Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor

A spectral reflectance sensor(SRS)fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index(NDVI)and photochemical reflectance index(PRI).NDVI is useful for indicating crop growth/phenology,whereas PRI was developed for observing physiological conditions.Thus,the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring system.However,capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions,such as solar irradiance and precipitation.Further,unlike growth/phenology,the physiological condition has diurnal changes as well as seasonal characteristics.This study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice,barley,and garlic.First,the measurement accuracy of SRSs was compared with handheld spectrometers,and the R^(2)values between the two devices were 0.96 and 0.81 for NDVI and PRI,respectively.Second,the experimental study of threshold criteria with respect to meteorological variables(i.e.,insolation,cloudiness,sunshine duration,and precipitation)was conducted,and sunshine duration was the most useful one for excluding distorted values of the vegetation indices.After data processing based on sunshine duration,the R^(2)values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004(NDVI)and 0.065–0.298(PRI)on the three crops,and the seasonal signals of vegetation indices became noticeably improved.This method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions.

Spectral indices measured with proximal sensing using canopy reflectance sensor,chlorophyll meter and leaf color chart for in-season grain yield prediction of basmati rice

A number of optical sensing tools are now available and can potentially be used for refining need-based fertilizer nitrogen(N)topdressing decisions.Algorithms for estimating field-specific fertilizer N needs are based on predictions of yield made while the crops are still growing in the field.The present study was conducted to establish and validate yield prediction models using spectral indices measured with proximal sensing using GreenSeeker canopy reflectance sensor,soil and plant analyzer development(SPAD)chlorophyll meter,and two different types of leaf color charts(LCCs)for five basmati rice genotypes across different growth stages.Regression analysis was performed using normalized difference vegetation index(NDVI)recorded with GreenSeeker sensor and leaf greenness indices measured with SPAD meter and LCCs developed by Punjab Agricultural University,Ludhiana(India)(PAU-LCC)and the International Rice Research Institute,Philippines(IRRI-LCC).The exponential relationship between NDVI and grain yield exhibited the highest coefficient of determination(R^(2))and minimum normalized root mean square error(NRMSE)at panicle initiation stage and explained 38.3%–76.4%variation in yield using genotype-specific models.Spectral indices pooled for different genotypes were closely related to grain yield at all growth stages and explained53.4%–57.2%variation in grain yield.Normalizing different spectral indices with cumulative growing degree days(CGDD)decreased the accuracy of yield prediction.Normalization with days after transplanting(DAT),however,did not reduce or improve the predictability of yield.The ability of each model to predict grain yield was validated with an independent dataset collected from two experiments conducted at different sites with a range of fertilizer N doses.The NDVI-based genotype-specific models exhibited a robust linear correlation(R^(2)=0.77,NRMSE=7.37%,n=180)between observed and predicted grain yields only at 35 DAT(i.e.,panicle initiation stage),while the SPAD,PAU-LCC,and IRRI-LCC consistently provided reliable predictions(with respective R^(2)of 0.63,0.60,and 0.53 and NRMSE of 10%,10%,and 13.6%)even with genotype invariant models with 900 data points obtained at different growth stages.The study revealed that unnormalized values of spectral indices,namely NDVI,SPAD,PAU-LCC,and IRRI-LCC,can be satisfactorily used for in-season estimation of grain yield for basmati rice.As LCCs are very economical compared with chlorophyll meters and canopy reflectance sensors,they can be preferably used by small farmers in developing countries.

Review of reflective fiber optic sensors for surface topography measurement

Different from the traditional contact surface topography measurement,reflective intensity-modulated fiber optic sensor(RIM-FOS)has the unique advantages of non-contact nondestructive detection.This paper briefly introduces the principle and performance of RIM-FOS for surface topography measurement and compares with several other methods of topography measurement.Based on the review of its development process,this paper summarizes and analyses the hot issues of RIM-FOS in the surface topography measurement,then predicts the future trend for a guidance of the further study.

Synchronous line-tracking robots based on STM 32

A pair of synchronous line-tracking robots based on STM32 are designed. Each robot is actually a small intelligent car with seven reflective infrared photoelectric sensors ST188 installed in the front to track the line. Two rear wheels each driven by a moter are the driving wheels, while each rooter is driven by an H-bridge circuit. The running direction is con- trolled by the turning of a servo fastened to the front wheel and the adjustment of speed difference between the rear wheels. Besides, the light-adaptive line-tracking can be performed. The speeds of the motors are controlled by adjusting pulse-width modulation （PWM） values and an angular displacement transducer is used to detect the relative position of the cars in real time. Thus, the speeds of the cars can be adjusted in time so that the synchronism of the cars can be achieved. Through ex-periments, the fast and accurate synchronous tracking can be well realized.

LS-SVM-based surface roughness prediction model for a reflective fiber optic sensor

Reflective fiber optic sensors have advantages for surface roughness measurements of some special workpieces,but their measuring precision and efficiency need to be improved further. A least-squares support vector machine（LS-SVM）-based surface roughness prediction model is proposed to estimate the surface roughness, Ra, and the coupled simulated annealing（CSA） and standard simplex（SS） methods are combined for the parameter optimization of the mode. Experiments are conducted to test the performance of the proposed model, and the results show that the range of average relative errors is-4.232%–2.5709%. In comparison with the existing models, the LS-SVM-based model has the best performance in prediction precision, stability, and timesaving.

Development of a surface acoustic wave wireless and passive temperature sensor incorporating reflective delay line

based on optimal design on the core element of the sensor,a wireless and passive surface acoustic wave（SAW）temperature sensor integrated with ID Tag was presented.A reflective delay line,which consists of a transducer and eight reflectors on YZ LiNbO3 substrate.Was fabricated as the sensor element,in which,three reflectors were used for temperature sensing,and the other five were for the ID Tag using phase encoding.Single phase unidirectional transducers（SPUDTs）and shorted grating were used to structure the sAW device,leading to excellent signal to noise ratio（SNR）.The performance of the SAW device was simulated by the coupling of modes（COM）prior to fabrication.Using the network analyzer,the response in time domain of the fabricated 434 MHz SAW sensor was characterized,the measured S11 agrees well with the simulated one,sharp reflection peaks,high signal/noise,and low spurious noise between the reflection peaks were observed.Using the radar system based on FSCW as the reader unit.the developed SAW temperature sensors were evaluated wirelessly.Excellent1 inearity and good resolution of士1℃ were observed.