Thoracopagus conjoined twins: a case report

25 yr. old, G2P1 presented with premature labour pains at 33wks 3d of gestation and was referred to our tertiary centre as a suspected case of conjoined twin pregnancy based on a sonography report which revealed fetus with two heads Decision in favour of LSCS was taken after counselling the patient and her attendants regarding the anomaly of the fetus and incompatibility of life and dangers of spontaneous vaginal delivery. LSCS was done with delivery of first twin by cephalic and second twin by breech extraction, both were preterm male babies  joined anteriorly  starting from  thorax to umbilicus (Thoracopagus) with  four arms and four legs, baby could not be revived and was declared clinically dead in few minutes by neonatologist,. Photographs were taken and we tried to obtain consent for autopsy but attendants were reluctant. A review of the literature suggests that early diagnosis by a combination of ultrasound and MRI is essential to management, providing prognosis for viability and success of surgical separation and the opportunity for early counselling of parents and termination if indicated.

Potential of X-ray imaging to detect citrus granulation in different cultivars with progress in harvesting time

263-268Granulation, a physiological disorder of citrus is manifested by shriveled juice sacs and internal dryness. Extractable juice in granulated tissue is drastically reduced as a consequence of gelatinization and secondary epidermis formation. Since, the defect cannot be detected externally it leads to consumer dissatisfaction and poor returns to farmers. Processing industry also faces huge economic loss due to reduction in the juice recovery from granulated fruit. In this context, here, we studied the possibility of developing an image processing algorithm through MATLAB software to detect granulation with advancement of maturity via X-ray micrographs. Fruit of eight citrus cultivars comprising of granulation susceptible and tolerant varieties harvested at four different intervals were exposed to X-rays. Voltage of 46 kV and current of 6.5 mA given to fruit for an exposure time of 320 mAs gave the best X-ray image contrasts. The developed algorithm could effectively distinguish the healthy and granulated fruit with an accuracy of 90% as validated by subsequent destructive analysis when estimated for four different harvesting dates. The imaging technique can be employed by the processors to determine the severity of granulation and to sort out fruit online which will help in saving economic losses

Farm-Scale Mapping of Soil Microbiological Indicators Using Geostatistical Technique

Soil microbiological properties viz. soil microbial biomass carbon (MBC) and dehydrogenase activity (DHA) are sensitive soil quality indicators. Spatial modeling and prediction map of soil MBC and DHA were generated for a semiarid agricultural farm, New Delhi, India from 288 geo-referenced grid samples spaced 100 m × 100 m distance using geospatial techniques and geo-statistics. Soil microbial biomass carbon (MBC) ranged from 19.7 to 519.7 µg g-1 with standard deviation of 84.1 and soil DHA varied from 1.2 to 17.2 µg TPF g-1 dry soil hr-1 with sample variance of 10.89. Soil MBC and DHA had high data viability with coefficient of variation (CV) of 42.5 % and 53.2%, respectively. The best fit semivariogram for both soil MBC and DHA was exponential model and had practical spatial range of 1500 m and 1473 m respectively. Environmental disturbances or extrinsic factors dominantly influenced the spatial variability of soil MBC, expressing its weak spatial dependency.  Besides, both soil structural/internal factors and extrinsic factors controlled soil DHA variability with moderate level of spatial dependency. Spatial variability map of soil MBC and DHA, prepared with good accuracy through ordinary kriging in GIS software, showed that major area of the farm had soil MBC ranging from 150 to 250 mg kg-1 and had DHA from 1.2 to 10 µg TPF g-1 dry soil hr-1

Farm-scale Mapping of Soil Phosphorus and Potassium Fractions Using Geostatistical Technique

Phosphorus (P) and potassium (K) are two major nutrients for agricultural productivity and sustainability. The spatial variability maps of soil phosphorus and potassium content in surfacesoils collected through grid sampling technique were developed using geo-spatial technology for Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI) farm, New Delhi, India. Soil available P content (NaHCO3-P) and P-fractions such as NaOH extractable-P(NaOH-P), citrate-bicarbonate extractable P (CB-P), citrate-bicarbonate-dithionite extractable-P(CBD-P) and HCl extractable-P (HCl-P) through sequential fractionation techniques and K fractions(available-K and non-exchangeable-K) were estimated. In geostatistical technique, exploratory data analysis and semivariogram analysis for P & K fractions were conducted and ordinary kriging was used for spatial interpolation and mapping. On average basis, among the P-fractions, Ca-boundphosphorus (HCl-P) had highest value followed by non-occluded Fe- and Al-bound-P (i.e. NaOH-P)and occluded-P within iron oxide and hydrous oxide (i.e. CBD-P). Soil available K in the farmranged from 43.9 to 839.3 mg kg-1 and non-exchangeable-K content was found to be in high to veryhigh level (820-1921 mg kg-1). Among the P & K fractions, occluded-P and Ca-bound P showed first order polynomial surface trend, which were removed before ordinary kriging interpolation. Semivariogram analysis of soil P- & K-fractions at the IARI farm indicated the effective spatial range dependency. Prediction maps of P- & K- fractions in the semiarid agricultural farm thorough ordinary kriging were found superior to log-normal ordinary kriging. The spatial variability map based fertilizer recommendation and management practices for major cropping systems in the farmarecrucial for precision nutrient management and sustainable agriculture

Inversion of radiative transfer model for retrieval of wheat biophysical parameters from broadband reflectance measurements

This study describes the retrieval of wheat biophysical variables of leaf chlorophyll (Cab), leaf area index (LAI), canopy chlorophyll (CCC), and leaf wetness (Cw) from broadband reflectance data corresponding to IRS LISS-3 (Linear Imaging Self Scanner) sensor by inversion of PROSAIL5B canopy radiative transfer model. Reflectance data of wheat crop, grown under different treatments, were measured by hand-held spectroradiometer and later integrated to LISS-3 reflectance using its band-wise relative spectral response function. Three inversion techniques were used and their performance was compared using different statistical parameters and target diagram. The inversion techniques tried were: a look up table with best solution (LUT-I), a look up table with mean of best 10% solutions (LUT-II) and an artificial neural network (ANN). All the techniques could estimate the biophysical variables by capturing variability in their observed values, though accuracy of estimation varied among the three techniques. Target diagram clearly depicted the superiority of LUT-II over the other two approaches indicating that a mean of best 10% solutions is a better strategy while ANN was worst performer showing highest bias for all the parameters. In all the three inversion techniques, the general order of retrieval accuracy was LAI > Cab > CCC > Cw. The range of Cw was very narrow and none of the techniques could estimate variations in it. In most of the cases, the parameters were underestimated by model inversion. The best identified LUT-II technique was then applied to retrieve wheat LAI from IRS LISS-3 satellite image of 5-Feb-2012 in Sheopur district. The comparison with ground observations showed that the RMSE of LAI retrieval was about 0.56, similar to that observed in ground experimentation. The findings of this study may help in refining the protocol for generating operational crop biophysical products from IRS LISS-3 or similar sensors

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Not AvailableThis study describes the retrieval of wheat biophysical variables of leaf chlorophyll (Cab), Leaf Area Index (LAI), canopy chlorophyll (CCC), and leaf wetness (Cw) from broadband reflectance data corresponding to IRS LISS-3 (Linear Imaging Self Scanner) sensor by inversion of PROSAIL5B canopy radiative transfer model. Reflectance data of wheat crop, grown under different treatments, were measured by hand-held spectroradiometer and later integrated to LISS-3 reflectance using its band-wise relative spectral response function. Three inversion techniques were used and their performance was compared using different statistical parameters and target diagram. The inversion techniques tried were: a look up table with best solution (LUT-I), a look up table with mean of best 10% solutions (LUT-II) and an artificial neural network (ANN). All the techniques could estimate the biophysical variables by capturing variability in their observed values, though accuracy of estimation varied among the three parameters. Target diagram clearly depicted the superiority of LUT-II over the other two approaches indicating that a mean of best 10% solutions is a better strategy while ANN was worst performer showing highest bias for all the parameters. In all the three inversion techniques, the general order of retrieval accuracy was LAI > Cab > CCC > Cw. The range of Cw was very narrow and none of the techniques could estimate variations in it. In most of the cases, the parameters were underestimated by model inversion. The best identified LUT-II technique was then applied to retrieve wheat LAI from IRS LISS-3 satellite image of 5-Feb-2012 in Sheopur district. The comparison with ground observations showed that the RMSE of LAI retrieval was about 0.56, similar to that observed in ground experimentation. The findings of this study may help in refining the protocol for generating operational crop biophysical products from IRS LISS-3 or similar sensors.Indian Agricultural Research Institut

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Not AvailableThe study was carried out for Indian capital city Delhi using Hyperion sensor onboard EO-1 satellite of NASA. After MODTRAN-4 based atmospheric correction, MNF, PPI, and n-D visualizer were applied and endmembers of 11 LCLU classes were derived which were employed in the classification of LULC. To incur better classification accuracy, a comparative study was also carried out to evaluate the potential of three classifier algorithms namely Random Forest (RF), Support Vector Machines (SVM) and Spectral Angle Mapper (SAM). The results of this study reemphasize the utility of satellite-borne hyperspectral data to extract endmembers and also to delineate the potential of the random forest as an expert classifier to assess land cover with higher classification accuracy that outperformed the SVM by 19% and SAM by 27% in overall accuracy. This research work contributes positively to the issue of land cover classification through exploration of hyperspectral endmembers. The comparison of classification algorithms’ performance is valuable for decision-makers to choose better classifier for more accurate information extraction.Not Availabl

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Not AvailableThe algorithms for deriving vegetation biophysical parameters rely on the understanding of bi-directional interaction of radiation and its subsequent linkages with canopy radiative transfer models and their inversion. In this study, an attempt has been made to define the geometry of sensor and source position to best relate plant biophysical parameters with bidirectional reflectance of wheat varieties varying in canopy architecture and to validate the performance of PROSAIL (PROSPECT+SAIL) canopy radiative transfer model. A field experiment was conducted with two wheat cultivars varying in canopy geometry and phenology. The bidirectional measurements between 400nm–1100nm at 5nm interval were recorded every week at six view azimuth and four view zenith positions using spectro-radiometer. Canopy biophysical parameters were recorded synchronous to bi-directional reflectance measurements. The broadband reflectances were used to compute the NDVIs which were subsequently related to leaf area index and biomass. Results showed that the bidirectional reflectance increased with increase in view zenith from 200 to 600 irrespective of the sensor azimuth. For a given view zenith, the reflectance was observed to be maximum at 1500 azimuth where the difference between the sun and sensor azimuth was least. For sun azimuth of 1600 and zenith of 520, the view geometry defined by 1500 azimuth and 500 zenith corresponded to hotspot position. The measured bidirectional NDVI had significant logarithmic relationship with LAI and linear relationship with biomass for both the varieties of wheat and maximum correlation of NDVI with LAI and with biomass was obtained at the hotspot position. The PROSAIL validation results showed that the model simulated well the overall shape of spectra for all combination of view zenith and azimuth position for both wheat varieties with overall RMSE less than 5 per cent. The hotspot and dark spot positions were also well simulated and hence model performance may be suitable for deriving wheat biophysical parameters using satellite derived reflectances.Not Availabl

Robust NIRS Models for Non-Destructive Prediction of Physicochemical Properties and ageing of Basmati Rice

Aim: To determine physicochemical properties and age of rice by non-destructive technique. Place and Duration of Study: Study was conducted at Division of Food Science and Postharvest Technology, Indian Agricultural Research Institute, New Delhi during 2020 to 2021.  Methodology: Rice were kept for accelerated aging at 42.6°C temperature & 71% RH for a duration of 30 days. Changes in four physicochemical properties namely amylose content, volume expansion ratio (VER), water absorption ratio (WAR), and kernel elongation ratio (KER) were evaluated destructively (by spectrophotometer and cooking method) and non-destructively (by spectroradiometer) at every alternate day, during 30 days storage. Results: The physicochemical parameters of rice showed a good correlation with spectral signatures.  Subsequently, Principal component Analysis (PCA), Partial Least Square Regression (PLSR), and Multiple Linear Regression (MLR) were used to model the physicochemical changes occurring during the process of accelerated aging using spectral reflectance values. Based on values of Coefficient of determination (R²) and Root mean square error (RMSE) accuracy of models was determined. Predictions with the MLR model resulted in a coefficient of determination (R2) of 0.82, 0.87, 0.9,7, 0.83 and 0.82 with root mean square error (RMSE) of 0.18, 0.13, 0.21, 0.124 and 4.2 for amylose content, VER, WAR, KER, and ageing process respectively for calibration. Conclusion: The study demonstrated the potential of NIRS in non-destructively predicting the physiochemical parameters of rice

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Not AvailableA field experiment was conducted during the winter (rabi) season of 2009–10 and 2010–11 on a sandy-loam soil in New Delhi, to study the effect of nitrogen levels on the water, radiation and nitrogen-use efficiencies of wheat [Triticum aestivum (L.) emend. Fiori. & Paol.] cultivars. The treatments comprising 2 wheat cultivars (‘PBW 502’ and ‘DBW 17’) and 3 nitrogen levels (N0: 0kg N/ha, N60: 60 kg N/ha and N120: 120 kg N/ha) were laid out in factorial randomized block design (RBD). Both cultivars were statistically at par for grain yield, above-ground biomass yield, water-use efficiency (WUE) and radiation-use efficiency (RUE). Treatments N120 registered 71% and 25% higher grain yield than N0 and N60 treatments respectively. Water-use efficiency (WUE) of N120 (9.92 kg/ha/mm) was significantly highest, followed by N60 (8.40 kg/ha/mm) and N0 (6.56 kg/ha/mm) treatments. Similarly, radiation-use efficiency (RUE) of N120 (2.49 gm/MJ) was significantly higher than N60 (1.90 gm/MJ) and N0 (1.85 gm/MJ) treatments. The partial factor productivity of nitrogen (PFPN) of the cultivar ‘PBW 502’ (48.96 kg grain/kg nitrogen applied) was significantly higher than that of ‘DBW 17’ (42.23 kg grain/kg nitrogen applied). Nitrogen @ 60 kg/ha (N60) showed significantly higher PFPN (56.08 kg grain/kg nitrogen applied) than N120 (35.10 kg grain/kg nitrogen applied) treatment. Therefore, cultivar ‘PBW 502’ or cultivar ‘DBW 17’ can be grown with 120 kg N/ha for obtaining higher grain yield, above ground biomass, WUE and RUE in the semi-arid tropical environment of Delhi region.Not Availabl