ensure precise grasping, a bending deformation model of the microgripper is formulated and subsequently validated. To achieve precise and reliable transportation of cells to specific positions, an approach that combines an extended Kalman filter with a model predictive control method is adopted to accomplish the trajectory tracking task. Through experiments, we observe that by applying the proposed control strategy, the mean absolute error of path tracking is found to be less than 0.155 mm. Remarkably, this value accounts for only 1.55% of the microrobot’s size, demonstrating the efficacy and accuracy of our control strategy. Furthermore, an experiment involving the grasping and transportation of a zebrafish embryonic cell (diameter: 800 μm) is successfully conducted. The results of this experiment not only validate the precision and effectiveness of the proposed microrobot and its associated models but also highlight its tremendous potential for cell manipulation in vitro and in vivo.

Abstract Wet-chemical synthesis via heating bulk solution is powerful to obtain nanomaterials. However, it still suffers from limited reaction rate, controllability, and massive consumption of energy/reactants, particularly for the synthesis on specific substrates. Herein, we present an innovative wet-interfacial Joule heating (WIJH) approach to synthesize various nanomaterials in a sub-second ultrafast, programmable, and energy/reactant-saving manner. In the WIJH, Joule heat generated by the graphene film (GF) is confined at the substrate-solution interface. Accompanied by instantaneous evaporation of the solvent, the temperature is steeply improved and the precursors are concentrated, thereby synergistically accelerating and controlling the nucleation and growth of nanomaterials on the substrate. WIJH leads to a record high crystallization rate of HKUST-1 (~1.97 μm s−1), an ultralow energy cost (9.55 × 10−6 kWh cm−2) and low precursor concentrations, which are up to 5 orders of magnitude faster, −6 and −2 orders of magnitude lower than traditional methods, respectively. Moreover, WIJH could handily customize the products’ amount, size, and morphology via programming the electrified procedures. The as-prepared HKUST-1/GF enables the Joule-heating-controllable and low-energy-required capture and liberation towards CO2. This study opens up a new methodology towards the superefficient synthesis of nanomaterials and solvent-involved Joule heating.

Artificial scent screening systems, inspired by the mammalian olfactory system, hold promise for fruit ripeness detection, but their commercialization is limited by low sensitivity or pattern recognition inaccuracy. This study presents a portable fruit ripeness prediction system based on colorimetric sensing combinatorics and deep convolutional neural networks (DCNN) to accurately identify fruit ripeness. Using the gas chromatography–mass spectrometry (GC-MS) method, the study discerned the distinctive gases emitted by mango, peach, and banana across various ripening stages. The colorimetric sensing combinatorics utilized 25 dyes sensitive to fruit volatile gases, generating a distinct scent fingerprint through cross-reactivity to diverse concentrations and varieties of gases. The unique scent fingerprints can be identified using DCNN. After capturing colorimetric sensor image data, the densely connected convolutional network (DenseNet) was employed, achieving an impressive accuracy rate of 97.39% on the validation set and 82.20% on the test set in assessing fruit ripeness. This fruit ripeness prediction system, coupled with a DCNN, successfully addresses the issues of complex pattern recognition and low identification accuracy. Overall, this innovative tool exhibits high accuracy, non-destructiveness, practical applicability, convenience, and low cost, making it worth considering and developing for fruit ripeness detection.

Abstract Chemiluminescence (CL) imaging, as an excitation‐free technique, exhibits a markedly improved signal‐to‐noise ratio (SNR) owing to the absence of an excitation light source and autofluorescence interference. However, conventional chemiluminescence imaging generally focuses on the visible and first near‐infrared (NIR‐I) regions, which hinders high‐performance biological imaging due to strong tissue scattering and absorption. To address the issue, self‐luminescent NIR‐II CL nanoprobes with a second near‐infrared (NIR‐II) luminescence in the presence of hydrogen peroxide are rationally designed. A cascade energy transfer, including chemiluminescence resonance energy transfer (CRET) from the chemiluminescent substrate to NIR‐I organic molecules and Förster resonance energy transfer (FRET) from NIR‐I organic molecules to NIR‐II organic molecules, occurs in the nanoprobes, contributing to NIR‐II light with great efficiency and good tissue penetration depth. Based on excellent selectivity, high sensitivity to hydrogen peroxide, and long‐lasting luminescence performance, the NIR‐II CL nanoprobes are applied to detect inflammation in mice, showing a 7.4‐fold enhancement in SNR compared with that of fluorescence.

Abstract The mechanical properties of cells play an important role in cell development and function. Therefore, measurement of cell mechanical properties is a fundamental and essential tool for cell research. In this article, a novel method to estimate the force exerted on a living cell is proposed based on glass needle deformation, which does not require additional physical sensors compared with other force‐sensing methods. The three‐dimensional (3D) spatial state of the needle is reconstructed, and the parameters of needle deflection are obtained based on a multi‐focus image fusion algorithm. The average reconstruction error of this algorithm is 0.94 µm. Based on the deformation of the needle, a mechanical model of needle deformation is established, and the model is calibrated using a constructed calibration system. At the range of 0–200 µN, the highest resolution is 0.002 µN and the lowest resolution is 5.3 µN. The proposed method can be used to estimate the force exerted by a needle on the surface of a living cell.

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.

Plant phenotyping and production management are emerging fields to facilitate Genetics, Environment, & Management (GEM) research and provide production guidance. Precision indoor farming systems (PIFS), vertical farms with artificial light (aka plant factories) in particular, have long been suitable production scenes due to the advantages of efficient land utilization and year-round cultivation. In this study, a mobile robotics platform (MRP) within a commercial plant factory has been developed to dynamically understand plant growth and provide data support for growth model construction and production management by periodical monitoring of individual strawberry plants and fruit. Yield monitoring, where yield = the total number of ripe strawberry fruit detected, is a critical task to provide information on plant phenotyping. The MRP consists of an autonomous mobile robot (AMR) and a multilayer perception robot (MPR), i.e., MRP = the MPR installed on top of the AMR. The AMR is capable of traveling along the aisles between plant growing rows. The MPR consists of a data acquisition module that can be raised to the height of any plant growing tier of each row by a lifting module. Adding AprilTag observations (captured by a monocular camera) into the inertial navigation system to form an ATI navigation system has enhanced the MRP navigation within the repetitive and narrow physical structure of a plant factory to capture and correlate the growth and position information of each individual strawberry plant. The MRP performed robustly at various traveling speeds with a positioning accuracy of 13.0 mm. The temporal–spatial yield monitoring within a whole plant factory can be achieved to guide farmers to harvest strawberries on schedule through the MRP’s periodical inspection. The yield monitoring performance was found to have an error rate of 6.26% when the plants were inspected at a constant MRP traveling speed of 0.2 m/s. The MRP’s functions are expected to be transferable and expandable to other crop production monitoring and cultural tasks.

Abstract 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/°C. 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.

High-precision printing of flexible wireless electronics has not been achieved before. Here, the authors leverage a room-temperature direct printing strategy to realize an all-MXene-printed integrated system capable of wireless communication, energy harvesting, and smart sensing.

Abstract 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.

Fresh weight is a widely used growth indicator for quantifying crop growth. Traditional fresh weight measurement methods are time-consuming, laborious, and destructive. Non-destructive measurement of crop fresh weight is urgently needed in plant factories with high environment controllability. In this study, we proposed a multi-modal fusion based deep learning model for automatic estimation of lettuce shoot fresh weight by utilizing RGB-D images. The model combined geometric traits from empirical feature extraction and deep neural features from CNN. A lettuce leaf segmentation network based on U-Net was trained for extracting leaf boundary and geometric traits. A multi-branch regression network was performed to estimate fresh weight by fusing color, depth, and geometric features. The leaf segmentation model reported a reliable performance with a mIoU of 0.982 and an accuracy of 0.998. A total of 10 geometric traits were defined to describe the structure of the lettuce canopy from segmented images. The fresh weight estimation results showed that the proposed multi-modal fusion model significantly improved the accuracy of lettuce shoot fresh weight in different growth periods compared with baseline models. The model yielded a root mean square error (RMSE) of 25.3 g and a coefficient of determination (R2) of 0.938 over the entire lettuce growth period. The experiment results demonstrated that the multi-modal fusion method could improve the fresh weight estimation performance by leveraging the advantages of empirical geometric traits and deep neural features simultaneously.

Abstract Metamaterials have attracted increasing attention in sensing applications. However, the critical feature sizes of meta‐atom span several orders of magnitude in length scale, almost all the metamaterials are designed to operate at limited bands. It is challenging for a single type of meta‐atom with ultra‐broadband adaptability. Inspired by the natural hierarchical architectures, herein, the authors introduce a new constructing scheme of cross‐wavelength hierarchical metamaterials with a single type of meta‐atom that can realize enhancement of terahertz (THz) resonance and surface‐enhanced Raman scattering (SERS) at the same time. By combining multiple subwavelength structures at different hierarchical levels into a single meta‐atom, the obtained metamaterial can operate in two frequencies and realize multiple functionalities. Armed with this hierarchical metamaterial, detecting analytes as small as sub‐nanoscale chemical molecules or as big as microscale biomolecules simultaneously can be realized in one single metamaterial for the first time. As a proof‐of‐concept example, a smart sensory packaging is developed, which allowed them to real‐time monitor the kinetic growth of pathogenic bacteria and their metabolites in food without opening the packaging. They believe that their work will provide a valuable example that satisfies the unmet need for multiscale functional meta‐devices.

Abstract Flexible devices serve as important intelligent interfaces in various applications involving health monitoring, biomedical therapies, and human–machine interfacing. To address the concern of electronic waste caused by the increasing usage of electronic devices based on synthetic polymers, bio‐origin materials that possess environmental benignity as well as sustainability offer new opportunities for constructing flexible electronic devices with higher safety and environmental adaptivity. Herein, the bio‐source and unique molecular structures of various types of natural bio‐origin materials are briefly introduced. Their properties and processing technologies are systematically summarized. Then, the recent progress of these materials for constructing emerging intelligent flexible electronic devices including energy harvesters, energy storage devices, and sensors are introduced. Furthermore, the applications of these flexible electronic devices including biomedical implants, artificial e‐skin, and environmental monitoring are summarized. Finally, future challenges and prospects for developing high‐performance bio‐origin material‐based flexible devices are discussed. This review aims to provide a comprehensive and systematic summary of the latest advances in the natural bio‐origin material‐based flexible devices, which is expected to offer inspirations for exploitation of green flexible electronics, bridging the gap in future human–machine–environment interactions.

Stereo matching is a depth perception method for plant phenotyping with high throughput. In recent years, the accuracy and real-time performance of the stereo matching models have been greatly improved. While the training process relies on specialized large-scale datasets, in this research, we aim to address the issue in building stereo matching datasets. A semi-automatic method was proposed to acquire the ground truth, including camera calibration, image registration, and disparity image generation. On the basis of this method, spinach, tomato, pepper, and pumpkin were considered for experiment, and a dataset named PlantStereo was built for reconstruction. Taking data size, disparity accuracy, disparity density, and data type into consideration, PlantStereo outperforms other representative stereo matching datasets. Experimental results showed that, compared with the disparity accuracy at pixel level, the disparity accuracy at sub-pixel level can remarkably improve the matching accuracy. More specifically, for PSMNet, the EPE and bad−3 error decreased 0.30 pixels and 2.13%, respectively. For GwcNet, the EPE and bad−3 error decreased 0.08 pixels and 0.42%, respectively. In addition, the proposed workflow based on stereo matching can achieve competitive results compared with other depth perception methods, such as Time-of-Flight (ToF) and structured light, when considering depth error (2.5 mm at 0.7 m), real-time performance (50 fps at 1046 × 606), and cost. The proposed method can be adopted to build stereo matching datasets, and the workflow can be used for depth perception in plant phenotyping.

Abstract Nanofluidic channels based on 2D nanomaterials are promising to harvest osmotic energy for their high ion selectivity and osmotic conductivity. However, anion‐selective nanofluidic channels are rare and chemical modification is necessary through fabrication. Here, a naturally anion‐selective composite membrane is reported, that is, NiAl‐Layered double hydroxide (LDH) coated anodic aluminum oxide (LDH@AAO), using a simple precipitant‐free in situ growth technique. Positively charged LDH plates growing in channels of AAO function as screening layers for anions. Both experiments and theoretical simulations are enforced to certify the vital role of LDH growth in ion distribution and salinity gradient energy conversion. The composite membrane achieves high output performance and long‐term stability. Furthermore, novel applications of nanofluidic channels are explored in hydroponic production and design a real‐time detecting system based on LDH@AAO composite membranes for nutrient solution. This work provides insights into naturally anion‐selective nanofluidic channels for osmotic energy harvesting and broadens the application in agricultural information sensing.

Abstract Plants have complex internal signaling pathways to quickly adjust to environmental changes and harvest energy from the environment. Facing the growing population, there is an urgent need for plant transformation and precise monitoring of plant growth to improve crop yields. Nanotechnology, an interdisciplinary research field, has recently been boosting plant yields and meeting global energy needs. In this context, a new field, “plant nanoscience,” which describes the interaction between plants and nanotechnology, emerges as the times require. Nanosensors, nanofertilizers, nanopesticides, and nano‐plant genetic engineering are of great help in increasing crop yields. Nanogenerators are helping to develop the potential of plants in the field of energy harvesting. Furthermore, the uptake and internalization of nanomaterials in plants and the possible effects are also worthy of attention. In this review, a forward‐looking perspective on the plant nanoscience is presented and feasible solutions for future food shortages and energy crises are provided.

Abstract The boom of plant phenotype highlights the need to measure the physiological characteristics of an individual plant. However, continuous real‐time monitoring of a plant's internal physiological status remains challenging using traditional silicon‐based sensor technology, due to the fundamental mismatch between rigid sensors and soft and curved plant surfaces. Here, the first flexible electronic sensing device is reported that can harmlessly cohabitate with the plant and continuously monitor its stem sap flow, a critical plant physiological characteristic for analyzing plant health, water consumption, and nutrient distribution. Due to a special design and the materials chosen, the realized plant‐wearable sensor is thin, soft, lightweight, air/water/light‐permeable, and shows excellent biocompatibility, therefore enabling the sap flow detection in a continuous and non‐destructive manner. The sensor can serve as a noninvasive, high‐throughput, low‐cost toolbox, and holds excellent potentials in phenotyping. Furthermore, the real‐time investigation on stem flow insides watermelon reveals a previously unknown day/night shift pattern of water allocation between fruit and its adjacent branch, which has not been reported before.

Timely and accurate cropland information at large spatial scales can improve crop management and support the government in decision making. Mapping the spatial extent and distribution of crops on a large spatial scale is challenging work due to the spatial variability. A multi-task spatiotemporal deep learning model, named LSTM-MTL, was developed in this study for large-scale rice mapping by utilizing time-series Sentinel-1 SAR data. The model showed a reasonable rice classification accuracy in the major rice production areas of the U.S. (OA = 98.3%, F1 score = 0.804), even when it only utilized SAR data. The model learned region-specific and common features simultaneously, and yielded a significant improved performance compared with RF and AtBiLSTM in both global and local training scenarios. We found that the LSTM-MTL model achieved a regional F1 score up to 10% higher than both global and local baseline models. The results demonstrated that the consideration of spatial variability via LSTM-MTL approach yielded an improved crop classification performance at a large spatial scale. We analyzed the input-output relationship through gradient backpropagation and found that low VH value in the early period and high VH value in the latter period were critical for rice classification. The results of in-season analysis showed that the model was able to yield a high accuracy (F1 score = 0.746) two months before rice maturity. The integration between multi-task learning and multi-temporal deep learning approach provides a promising approach for crop mapping at large spatial scales.

Measurement of optical properties is critical for understanding light-tissue interaction, properly interpreting measurement data, and gaining better knowledge of tissue physicochemical properties. However, conventional optical measuring techniques are limited in point measurement, which partly hinders the applications on characterizing spatial distribution and inhomogeneity of optical properties of biological tissues. Spatial-frequency domain imaging (SFDI), as an emerging non-contact, depth-varying and wide-field optical imaging technique, is capable of measuring the optical properties in a wide field-of-view on a pixel-by-pixel basis. This review first describes the typical SFDI system and the principle for estimating optical properties using the SFDI technique. Then, the applications of SFDI in the fields of biomedicine, as well as food and agriculture, are reviewed, including burn assessment, skin tissue evaluation, tumor tissue detection, brain tissue monitoring, and quality evaluation of agro-products. Finally, a discussion on the challenges and future perspectives of SFDI for optical property estimation is presented.

Terahertz (THz) technique has become one of the most promising analytical methods and has been applied in many fields. Attenuated total reflection (ATR) technique applied in THz spectroscopy and imaging has been proven to be superior in functionalities such as modulation, sensing, analyzing, and imaging. Here, we first provide a concise introduction to the principle of ATR, discuss the factors that impact the ATR system, and demonstrate recent advances on THz wave modulation and THz surface plasmon sensing based on the THz-ATR system. Then, applications on THz-ATR spectroscopy and imaging are reviewed. Towards the later part, the advantages and limitations of THz-ATR are summarized, and prospects of modulation, surface plasmon sensing, spectroscopy and imaging are discussed.

As a sustainable, cost-effective and energy-efficient method, membranes are becoming a progressively vital technique to solve the problem of the scarcity of freshwater resources. With these critical advantages, carbon nanotubes (CNTs) have great potential for membrane desalination given their high aspect ratio, large surface area, high mechanical strength and chemical robustness. In recent years, the CNT membrane field has progressed enormously with applications in water desalination. The latest theoretical and experimental developments on the desalination of CNT membranes, including vertically aligned CNT (VACNT) membranes, composited CNT membranes, and their applications are timely and comprehensively reviewed in this manuscript. The mechanisms and effects of CNT membranes used in water desalination where they offer the advantages are also examined. Finally, a summary and outlook are further put forward on the scientific opportunities and major technological challenges in this field.

Large-scale crop yield estimation is critical for understanding the dynamics of global food security. Understanding and quantifying the temporal cumulative effect of crop growth and spatial variances across different regions remains challenging for large-scale crop yield estimation. In this study, a deep spatial-temporal learning framework, named DeepCropNet (DCN), has been developed to hierarchically capture the features for county-level corn yield estimation. The temporal features are learned by an attention-based long short-term memory network and the spatial features are learned by the multi-task learning (MTL) output layers. The DCN model has been applied to quantify the relationship between meteorological factors and the county-level corn yield in the US Corn Belt from 1981 to 2016. Three meteorological factors, including growing degree days, killing degree days, and precipitation, are used as time-series inputs. The results show that DCN provides an improved estimation accuracy (RMSE = 0.82 Mg ha ^−1 ) as compared to that of conventional methods such as LASSO (RMSE = 1.14 Mg ha ^−1 ) and Random Forest (RMSE = 1.05 Mg ha ^−1 ). Temporally, the attention values computed from the temporal learning module indicate that DCN captures the temporal cumulative effect and this temporal pattern is consistent across all states. Spatially, the spatial learning module improves the estimation accuracy based on the regional specific features captured by the MTL mechanism. The study highlights that the DCN model provides a promising spatial-temporal learning framework for corn yield estimation under changing meteorological conditions across large spatial regions.

A detection method for 3 kinds of phenolic compounds of endocrine disruptors (4-nonylphenol, 4-octylphenol, and bisphenol A) in infant milk powders by solid-phase extraction combined GC-MS was established. This method can effectively remove interference materials from infant milk powder products. The limit of detection and the limit of quantitation of the 3 kinds of compounds were 0.8 μg/kg and 2.5 μg/kg, respectively, with the relative standard deviations of 4.3–12.1%. The recovery rates of 4-nonylphenol, 4-octylphenol, and bisphenol A were of 68.5–89.2%, 64.8–87.0%, and 97.8–110.0%, respectively. Concentrations of the bisphenol A were from 0.8 to 14 μg/kg in 35 samples of the total 60 samples. And the other two compounds of 4-nonylphenol and 4-octylphenol were not found in all the 60 samples tested. The established method is simple, rapid, accurate, and highly sensitive, and the pollution of endocrine disruptors in some infant milk powders products was detectable in trace amounts.

Battery production is one of the main sources of heavy metals that present great harm to human health even in low concentrations. Chromium (Cr), Cadmium (Cd) and Lead (Pb) were measured in edible portions of vegetables and soils around a battery production area in China, and the potential health risk of heavy metal contamination to the local population via vegetable consumption was evaluated. Their concentrations in edible portions of vegetables were 2.354 (0.078-14.878), 0.035 (0.003-0.230) and 0.039 (0.003-0.178) mg kg-1, respectively. Approximately 3 % of the Cd in the vegetable samples exceeded the maximum concentration allowable by national food safety criteria, although Pb content in all samples were within the criteria. Transfer factors (TF) from soils to vegetables were dependent on vegetable species. Leguminous vegetables were more likely to accumulate Cr, while leaf vegetables tended to show higher levels of concentration of Cd and Pb. Melon vegetables demonstrated a relatively low capacity for accumulating the heavy metals studied. TF were positively correlated with soil organic matter and negatively correlated with soil pH. The mean estimated daily intake of Cr, Cd and Pb via dietary consumption of vegetables was 0.011, 1.65 × 10-4 and 1.84 × 10-4 mg kg-1 of body weight per day, respectively, levels that were much lower than the reference doses recommended by USEPA (U.S. Environmental Protection Agency) and JECFA (Joint FAO/WHO Expert Committee on Food Additives), indicating that the potential health risk of Cr, Cd and Pb exposure via vegetable consumption to the local population around this battery production area could be negligible.

The development of a rapid, sensitive, specific method for the foodborne pathogenic bacteria detection is of great importance to ensure food safety and security. In recent years impedimetric biosensors which integrate biological recognition technology and impedance have gained widespread application in the field of bacteria detection. This paper presents an overview on the progress and application of impedimetric biosensors for detection of foodborne pathogenic bacteria, particularly the new trends in the past few years, including the new specific bio-recognition elements such as bacteriophage and lectin, the use of nanomaterials and microfluidics techniques. The applications of these new materials or techniques have provided unprecedented opportunities for the development of high-performance impedance bacteria biosensors. The significant developments of impedimetric biosensors for bacteria detection in the last five years have been reviewed according to the classification of with or without specific bio-recognition element. In addition, some microfluidics systems, which were used in the construction of impedimetric biosensors to improve analytical performance, are introduced in this review.

A surface plasmon resonance (SPR) immunosensor was developed for the detection of E. coli O157:H7 by means of a new subtractive inhibition assay. In the subtractive inhibition assay, E. coli O157:H7 cells and goat polyclonal antibodies for E. coli O157:H7 were incubated for a short of time, and then the E. coli O157:H7 cells which bound antibodies were removed by a stepwise centrifugation process. The remaining free unbound antibodies were detected through interaction with rabbit anti-goat IgG polyclonal antibodies immobilized on the sensor chip using a BIAcore 3000 biosensor. The results showed that the signal was inversely correlated with the concentration of E. coli O157:H7 cells in a range from 3.0 × 104 to 3.0 × 108 cfu/mL with a detection limit of 3.0 × 104 cfu/mL. Compared with direct SPR by immobilizing antibodies on the chip surface to capture the bacterial cells and ELISA for E. coli O157:H7 (detection limit: both 3.0 × 105 cfu/mL in this paper), the detection limit of subtractive inhibition assay method was reduced by one order of magnitude. The method simplifies bacterial cell detection to protein-protein interaction, which has the potential for providing a practical alternative for the monitoring of E. coli O157:H7 and other pathogens.

As knowledge of the structure and function of nucleic acid molecules has increased, sequence-specific DNA detection has gained increased importance. DNA biosensors based on nucleic acid hybridization have been actively developed because of their specificity, speed, portability, and low cost. Recently, there has been considerable interest in using nano-materials for DNA biosensors. Because of their high surface-to-volume ratios and excellent biological compatibilities, nano-materials could be used to increase the amount of DNA immobilization; moreover, DNA bound to nano-materials can maintain its biological activity. Alternatively, signal amplification by labeling a targeted analyte with nano-materials has also been reported for DNA biosensors in many papers. This review summarizes the applications of various nano-materials for DNA biosensors during past five years. We found that nano-materials of small sizes were advantageous as substrates for DNA attachment or as labels for signal amplification; and use of two or more types of nano-materials in the biosensors could improve their overall quality and to overcome the deficiencies of the individual nano-components. Most current DNA biosensors require the use of polymerase chain reaction (PCR) in their protocols. However, further development of nano-materials with smaller size and/or with improved biological and chemical properties would substantially enhance the accuracy, selectivity and sensitivity of DNA biosensors. Thus, DNA biosensors without PCR amplification may become a reality in the foreseeable future.

Skinning injury on potato tubers is a kind of superficial wound that is generally inflicted by mechanical forces during harvest and postharvest handling operations. Though skinning injury is pervasive and obstructive, its detection is very limited. This study attempted to identify injured skin using two CCD (Charge Coupled Device) sensor-based machine vision technologies, i.e., visible imaging and biospeckle imaging. The identification of skinning injury was realized via exploiting features extracted from varied ROIs (Region of Interests). The features extracted from visible images were pixel-wise color and texture features, while region-wise BA (Biospeckle Activity) was calculated from biospeckle imaging. In addition, the calculation of BA using varied numbers of speckle patterns were compared. Finally, extracted features were implemented into classifiers of LS-SVM (Least Square Support Vector Machine) and BLR (Binary Logistic Regression), respectively. Results showed that color features performed better than texture features in classifying sound skin and injured skin, especially for injured skin stored no less than 1 day, with the average classification accuracy of 90%. Image capturing and processing efficiency can be speeded up in biospeckle imaging, with captured 512 frames reduced to 125 frames. Classification results obtained based on the feature of BA were acceptable for early skinning injury stored within 1 day, with the accuracy of 88.10%. It is concluded that skinning injury can be recognized by visible and biospeckle imaging during different stages. Visible imaging has the aptitude in recognizing stale skinning injury, while fresh injury can be discriminated by biospeckle imaging.