Ag-IoT for crop and environment monitoring: Past, present, and future

CONTEXT: Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction of IoT (Internet of Things) into crop, soil, and microclimate sensing has transformed crop monitoring into a quantitative and data-driven work from a qualitative and experience-based task. OBJECTIVE: Ag-IoT systems enable a data pipeline for modern agriculture that includes data collection, transmission, storage, visualization, analysis, and decision-making. This review serves as a technical guide for Ag-IoT system design and development for crop, soil, and microclimate monitoring. METHODS: It highlighted Ag-IoT platforms presented in 115 academic publications between 2011 and 2021 worldwide. These publications were analyzed based on the types of sensors and actuators used, main control boards, types of farming, crops observed, communication technologies and protocols, power supplies, and energy storage used in Ag-IoT platforms

Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors

Under spatially-coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number (N) of optimizable phase-only diffractive features is greater than or equal to ~2 Ni x No, where Ni and No refer to the number of useful pixels at the input and the output FOVs, respectively. Here we report the design of a spatially-incoherent diffractive optical processor that can approximate any arbitrary linear transformation in time-averaged intensity between its input and output FOVs. Under spatially-incoherent monochromatic light, the spatially-varying intensity point spread functon(H) of a diffractive network, corresponding to a given, arbitrarily-selected linear intensity transformation, can be written as H(m,n;m',n')=|h(m,n;m',n')|^2, where h is the spatially-coherent point-spread function of the same diffractive network, and (m,n) and (m',n') define the coordinates of the output and input FOVs, respectively. Using deep learning, supervised through examples of input-output profiles, we numerically demonstrate that a spatially-incoherent diffractive network can be trained to all-optically perform any arbitrary linear intensity transformation between its input and output if N is greater than or equal to ~2 Ni x No. These results constitute the first demonstration of universal linear intensity transformations performed on an input FOV under spatially-incoherent illumination and will be useful for designing all-optical visual processors that can work with incoherent, natural light.Comment: 29 Pages, 10 Figure

Different glucose analyzers report different glucose concentration values in term newborns

Background: The American Academy of Pediatrics and Pediatric Endocrine Society neonatal hypoglycemia guidelines based their glucose concentration treatment thresholds on studies that predominantly used Beckman and Yellow Springs Glucose Oxidase Analyzers. Currently, a majority (76%) of U.S. hospital laboratories utilizing glucose oxidase methodology use Vitros® Glucose Analyzers. However, a bias of ~+5% between glucose concentrations from Beckman vs. Vitros Glucose Analyzers has been reported; this could have a clinically significant effect when using published guideline treatment thresholds. Methods: To determine if there is similar instrument bias between Beckman and Vitros Analyzers in reported glucose concentrations from term newborns, we compared plasma glucose concentrations measured within the first 3 h after birth by Beckman vs. Vitros Analyzers in a total of 1,987 newborns (Beckman n = 904, Vitros n = 1,083). Data were fit using nonlinear cubic spline models between collection time and glucose concentration. Results: The non-linear patterns of initial glucose concentrations (during the first 3 h after birth) as measured by Beckman and Vitros Analyzers paralleled each other with no overlap of the fit spline curve 95% confidence intervals, with an approximate +5 mg/dL constant bias. Additionally, in method comparison studies performed in the Chemistry Laboratory on adult samples, there was a +4.2-7.4 mg/dL measured glucose bias for the Beckman vs. Vitros Analyzer. Conclusion: Glucose concentrations from term, appropriate size for gestational age newborns were about 5 mg/dL higher when measured by Beckman vs. Vitros Analyzers. Perhaps, concentrations of 45 mg/dL reported from Beckman Analyzers may be equivalent to 40 mg/dL from Vitros Analyzers. When managing neonatal hypoglycemia, it is important to know which analyzer was used and whether adjusting for potential instrument bias is necessary when following published guidelines. Keywords: glucose; glucose analyzer; guideline; neonatal hypoglycemia; newborn

The Optimum Feeding Frequency in Growing Korean Rockfish () Rearing at the Temperature of 15°C and 19°C

Two feeding trials were conducted to determine the optimum feeding frequency in growing Korean rockfish, (Sebastes schlegeli) reared at the temperatures of 15°C and 19°C. Fish averaging 92.2±0.7 g (mean±standard deviation [SD]) at 15.0±0.5°C and 100.2±0.4 g (mean±SD) at 19.0±0.5°C water temperature were randomly distributed into each of 15 indoor tanks containing 250-L sea water from a semi-recirculation system. A total of five feeding frequency groups were set up in three replicates as follows: one meal in a day at 08:00 hour, two meals a day at 08:00 and 17:00 hours, three meals a day at 08:00, 14:00, and 20:00 hours, four meals a day at 08:00, 12:00, 16:00, and 20:00 hours, and one meal every 2 days at 08:00 hour. Fish were fed at the rate of 1.2% body weight (BW)/d at 15°C and 1.5% BW/d at 19°C. At the end of 8 wks of feeding trial weight gain and specific growth rate were significantly higher at the fish fed groups of one meal a day and two meals a day at 15°C and fish fed groups of 1 meal every 2 days at 19°C were significantly lower than those of all other fish fed groups. Glutamic oxaloacetic transaminase and glutamic pyruvic transaminase of fish fed group at 1 meal every 2 days was significantly higher than those of all other fish fed groups in both experiments. Weight gain, specific growth rate and condition factor were gradually decreased as the feeding frequency increased. The results indicate that growing Korean rockfish 92 and 100 g perform better at 15°C than 19°C water temperature. As we expected, current results have indicated that a feeding frequency of 1 meal a day is optimal for the improvement of weight gain in growing Korean rockfish grown from 92 g to 133 g at 15°C and 100 g to 132 g at 19°C water temperature

Razvoj normaliziranog indeksa tla za urbane studije upotrebom podataka daljinskih mjerenja

This paper presents two novel spectral soil area indices to identify bare soil area and distinguish it more accurately from the urban impervious surface area (ISA). This study designs these indices based on medium spatial resolution remote sensing data from Landsat 8 OLI dataset. Extracting bare soil or urban ISA is more challenging than extracting water bodies or vegetation in multispectral Remote Sensing (RS). Bare soil and the urban ISA area often were mixed because of their spectral similarity in multispectral sensors. This study proposes Normalized Soil Area Index 1 (NSAI1) and Normalized Soil Area Index 2 (NSAI2) using typical multispectral bands. Experiments show that these two indices have an overall accuracy of around 90%. The spectral similarity index (SDI) shows these two indices have higher separability between soil area and ISA than previous indices. The result shows that percentile thresholds can effectively classify bare soil areas from the background. The combined use of both indices measured the soil area of the study area over 71 km2. Most importantly, proposed soil indices can refine urban ISA measurement accuracy in spatiotemporal studies.Ovaj rad prikazuje dva nova spektralna indeksa tla kako bi se identificiralo golo tlo te kako bi se bolje razlikovalo od urbanih nepropusnih površina (ISA). Ti indeksi su definirani na temelju srednje prostorne rezolucije daljinskih podataka Landsat 8 OLI skupa podataka. U multispektralnim daljinskim mjerenjima (RS) prepoznavanje golog tla ili urbane ISA podloge je složenije od prepoznavanja vodenih tijela ili podloge s vegetacijom. Zbog sličnosti spektara dobivenih multispektralnim senzorima golo tlo i urbana ISA površina često se ne razlučuju. Ova studija predlaže dva normalizirana indeksa tla (NSAI1 i NSAI2) korištenjem tipičnih multispektralnih pojaseva. Eksperimenti pokazuju da ta dva indeksa imaju sveukupnu točnost od približno 90%. Indeks spektralne sličnosti (SDI) pokazuje da ta dva indeksa razlikuju golo tlo od urbane ISA podloge bolje nego dosadašnji indeksi. Rezultati pokazuju da percentilni pragovi mogu efikasno razlučiti površine s golim tlom od pozadine. Kombiniranom upotrebom oba indeksa izmjerena je površina tla veća od 71 km2. Najznačajniji rezultat je taj da predloženi indeksi tla mogu poboljšati točnost mjerenja urbanih ISA u u prostorno-vremenskim studijama

Multi-mode Combustion Process Monitoring through Flame Imaging and Soft-computing

Reliable monitoring and diagnosis of combustion stability in combustion systems such as fossil-fuel fired boilers, gas turbines and combustion engines are crucial to maintain the system safety, combustion efficiency and low emissions, particularly under variable operation conditions. Considerable efforts have thus been made in developing techniques for online monitoring and diagnosis of the stability of a combustion process. Among those, flame imaging conjoined with image processing and soft computing techniques has been paid much attention for both laboratorial and industrial applications. Some imaging and soft computing techniques have been proposed for combustion state monitoring, but most of them can only detect a single-mode condition. However, modern combustion systems often operate under variable conditions (i.e., multi-mode process). Due to the dynamic nature of the combustion process, single-mode monitoring methods often mistakenly determine some normal combustion behaviours as abnormal ones. The recent trend of using a variety of fuels, including low quality coals, coal blends, and co-firing biomass and coal, has further deteriorated this issue. In this study, a method based on flame imaging and soft-computing techniques for multi-mode combustion process monitoring is proposed. Flame images are acquired using a flame imaging system. Mean intensity values of RGB image components and texture descriptors are extracted and computed from the grey-level co-occurrence matrix. Such features are then used as inputs to a combined PCA-KSVM (principle component analysis-kernel support vector machine) model for multi-mode process monitoring. In this method, the PCA serves for eliminating the impact of noise and instabilities on the mode recognition. The KSVM identifies the combustion mode by using the scores of the features in the principle component subspace. Finally, two multivariate statistic indices, T2 and SPE, are computed and used to assess the stabilities of the combustion process. The proposed approach has been examined by using flame images obtained on the UKCCSRC PACT 250kW PF (pulverised fuel) test rig under different operation conditions (e.g., variations in the primary air and secondary-territory air split). Test results have shown that the computed image features represent well the dynamic behaviours of the flame, and that the PCA-KSVM model has outperformed conventional methods in monitoring the multi-mode combustion process

Specifically Progressive Deficits of Brain Functional Marker in Amnestic Type Mild Cognitive Impairment

Background: Deficits of the default mode network (DMN) have been demonstrated in subjects with amnestic type mild cognitive impairment (aMCI) who have a high risk of developing Alzheimer’s disease (AD). However, no longitudinal study of this network has been reported in aMCI. Identifying links between development of DMN and aMCI progression would be of considerable value in understanding brain changes underpinning aMCI and determining risk of conversion to AD. Methodology/Principal Findings: Resting-state fMRI was acquired in aMCI subjects (n = 26) and controls (n = 18) at baseline and after approximately 20 months follow up. Independent component analysis was used to isolate the DMN in each participant. Differences in DMN between aMCI and controls were examined at baseline, and subsequent changes between baseline and follow-up were also assessed in the groups. Posterior cingulate cortex/precuneus (PCC/PCu) hyper-functional connectivity was observed at baseline in aMCI subjects, while a substantial decrement of these connections was evident at follow-up in aMCI subjects, compared to matched controls. Specifically, PCC/PCu dysfunction was positively related to the impairments of episodic memory from baseline to follow up in aMCI group. Conclusions/Significance: The patterns of longitudinal deficits of DMN may assist investigators to identify and monitor the development of aMCI

Machine-Learning-Method-Based Inversion of Shallow Bathymetric Maps Using ICESat-2 ATL03 Data

peer reviewedThe application of empirical methods for satellite-derived bathymetry is limited by the lack of in situ bathymetric data in remote, inaccessible areas. This challenge has been addressed with the launch of Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). This study provides an accurate bathymetric photon extraction process for ICESat-2 ATL03 data, and the ${{\bm{R}}}^2$ value of the bathymetric photons obtained using this process and airborne bathymetric LiDAR data is up to 99%. Next, based on two types of remote sensing data, ICESat-2 and Sentinel-2, machine learning models, including linear regression (LR), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were trained to obtain bathymetric maps. The experimental results show that the mean root mean square error (RMSE), mean absolute error (MAE), and mean relative error (MRE) values of the LR models are less than 3.02 m, 2.38 m, and 86.03%, respectively. The mean RMSE, MAE, and MRE values of the LightGBM and CatBoost models are less than 0.91 m, 0.66 m, and 23.17%, respectively. It is concluded that the proposed denoising process for ICESat-2 ATL03 data is effective, and the results of the bathymetric maps obtained using these data are satisfactory. Thus, the proposed approach is effective, and this strategy can be used to replace conventional bathymetric inversion methods to obtain high-accuracy bathymetric maps