Estimation of Dynamic Canopy Variables Using Hyperspectral Derived Vegetation Indices Under Varying N Rates at Diverse Phenological Stages of Rice

Non-destructive and rapid estimation of canopy variables is imperative for predicting crop growth and managing nitrogen (N) application. Hyperspectral remote sensing can be used for timely and accurate estimation of canopy physical and chemical properties; however, discrepancies associated with soil and water backgrounds complicate the estimation of crop N status using canopy spectral reflectance (CSR). This study established the quantitative relationships between dynamic canopy nitrogen (CN) status indicators, leaf dry weight (LDW), leaf N concentration (LNC), leaf N accumulation (LNA), and CSR-derived new hyperspectral vegetation indices (HVIs), and to access the plausibility of using these relationships to make in-season estimations of CN variables at the elongation (EL), booting (BT), and heading (HD) stages of rice crop growth. Two-year multi-N rate field experiments were conducted in 2015 and 2016 in Hubei Province, China, using the rice cultivar Japonica. The results showed that the sensitive spectral regions were negatively correlated with CN variables in the visible (400–720 nm and 560–710 nm) regions, and positively correlated (r > 0.50, r > 0.60) with red and NIR (720–900 nm) regions. These sensitive regions are used to formulate the new (SR777/759, SR768/750) HVIs to predict CN variables at the EL, BT, and HD stages. The newly developed stepwise multiple linear regression (SMLR) models could efficiently estimate the dynamic LDW at the BT stage and LNC and LNA at the HD stage. The SMLR models performed accurately and robustly when used with a validation data set. The projected results offer a suitable approach for rapid and accurate estimation of canopy N-indices for the precise management of N application during the rice growth period

Studies on crystal growth of some pure and mixed rare-earth Fumarates and their characteristics.

Crystal growth and its characterization has become valuable branch of science due to the growing demand of scientifically and technologically important crystals for different applications. Rare-earth based crystals of metal organic coordination compounds have an important role from both academic and technological point of view due to their outstanding physical, magnetic and luminescent properties. The compounds of rare-earths have also gained importance for their dielectric, ferroelectric, ferroelastic and conductivity behaviour. Due to the large applicability of rare-earth coordination compounds, it was thought worthwhile to investigate growth and characterization of tebium fumarate, gadolinium fumarate and mixed gadolinium-terbium fumarate heptahydrate single crystals for scientific investigations. The work presented in this thesis was carried out at the Solid-State Research Laboratory, Department of Physics, University of Kashmir, Srinagar. The thesis entitled “Studies on crystal growth of some pure and mixed rare-earth fumarates and their characteristics” is a comprehensive report on the growth of single crystals of terbium, gadolinium and mixed gadolinium-terbium fumarate heptahydrates in hydro silica gel and their detailed characterization. It also includes the results of dielectric, ac conductivity, thermal, luminescent and magnetic moment measurements of the grown crystals. The thesis is divided into two sections, Section-A pertaining to growth and characterization of crystals, consists of four chapters and section-B including the physical properties of as-grown crystals consists of five chapters.Digital copy of Thesis.University of Kashmir

Interactive effects of vanadium and phosphorus on their uptake, growth and heat shock proteins in chickpea genotypes under hydroponic conditions

The present study was carried out to examine the interaction of vanadium and phosphorus and changes in heat shock genes to optimize the growth of chickpea genotypes. Two sets of hydroponic experiments were carried out using vanadium and phosphorus with five-level central composite design. Five levels of vanadium (0-1180 mu M) and phosphorus (0-100011 mu M) were used to evaluate their interactive effects. Plants fresh biomass and uptake of vanadium and phosphorus were influenced by vanadium and phosphorus application. Enhanced fresh biomass was most likely a result of increased phosphorus uptake by chickpea genotypes. Addition of vanadium induced toxic effects while, higher concentration of phosphorus alleviated its toxic effects. The obtained results also indicated that lower vanadium concentration promoted phosphorus absorption however; higher concentration of vanadium inhibited the phosphorus uptake. The morphological changes in leaves indicated that the cells were deformed and reduced in size when treated with higher vanadium levels with fixed phosphorus while, there was little deformation and reduction in cells size were observed when plants were treated with higher levels of phosphorus with fixed vanadium. Whereas, the proportion of deformation of cells were higher in Balkasar as compared to C-44 genotype. The results also showed that at elevated vanadium with fixed phosphorus, Hsp70 was expressed only in C-44 while, not in Balkasar however, Hsp90 and GAPDH showed non-significant results. (C) 2016 Elsevier B.V. All rights reserved

Metaheuristic Method for a Wind-Integrated Distribution Network to Support Voltage Stabilisation Employing Electric Vehicle Loads

Distributed generation (DG) has been incorporated into the distribution networks and, despite the rising prevalence of electric vehicle (EV) loads that are uncertain and cause substantial challenges in their operation, it is necessary to enhance the voltage profile, reduce power losses, and consequently improve the stability of whole networks. The recently proposed beluga whale optimisation algorithm is explored in the optimisation framework to determine the most suitable size of wind turbine generating systems (WTGS), while the optimum placements are determined by minimising the placement index (P-Index) using the distribution load flow (DLF) method. The voltage stability factor (VSF) is employed to formulate the P-Index to enhance voltage sensitivity in distribution systems. The main purpose of this article is to assess the influence of voltage-dependent, uncertain ZIP-form EV loads in order to analyse their potential in the active and reactive power operations of the distribution network while retaining the system voltage within a specified limit by significantly reducing system losses and taking distribution network-level constraints into account. The efficacy of the methodology is validated on the standard IEEE-33 test system by formulating two performance indices to determine a significant enhancement in convergence characteristics and a reduction in system losses

Type‐2 fuzzy‐based adaptively predictive controlled variable frequency transformer coordinated to SMES for improved load frequency control

Abstract Traditionally, phase shifters powered by power electronics are used in conjunction with energy storage devices to improve the load frequency control. Conventional phase shifters provide minimal inertia and offer a significant threat to power quality due to harmonics. This study suggests the type‐2 fuzzy based adaptively predictive controlled variable frequency transformer (F2‐APC‐VFT) as a phase shifter to work in coordination with superconducting magnetic energy storage (SMES) for improved load frequency control. To assess the effectiveness of coordinated control, a two‐area, four‐machine linked power system with F2‐APC‐VFT installed in succession with the connecting line near to area one and SMES in the other area is employed. A type‐2 fuzzy controller is used to tune the cost function weights of the adaptive predictive controller (APC). A simulation study is conducted in MATLAB to show the effectiveness of the suggested strategy. Tuning the cost function weights with a type‐2 fuzzy controller considerably impacts in minimising the frequency and tie‐power deviations. The effectiveness of the F2‐APC is verified by comparing its performance with that of an adaptive neuro fuzzy inference system (ANFIS) controller and constant weight‐based adaptive predictive controller (C‐APC)

Assessment of UAV-Onboard Multispectral Sensor for Non-Destructive Site-Specific Rapeseed Crop Phenotype Variable at Different Phenological Stages and Resolutions

Unmanned aerial vehicles (UAVs) equipped with spectral sensors have become useful in the fast and non-destructive assessment of crop growth, endurance and resource dynamics. This study is intended to inspect the capabilities of UAV-onboard multispectral sensors for non-destructive phenotype variables, including leaf area index (LAI), leaf mass per area (LMA) and specific leaf area (SLA) of rapeseed oil at different growth stages. In addition, the raw image data with high ground resolution (20 cm) were resampled to 30, 50 and 100 cm to determine the influence of resolution on the estimation of phenotype variables by using vegetation indices (VIs). Quadratic polynomial regression was applied to the quantitative analysis at different resolutions and growth stages. The coefficient of determination (R2) and root mean square error results indicated the significant accuracy of the LAI estimation, wherein the highest R2 values were attained by RVI = 0.93 and MTVI2 = 0.89 at the elongation stage. The noise equivalent of sensitivity and uncertainty analyses at the different growth stages accounted for the sensitivity of VIs, which revealed the optimal VIs of RVI, MTVI2 and MSAVI in the LAI estimation. LMA and SLA, which showed significant accuracies at (R2 = 0.85, 0.81) and (R2 = 0.85, 0.71), were estimated on the basis of the predicted leaf dry weight and LAI at the elongation and flowering stages, respectively. No significant variations were observed in the measured regression coefficients using different resolution images. Results demonstrated the significant potential of UAV-onboard multispectral sensor and empirical method for the non-destructive retrieval of crop canopy variables