Potato is one of the key crops for ensuring food security and can be a potential substitute for cereal crops due to its high yielding nature and nutritional value.Crop nutrient management practices within potato field...Potato is one of the key crops for ensuring food security and can be a potential substitute for cereal crops due to its high yielding nature and nutritional value.Crop nutrient management practices within potato fields are implemented uniformly without considering crop requirements and soil variability,causing uneven and low yield.However,yield can be increased by identifying growth and yield-limiting factors.Geospatial tools are robust and effective in identifying the spatial variations within the field.Proximal sensing allows quick analysis of soil and plant characteristics,decreases the need for laborious and expensive soil and plant sampling,and strengthens precision agriculture techniques.The aim of the study was to quantify the soil spatial variability and identify potato crop growth and yield limiting factors for the optimization of inputs.Two fields were selected in the subtropical region of Pakistan(Koont,Rawalpindi),and each field was cultivated with two different potato varieties.A grid sampling approach was developed to collect soil samples and tuber yields.The soil was tested for nitrogen(N),phosphorus(P),potassium(K),pH,electrical conductivity(E.C),temperature,and moisture content(M.C)by using a soil proximal sensor.Normalized difference vegetation index(NDVI)was recorded using a handheld GreenSeeker,and chlorophyll was estimated using a chlorophyll meter.Descriptive statistics and correlation analysis for soil and crop parameters were performed in Minitab 21,while geostatistical analysis was performed in Arc Map 10.8 to show spatial variability and to generate kriged maps of different soil properties.The coefficient of variation of soil properties and plant parameters showed moderate to high variability within the field,except for pH and temperature.The correlation matrix suggested that N,P,K,E.C.,chlorophyll,NDVI,plant height,and leaf area had a significant relationship with potato yield.Most of the soil and plant parameters had a medium to high range of influence(20 to 90 m)and varied greatly within the field.Kriged maps of plant and soil parameters also showed spatial variations and were aligned with descriptive statistics and correlations.Quantification of soil spatial variability within potato fields can assist in measuring yield-limiting soil characteristics to establish management zones for variable rate fertilization for optimum tuber yield and low environmental impact.展开更多
Fiber sensors are commonly used to detect environmental,physiological,optical,chemical,and biological factors.Thermally drawn fibers offer numerous advantages over other commercial products,including enhanced sensitiv...Fiber sensors are commonly used to detect environmental,physiological,optical,chemical,and biological factors.Thermally drawn fibers offer numerous advantages over other commercial products,including enhanced sensitivity,accuracy,improved functionality,and ease of manufacturing.Multimaterial,multifunctional fibers encapsulate essential internal structures within a microscale fiber,unlike macroscale sensors requiring separate electronic components.The compact size of fiber sensors enables seamless integration into existing systems,providing the desired functionality.We present a multimodal fiber antenna monitoring,in real time,both the local deformation of the fiber and environmental changes caused by foreign objects in proximity to the fiber.Time domain reflectometry propagates an electromagnetic wave through the fiber,allowing precise determination of spatial changes along the fiber with exceptional resolution and sensitivity.Local changes in impedance reflect fiber deformation,whereas proximity is detected through alterations in the evanescent field surrounding the fiber.The fiber antenna operates as a waveguide to detect local deformation through the antisymmetric mode and environmental changes through the symmetric mode.This multifunctionality broadens its application areas from biomedical engineering to cyber-physical interfacing.In antisymmetric mode,the device can sense local changes in pressure,and,potentially,temperature,pH,and other physiological conditions.In symmetric mode,it can be used in touch screens,environmental detection for security,cyber-physical interfacing,and human-robot interactions.展开更多
Frozen soils or those with permafrost cover large areas of the earth's surface and support unique vegetative ecosystems. Plants growing in such harsh conditions have adapted to small niches, which allow them to su...Frozen soils or those with permafrost cover large areas of the earth's surface and support unique vegetative ecosystems. Plants growing in such harsh conditions have adapted to small niches, which allow them to survive. In northern Alaska, USA, both moist acidic and non-acidic tundra occur, yet determination of frozen soil p Hs currently requires thawing of the soil so that electrometric pH methods can be utilized. Contrariwise, a portable X-ray fluorescence(PXRF) spectrometer was used in this study to assess elemental abundances and relate those characteristics to soil pH through predictive multiple linear regressions. Two operational modes, Soil Mode and Geochem Mode, were utilized to scan frozen soils in-situ and under laboratory conditions, respectively, after soil samples were dried and ground. Results showed that lab scanning produced optimal results with adjusted coefficient of determination(R^2) of 0.88 and 0.33 and root mean squared errors(RMSEs) of 0.87 and 0.34 between elemental data and lab-determined pH for Soil Mode and Geochem Mode, respectively. Even though the presence of ice attenuated fluoresced radiation under field conditions, adjusted R^2 and RMSEs between the datasets still provided reasonable model generalization(e.g., 0.73 and 0.49 for field Geochem Mode). Principal component analysis qualitatively separated multiple sampling sites based on elemental data provided by PXRF, reflecting differences in the chemical composition of the soils studied. Summarily, PXRF can be used for in-situ determination of soil pH in arctic environments without the need for sample modification and thawing. Furthermore, use of PXRF for determination of soil pH may provide higher sample throughput than traditional eletrometric-based methods, while generating elemental data useful for the prediction of multiple soil parameters.展开更多
Field portable X-ray fluorescence （PXRF） spectrometry has become an increasingly popular technique for in-situ elemental characterization of soils. The technique is fast, portable, and accurate, requiring minimal sa...Field portable X-ray fluorescence （PXRF） spectrometry has become an increasingly popular technique for in-situ elemental characterization of soils. The technique is fast, portable, and accurate, requiring minimal sample preparation and no consumables. However, soil moisture 〉 20% has been known to cause fluorescence denudation and error in elemental reporting and few studies have evaluated the presence of soil moisture in solid form as ice. Gelisols （USDA Soil Taxonomy）, permafrost-affected soils, cover a large amount of the land surface in the northern and southern hemispheres. Thus, the applicability of PXRF in those areas requires further investigation. PXRF was used to scan the elemental composition （Ba, Ca, Cr, Fe, K, Mn, Pb, Rb, Sr, Ti, Zn, and Zr） of 13 pedons in central and northern Alaska, USA. Four types of scans were completed： 1） in-situ frozen soil, 2） re-frozen soil in the laboratory, 3） melted soil/water mixture in the laboratory, and 4） moisture-corrected soil. All were then compared to oven dry soil scans. Results showed that the majority of PXRF readings from in-situ, re-frozen, and melted samples were significantly underestimated, compared to the readings on oven dry samples, owing to the interference expected by moisture. However, when the moisture contents were divided into 〉 40% and 〈 40〈 groups, the PXRF readings under different scanning conditions performed better in the group with 〈 40% moisture contents. Most elements of the scans on the melted samples with 〈 40% moisture contents acceptably compared to those of the dry samples, with R2 values ranging from 0.446 （Mn） to 0.930 （St）. However, underestimation of the melted samples was still quite apparent. Moisture-corrected sample PXRF readings provided the best correlation to those of the dry, ground samples as indicated by higher R2 values, lower root mean square errors （RMSEs）, and slopes closer to 1 in linear regression equations. However, the in-situ （frozen） sample scans did not differ appreciably from the melted sample scans in their correlations to dry sample scans in terms of R2 values （0.81 vs. 0.88）, RMSEs （1.06 vs. 0.85）, and slopes （0.88 vs. 0.92）. Notably, all of those relationships improved for the group with moisture contents 〈 40%.展开更多
Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification,making it a significant carbon source for soil.Applying biochar to soil is a carbon-negative process that helps combat clima...Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification,making it a significant carbon source for soil.Applying biochar to soil is a carbon-negative process that helps combat climate change,sustain soil biodiversity,and regulate water cycling.However,quantifying soil carbon content conventionally is time-consuming,labor-intensive,imprecise,and expensive,making it difficult to accurately measure in-field soil carbon’s effect on storage water and nutrients.To address this challenge,this paper for the first time,reports on extensive lab tests demonstrating non-intrusive methods for sensing soil carbon and related smart biochar applications,such as differentiating between biochar types from various biomass feedstock species,monitoring soil moisture,and biochar water retention capacity using portable microwave and millimeter wave sensors,and machine learning.These methods can be scaled up by deploying the sensor in-field on a mobility platform,either ground or aerial.The paper provides details on the materials,methods,machine learning workflow,and results of our investigations.The significance of this work lays the foundation for assessing carbon-negative technology applications,such as soil carbon content accounting.We validated our quantification method using supervised machine learning algorithms by collecting real soil mixed with known biochar contents in the field.The results show that the millimeter wave sensor achieves high sensing accuracy(up to 100%)with proper classifiers selected and outperforms the microwave sensor by approximately 10%–15%accuracy in sensing soil carbon content.展开更多
An increase in data availability from different sensors and sources has changed how crop models are being used.Data assimilation is one approach for integrating data and models that has been widely used for field crop...An increase in data availability from different sensors and sources has changed how crop models are being used.Data assimilation is one approach for integrating data and models that has been widely used for field crops but not yet in protected environments.We present a case study of data assimilation in a greenhouse,updating growth estimates of the Reduced State TOMGRO model.We assimilated data obtained through the continuous monitoring of plant mass and images captured by low-cost cameras,using the Unscented Kalman Filter and the Ensemble Kalman Filter.In some cases,assimilation led to improvements of more than 40%in the RMSE of yield estimates of the non-calibrated model,within a validation set.The improvements were more noticeable when there was a need to adjust the estimates to a condition the model does not represent.In these situations,we noted the RMSE decreased by almost 80%,depending on the variable being assimilated.However,in some cases,the results were also impaired by assimilation,and we highlight the impacts on the filter performance caused by the quality of observations and of observation models.Overall,the employed measurements,i.e.,area of organs observed in pictures and plant-water mass,seemed suitable for tracking plant growth and for obtaining good approximations of the state variables estimated by the model.As with other studies,it was not the case that assimilating one state was useful for improving the value of others,including yield.As the first study using filters and non-destructive observations in a process-based crop model in a protected environment,we identified a lot of potential,but to identify the best use of these techniques with real-time data,more studies are needed.By making all data and code from this study available,we hope to ease future research in this area.展开更多
The development of intelligent electronic power systems necessitates advanced flexible pressure sensors.Despite improved compressibility through surface micro-structures or bulk pores,conventional capacitive pressure ...The development of intelligent electronic power systems necessitates advanced flexible pressure sensors.Despite improved compressibility through surface micro-structures or bulk pores,conventional capacitive pressure sensors face limitations due to their low dielectric constant and poor temperature tolerance of most elastomers.Herein,we constructed oriented polyimide-based aerogels with mechanical robustness and notable changes in dielectric constant under compression.The enhancement is attributed to the doping of surface-modified dielectric nanoparticles and graphene oxide sheets,which interact with polymer molecular chains.The resulting aerogels,with their excellent temperature resistance,were used to assemble high-performance capacitive pressure sensors.The sensor exhibits a maximum sensitivity of 1.41 kPa^(−1)over a wide working range of 0-200 kPa.Meanwhile,the sensor can operate in environments up to 150℃during 2000 compression/release cycles.Furthermore,the aerogel-based sensor demonstrates proximity sensing capabilities,showing great potential for applications in non-contact sensing and extreme environment detection.展开更多
The integrated perception capable of detecting and monitoring varieties of activities is one of the ultimate purposes of wearable electronics and intelligent robots.Limited by the space occupation,it lacks practical f...The integrated perception capable of detecting and monitoring varieties of activities is one of the ultimate purposes of wearable electronics and intelligent robots.Limited by the space occupation,it lacks practical feasibility to stack multiple types of single sensors on each other.Herein,a high-sensitivity dual-function capacitive sensor with proximity sensing and pressure sensing is proposed.The fringing electric field can be confined in the proximity-sensitive area by fibrous loop-patterned electrode,leading to more stolen charges when object approaching and thus a high proximity sensitivity.The high-permittivity doped structured dielectric layer reduces the compressive stiffness and enhances the rate of compression-caused increase in the equivalent relative permittivity of the dielectric layer,resulting in a larger increase in capacitance and thus a high pressure sensitivity.The electrodes and dielectric layer together compose the capacitor and act as the sensor without taking up additional space.The decoupling of proximity-sensing and pressure-sensing modes can be achieved by decrease or increase in capacitance.Combined with array distribution and sequential scanning,the sensors can be used for detection of motion trajectory,contour recognition,pressure distribution.展开更多
文摘Potato is one of the key crops for ensuring food security and can be a potential substitute for cereal crops due to its high yielding nature and nutritional value.Crop nutrient management practices within potato fields are implemented uniformly without considering crop requirements and soil variability,causing uneven and low yield.However,yield can be increased by identifying growth and yield-limiting factors.Geospatial tools are robust and effective in identifying the spatial variations within the field.Proximal sensing allows quick analysis of soil and plant characteristics,decreases the need for laborious and expensive soil and plant sampling,and strengthens precision agriculture techniques.The aim of the study was to quantify the soil spatial variability and identify potato crop growth and yield limiting factors for the optimization of inputs.Two fields were selected in the subtropical region of Pakistan(Koont,Rawalpindi),and each field was cultivated with two different potato varieties.A grid sampling approach was developed to collect soil samples and tuber yields.The soil was tested for nitrogen(N),phosphorus(P),potassium(K),pH,electrical conductivity(E.C),temperature,and moisture content(M.C)by using a soil proximal sensor.Normalized difference vegetation index(NDVI)was recorded using a handheld GreenSeeker,and chlorophyll was estimated using a chlorophyll meter.Descriptive statistics and correlation analysis for soil and crop parameters were performed in Minitab 21,while geostatistical analysis was performed in Arc Map 10.8 to show spatial variability and to generate kriged maps of different soil properties.The coefficient of variation of soil properties and plant parameters showed moderate to high variability within the field,except for pH and temperature.The correlation matrix suggested that N,P,K,E.C.,chlorophyll,NDVI,plant height,and leaf area had a significant relationship with potato yield.Most of the soil and plant parameters had a medium to high range of influence(20 to 90 m)and varied greatly within the field.Kriged maps of plant and soil parameters also showed spatial variations and were aligned with descriptive statistics and correlations.Quantification of soil spatial variability within potato fields can assist in measuring yield-limiting soil characteristics to establish management zones for variable rate fertilization for optimum tuber yield and low environmental impact.
文摘Fiber sensors are commonly used to detect environmental,physiological,optical,chemical,and biological factors.Thermally drawn fibers offer numerous advantages over other commercial products,including enhanced sensitivity,accuracy,improved functionality,and ease of manufacturing.Multimaterial,multifunctional fibers encapsulate essential internal structures within a microscale fiber,unlike macroscale sensors requiring separate electronic components.The compact size of fiber sensors enables seamless integration into existing systems,providing the desired functionality.We present a multimodal fiber antenna monitoring,in real time,both the local deformation of the fiber and environmental changes caused by foreign objects in proximity to the fiber.Time domain reflectometry propagates an electromagnetic wave through the fiber,allowing precise determination of spatial changes along the fiber with exceptional resolution and sensitivity.Local changes in impedance reflect fiber deformation,whereas proximity is detected through alterations in the evanescent field surrounding the fiber.The fiber antenna operates as a waveguide to detect local deformation through the antisymmetric mode and environmental changes through the symmetric mode.This multifunctionality broadens its application areas from biomedical engineering to cyber-physical interfacing.In antisymmetric mode,the device can sense local changes in pressure,and,potentially,temperature,pH,and other physiological conditions.In symmetric mode,it can be used in touch screens,environmental detection for security,cyber-physical interfacing,and human-robot interactions.
文摘Frozen soils or those with permafrost cover large areas of the earth's surface and support unique vegetative ecosystems. Plants growing in such harsh conditions have adapted to small niches, which allow them to survive. In northern Alaska, USA, both moist acidic and non-acidic tundra occur, yet determination of frozen soil p Hs currently requires thawing of the soil so that electrometric pH methods can be utilized. Contrariwise, a portable X-ray fluorescence(PXRF) spectrometer was used in this study to assess elemental abundances and relate those characteristics to soil pH through predictive multiple linear regressions. Two operational modes, Soil Mode and Geochem Mode, were utilized to scan frozen soils in-situ and under laboratory conditions, respectively, after soil samples were dried and ground. Results showed that lab scanning produced optimal results with adjusted coefficient of determination(R^2) of 0.88 and 0.33 and root mean squared errors(RMSEs) of 0.87 and 0.34 between elemental data and lab-determined pH for Soil Mode and Geochem Mode, respectively. Even though the presence of ice attenuated fluoresced radiation under field conditions, adjusted R^2 and RMSEs between the datasets still provided reasonable model generalization(e.g., 0.73 and 0.49 for field Geochem Mode). Principal component analysis qualitatively separated multiple sampling sites based on elemental data provided by PXRF, reflecting differences in the chemical composition of the soils studied. Summarily, PXRF can be used for in-situ determination of soil pH in arctic environments without the need for sample modification and thawing. Furthermore, use of PXRF for determination of soil pH may provide higher sample throughput than traditional eletrometric-based methods, while generating elemental data useful for the prediction of multiple soil parameters.
文摘Field portable X-ray fluorescence （PXRF） spectrometry has become an increasingly popular technique for in-situ elemental characterization of soils. The technique is fast, portable, and accurate, requiring minimal sample preparation and no consumables. However, soil moisture 〉 20% has been known to cause fluorescence denudation and error in elemental reporting and few studies have evaluated the presence of soil moisture in solid form as ice. Gelisols （USDA Soil Taxonomy）, permafrost-affected soils, cover a large amount of the land surface in the northern and southern hemispheres. Thus, the applicability of PXRF in those areas requires further investigation. PXRF was used to scan the elemental composition （Ba, Ca, Cr, Fe, K, Mn, Pb, Rb, Sr, Ti, Zn, and Zr） of 13 pedons in central and northern Alaska, USA. Four types of scans were completed： 1） in-situ frozen soil, 2） re-frozen soil in the laboratory, 3） melted soil/water mixture in the laboratory, and 4） moisture-corrected soil. All were then compared to oven dry soil scans. Results showed that the majority of PXRF readings from in-situ, re-frozen, and melted samples were significantly underestimated, compared to the readings on oven dry samples, owing to the interference expected by moisture. However, when the moisture contents were divided into 〉 40% and 〈 40〈 groups, the PXRF readings under different scanning conditions performed better in the group with 〈 40% moisture contents. Most elements of the scans on the melted samples with 〈 40% moisture contents acceptably compared to those of the dry samples, with R2 values ranging from 0.446 （Mn） to 0.930 （St）. However, underestimation of the melted samples was still quite apparent. Moisture-corrected sample PXRF readings provided the best correlation to those of the dry, ground samples as indicated by higher R2 values, lower root mean square errors （RMSEs）, and slopes closer to 1 in linear regression equations. However, the in-situ （frozen） sample scans did not differ appreciably from the melted sample scans in their correlations to dry sample scans in terms of R2 values （0.81 vs. 0.88）, RMSEs （1.06 vs. 0.85）, and slopes （0.88 vs. 0.92）. Notably, all of those relationships improved for the group with moisture contents 〈 40%.
基金supported by SGC project5 entitled"Mobile Biochar Production for Methane Emission Reduction and Soil Amendment".Grant Agreement#CCR20014supported in part by NSF CBET#1856112supported in part by an F3 R&D GSR Award (Farms Food Future Innovation Initiative (or F3),as funded by US Dept.of Commerce,Economic Development Administration Build Back Better Regional Challenge).
文摘Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification,making it a significant carbon source for soil.Applying biochar to soil is a carbon-negative process that helps combat climate change,sustain soil biodiversity,and regulate water cycling.However,quantifying soil carbon content conventionally is time-consuming,labor-intensive,imprecise,and expensive,making it difficult to accurately measure in-field soil carbon’s effect on storage water and nutrients.To address this challenge,this paper for the first time,reports on extensive lab tests demonstrating non-intrusive methods for sensing soil carbon and related smart biochar applications,such as differentiating between biochar types from various biomass feedstock species,monitoring soil moisture,and biochar water retention capacity using portable microwave and millimeter wave sensors,and machine learning.These methods can be scaled up by deploying the sensor in-field on a mobility platform,either ground or aerial.The paper provides details on the materials,methods,machine learning workflow,and results of our investigations.The significance of this work lays the foundation for assessing carbon-negative technology applications,such as soil carbon content accounting.We validated our quantification method using supervised machine learning algorithms by collecting real soil mixed with known biochar contents in the field.The results show that the millimeter wave sensor achieves high sensing accuracy(up to 100%)with proper classifiers selected and outperforms the microwave sensor by approximately 10%–15%accuracy in sensing soil carbon content.
文摘An increase in data availability from different sensors and sources has changed how crop models are being used.Data assimilation is one approach for integrating data and models that has been widely used for field crops but not yet in protected environments.We present a case study of data assimilation in a greenhouse,updating growth estimates of the Reduced State TOMGRO model.We assimilated data obtained through the continuous monitoring of plant mass and images captured by low-cost cameras,using the Unscented Kalman Filter and the Ensemble Kalman Filter.In some cases,assimilation led to improvements of more than 40%in the RMSE of yield estimates of the non-calibrated model,within a validation set.The improvements were more noticeable when there was a need to adjust the estimates to a condition the model does not represent.In these situations,we noted the RMSE decreased by almost 80%,depending on the variable being assimilated.However,in some cases,the results were also impaired by assimilation,and we highlight the impacts on the filter performance caused by the quality of observations and of observation models.Overall,the employed measurements,i.e.,area of organs observed in pictures and plant-water mass,seemed suitable for tracking plant growth and for obtaining good approximations of the state variables estimated by the model.As with other studies,it was not the case that assimilating one state was useful for improving the value of others,including yield.As the first study using filters and non-destructive observations in a process-based crop model in a protected environment,we identified a lot of potential,but to identify the best use of these techniques with real-time data,more studies are needed.By making all data and code from this study available,we hope to ease future research in this area.
基金financially supported by the National Key Research&Development Program of China(No.2022YFA1205200).
文摘The development of intelligent electronic power systems necessitates advanced flexible pressure sensors.Despite improved compressibility through surface micro-structures or bulk pores,conventional capacitive pressure sensors face limitations due to their low dielectric constant and poor temperature tolerance of most elastomers.Herein,we constructed oriented polyimide-based aerogels with mechanical robustness and notable changes in dielectric constant under compression.The enhancement is attributed to the doping of surface-modified dielectric nanoparticles and graphene oxide sheets,which interact with polymer molecular chains.The resulting aerogels,with their excellent temperature resistance,were used to assemble high-performance capacitive pressure sensors.The sensor exhibits a maximum sensitivity of 1.41 kPa^(−1)over a wide working range of 0-200 kPa.Meanwhile,the sensor can operate in environments up to 150℃during 2000 compression/release cycles.Furthermore,the aerogel-based sensor demonstrates proximity sensing capabilities,showing great potential for applications in non-contact sensing and extreme environment detection.
基金the National Key Research and Development Program of China(No.2021YFB2011500)the National Natural Science Foundation of China(Nos.52025055 and 51905415)+4 种基金Institutional Foundation of The First Affiliated Hospital of Xi’an Jiaotong University,the China Gas Turbine Establishment of Aero Engine Corporation of China(No.GJCZ-2019-0039)the National Postdoctoral Program for Innovative Talents(No.BX20180251)Young Talent Fund of University Association for Science and Technology in Shaanxi,China(No.20200404)Basic Research Program of Natural Science of Shaanxi Province of China(Nos.2019JLM-5 and 2021JLM-42)Shaanxi University Youth Innovation Team.
文摘The integrated perception capable of detecting and monitoring varieties of activities is one of the ultimate purposes of wearable electronics and intelligent robots.Limited by the space occupation,it lacks practical feasibility to stack multiple types of single sensors on each other.Herein,a high-sensitivity dual-function capacitive sensor with proximity sensing and pressure sensing is proposed.The fringing electric field can be confined in the proximity-sensitive area by fibrous loop-patterned electrode,leading to more stolen charges when object approaching and thus a high proximity sensitivity.The high-permittivity doped structured dielectric layer reduces the compressive stiffness and enhances the rate of compression-caused increase in the equivalent relative permittivity of the dielectric layer,resulting in a larger increase in capacitance and thus a high pressure sensitivity.The electrodes and dielectric layer together compose the capacitor and act as the sensor without taking up additional space.The decoupling of proximity-sensing and pressure-sensing modes can be achieved by decrease or increase in capacitance.Combined with array distribution and sequential scanning,the sensors can be used for detection of motion trajectory,contour recognition,pressure distribution.
