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[学术文献 ] Which and how many soil sensors are ideal to predict key soil properties: A case study with seven sensors 进入全文
GEODERMA
Soil sensing enables rapid and cost-effective soil analysis. However, a single sensor often does not generate enough information to reliably predict a wide range of soil properties. Within a case-study, our objective was to identify how many and which combinations of soil sensors prove to be suitable for high-resolution soil mapping. On a subplot of an agricultural field showing a high spatial soil variability, six in-situ proximal soil sensors (PSSs) next to remote sensing (RS) data from Sentinel-2 were evaluated based on their capabilities to predict a set of soil properties including: soil organic carbon, pH, moisture as well as plant-available phosphorus, magnesium and potassium. The set of PSSs consisted of ion-selective pH electrodes, a capacitive soil moisture sensor, an apparent soil electrical conductivity measuring system as well as passive gamma-ray-, X-ray fluorescence- and near-infrared spectroscopy. All possible combinations of sensors were exhaustively evaluated and ranked based on their prediction performances using model stacking. Over all soil properties, data fusion demonstrated a considerable increase in prediction accuracy. Five out of six soil properties were predicted with an R2 ≥ 0.80 with the best sensor fusion model. Nonetheless, the improvement derived from fusing an increasing number of PSSs was subject to diminishing returns. Sometimes adding more PSSs even decreased prediction performances. Gamma-ray spectroscopy and near-infrared spectroscopy demonstrated to be most effective, both as single sensors or in combination with other sensors. As a single sensor, RS outperformed three out of six PSSs. RS showed especially potential for fusion with single PSSs but was of limited benefit when multiple PSSs were fused. Model stacking proved to be more robust than using single base-models because sensor performances were less model-dependent.
[学术文献 ] Sensitive and specific detection of Listeria monocytogenes in food samples using imprinted upconversion fluorescence probe prepared by emulsion polymerization method 进入全文
FOOD CHEMISTRY-X
Listeria monocytogenes (L. monocytogenes) is a foodborne pathogen with high morbidity and mortality rates, necessitating rapid detection methods. Current techniques, while reliable, are labor-intensive and not amenable to on-site testing. We report the design and synthesis of a novel imprinted upconversion fluorescence probe through Pickering emulsion polymerization for the specific detection of L. monocytogenes. The probe employs trimethylolpropane trimethacrylate and divinylbenzene as cross-linkers, acryloyl-modified chitosan as a functional monomer, and the bacterium itself as the template. The developed probe demonstrated high specificity and sensitivity in detecting L. monocytogenes, with a limit of detection of 72 CFU/mL. It effectively identified the pathogen in contaminated salmon and chicken samples, with minimal background interference. The integration of molecular imprinting and upconversion fluorescence materials presents a potent and reliable approach for the rapid and specific detection of L. monocytogenes, offering considerable potential for on-site food safety testing.
[学术文献 ] Sample-in-answer-out centrifugal microfluidic chip reaction biosensor powered by Thermus thermophilus Argonaute (TtAgo) for rapid, highly sensitive and multiplexed molecular diagnostics of foodborne bacterial pathogens 进入全文
CHEMICAL ENGINEERING JOURNAL
Foodborne pathogens endanger public health and rapid, sensitive and accurate detection of them is vital. Argonaute stands in the frontline as the next generation nucleic acid detection tool, bearing a few comparable advantages. Centrifugal microfluidic chips (CMCs) use centrifugal force to achieve liquid flowing, mixing and reaction, eliminating complicated designs of valves and pumps. In this study, for the first time, we devised a Thermus thermophilus Argonaute (TtAgo)-powered centrifugal microfluidic chip reaction biosensor for Sample-inAnswer-out detection of Pathogen S. aureus (termed as ASAP) with ultrahigh rapidity and sensitivity in a one-pot fashion. The samples subjected to testing were mixed with a home-made nucleic acid fast extraction reagent and then the mixture was injected into the sample cell of CMC, which was centrifuged down into the reaction cell. The reaction cell was preloaded with both LAMP (Loop-mediated isothermal amplification) and TtAgo systems. As such, the species-specific nuc genes were amplified by LAMP, while the TtAgo performed site-specific cleavage to output fluorescent (FL) signals. The sample-to-answer time was 17 min, and the limit of detection (LOD) reached 1 CFU/mL. ASAP was able to perform simultaneous multiplexed detection and up to 16 samples can be detected at the same time. ASAP reduced reagent consumption and minimized the influence of carry-over and cross-contamination. ASAP was capable of detecting S. aureus in foods, but also detecting physiological samples from infect, holding great promise for practical applications. Overall, our work has enriched the Argonautepowered biosensing and CMC biosensor technology by providing a conceptually novel bacterial detection platform.
[学术文献 ] YOLO算法在动植物表型研究中应用综述 进入全文
农业机械学报
动植物表型是动植物特征与性状的定量描述，表型特征的精准计算与分析是推进数字农业发展的重要基础。得益于深度学习技术的迅猛发展，以YOLO系列算法为代表的计算机视觉模型在动植物表型分析任务中展现出了优良性能和巨大潜力。以家畜类、家禽类、作物类、果蔬类等动植物为对象，分别从目标检测、关键点检测、目标分割3方面概述了YOLO系列算法应用研究进展。最后指出YOLO系列算法未来发展趋势，包括轻量化架构设计、小目标精准检测、弱监督学习、复杂场景部署、大模型目标检测等。
[学术文献 ] 数智农田构建关键技术装备及展望 进入全文
农业工程学报
现代数智化技术装备使农田生产管理场景多元化，该研究依托中国农业大学超高产种植制度科技小院的智慧技术示范基地，集成大数据、物联网、云计算和人工智能等技术，应用农情监测站、水肥微喷带灌溉、病虫害管理、智能作业装备、种植管理及溯源等系统，建立人‒机‒作物全要素生产的监测、管控和决策云平台，构建作物‒农艺‒农机信息融合模式，实现农作物的生产信息采集、科学种植、机械作业、智能管理及追溯，达到了农作物质优产高、高效安全的生产目标。构建的数智农田较传统农田减少人力成本约50%，农药减量约30%，肥料节省15%～25%，较常规灌溉节水50%、滴灌节水20%～30%，实现单位面积增产约15%～25%、增效约25%，解决了传统农田的作物信息获取难、管护不及时，机械化程度低等问题，基本实现了农田作物生产的信息化、机械化和智能化作业。
[学术文献 ] A disposable fiber-optic plasmonic sensor for chemical sensing 进入全文
ANALYTICAL BIOCHEMISTRY
The integration of fiber optics and plasmonic sensors is promising to improve the practical usability over conventional bulky sensors and systems. To achieve high sensitivity, it typically requires fabrication of well-defined plasmonic nanostructures on optical fibers, which greatly increases the cost and complexity of the sensors. Here, we present a fiber-optic sensor system by using chemical absorption of gold nanoparticles and a replaceable configuration. By functioning gold nanoparticles with aptamers or antibodies, we demonstrate the applications in chemical sensing using two different modes. Measuring shift in resonance wavelength enables the Pb2+ detection with a high linearity and a limit of detection of 0.097 nM, and measuring absorption peak amplitude enables the detection of E. coli in urinary tract infection with a dynamic range between 10(3) to 10(8) CFU/mL. The high sensitivity, simple fabrication and disposability of this sensing approach could pave the way for point-of-care testing with fiber-optic plasmonic sensors.
