Manjeet Singh

Calibration and Algorithm Development for Estimation of Nitrogen in Wheat Crop Using Tractor Mounted N-Sensor

The experiment was planned to investigate the tractor mounted N-sensor (Make Yara International) to predict nitrogen (N) for wheat crop under different nitrogen levels. It was observed that, for tractor mounted N-sensor, spectrometers can scan about 32% of total area of crop under consideration. An algorithm was developed using a linear relationship between sensor sufficiency index (SIsensor) and SISPAD to calculate the Napp as a function of SISPAD. There was a strong correlation among sensor attributes (sensor value, sensor biomass, and sensor NDVI) and different N-levels. It was concluded that tillering stage is most prominent stage to predict crop yield as compared to the other stages by using sensor attributes. The algorithms developed for tillering and booting stages are useful for the prediction of N-application rates for wheat crop. N-application rates predicted by algorithm developed and sensor value were almost the same for plots with different levels of N applied.

Expansion of Empirical-Statistical Based Topographic Correction Algorithm for Reflectance Modeling on Himalayan Terrain using AWiFS and MODIS Sensor

Irregular shape of terrain causes variable illumination angles and diverse reflectance values within same land cover type in optical remote sensing image. It causes problems in image segmentation and misclassification (snow with other land cover). This perception leads to develop an empirical-statistical based topographic correction (ESbTC) algorithm for reflectance modeling after compared with existed topographic correction methods like Cosine correction, C-correction, Minnaert correction, sun–canopy–senor with c-correction (SCS + C) and slope matching, in the context of snow reflectance. An image based atmospheric correction has used in present study included dark-object subtraction (DOS) and effect of Rayleigh scattering on the transmissivity in different spectral bands of AWiFS and MODIS image data. The performance of different models is evaluated using (1) visual analysis, (2) change in snow reflectance on sunny and shady slopes after the corrections, (3) validation with in situ observations and (4) graphical analysis. Further snow cover area (SCA) has been estimated with normalized difference snow index (NDSI) and validated with support vector machine (SVM), a supervised classification technique. The result shows that the proposed algorithm (ESbTC) and slope-matching technique could eliminate most of the shadowing effects in Himalayan rugged terrain and correctly estimate snow reflectance from AWiFS and MODIS imagery as compared with in situ observations whereas other methods significantly underestimate reflectance values after the corrections.

Thermoelastic Plane Waves at Imperfectly Bonded Interface of Generalized Thermoelastic Cubic-Crystal Solids

Thermoelastic plane wave propagation at an imperfect interface between two generalized thermoelastic cubic crystal solid half-spaces of different elastic and thermal properties with one relaxation time has been studied. The thermoelastic theory with one relaxation time developed by Lord and Shulman [1] has been used to study the problem. The amplitude ratios of different reflected and transmitted waves are obtained for an imperfect boundary and deduced for normal stiffness, transverse stiffness, thermal contact conductance, slip and welded boundaries. The variation of amplitude ratios with angle of emergence has been shown graphically. It is noticed that the amplitude ratios of reflected and transmitted waves are affected by the stiffness and thermal properties of the media.

Economic Analysis and Feasibility of Tractor Operated Cotton Harvesters

India is the first country in having a maximum area under cotton crop, second in yarn production and third in raw cotton production in the world. However, entire cotton is picked by human labour involving about 517 man h/ha. A 3-row finger type cotton stripper and single row cotton picker have been developed indigenously and the cost economics has been calculated as per the standard procedure to determine the feasibility and economic viability of the machines. It has been estimated that the break-even point (BEP) calculated on an area basis for a cotton stripper and cotton picker was 1255 and 1543 ha respectively. The payback period calculated on year basis for the cotton stripper and cotton picker was found 3.17 and 6.43 years respectively. Cost of harvesting done by the cotton stripper and cotton picker can save cost up to 57.48 and 44.42 percent respectively as compared to the hand picking.

An advance air-induced air-assisted electrostatic nozzle with enhanced performance

Electrostatic force field has been exploited in the development of an air-induced air-assisted electrostatic nozzle.Parameters are optimized and engineering design aspects have been addressed.Charge to mass ratio of 10mC/kg at voltage less than 2.5kV and electric power consumption less than 75mW.The deposition and uniformity has increased on both front and backside of the leaves. There is a pressing need of new chemical application sprayer for small scale farms in Indian agricultural pesticides spraying. A new air-assisted electrostatic nozzle has been designed and developed for small scale farms with a specific focus on Indian agricultural and rural developing economies. An air-assisted electrostatic nozzle is a combination of an air-assisted nozzle and induction based electrostatic charging system. Spray droplets are electrified to more than 10mC/kg charge-to-mass ratio by charging voltage less than 2.5kV at liquid flow of 150ml/min and electric power consumption less than 75mW. Higher charge-to-mass ratio ensured the high range spraying distance to overcome the charge neutralization by recombination of naturally occurring ions present in the environment and charged liquid droplets. The results of applied induction electrification process were characterized by a charge-to-mass ratio and the results are in good agreement with the theoretical considerations. There has been 23 fold increase of liquid deposition with better uniformity on the obscured as well as front target. This nozzle is light weight, highly efficient, reduces pesticide use and human health risks, and eco-friendly.

Ground-based hyperspectral remote sensing to discriminate biotic stress in cotton crop

A large gap exists between the potential yield and the yield realized at the agricultural field. Among the factors contributing towards this yield gap are the biotic stresses that affect the crops growth and development. Severity of infestation of the pests and diseases differs between agroclimatic region, individual crops and seasons within a region. Information about the timing of start of infestation of these diseases and pests with their gradual progress in advance could enable plan necessary pesticide schedule for the season, region on the particular crop against the specific menace expected. This could be enabled by development of region, crop and pest-specific prediction models to forewarn these menaces. In India most (70%) of the land-holding size of farmers average 0.39 ha (some even 20 m x 20 m) and only 1% crop growers hold< 10 ha (mean: 17.3 ha). Patchiness of disease and pest incidence could pose problems in its proper assessment and management. Thus, such exercise could be highly time-consuming and labour-intensive for the seventh largest country with difficult terrain, 66% gross cropped area under food crops, lacking in number of skilled manpower and shrinking resources. Remote sensing overcomes such limitations with ability to access all parts of the country and can often achieve a high spatial, temporal and spectral resolution and thus leading to an accurate estimation of area affected. Due to pest and disease stress plants showed different behavior in terms of physiological and morphological changes lead to symptoms such as wilting, curling of leaf, stunned growth, reduction in leaf area due to severe defoliation or chlorosis or necrosis of photosynthetically active parts (Prabhakar et al., 2011; Booteet al., 1983; Aggarwal et al., 2006). Damage evaluation of diseases has been largely done by visual inspections and quantification but visual quantification of plant pest and diseases with accuracy and precision is a tough task. Utilization of remote sensing techniques are based on the assumption that plant pest and disease stresses interfere with physical structure and function of plant and influence the absorption of light energy and therefore changes the reflectance spectrum of plants. Moreover, remote sensing provides better means to objectively quantify crop stress than visual methods and it can be used repeatedly to collect sample measurements non-destructively and non-invasively (Nutteret et al., 1990; Nilson, 1995). Recent advances in the field of spectroscopy and other remote sensing techniques offer much needed technology of hyperspectral remote sensing (Prabhakar et al., 2011). Hyperspectral remote sensing for disease detection helps in monitoring the diseases in plants with the help of different plant spectral properties at the visible, near infrared and shortwave infrared regions ranging from 350 – 2500 nm, which develops specific signatures for a specific stress for a given plant (Yang et al., 2009). It has been effectively used in assessment of disease in agricultural crops like wheat, rice, tomato etc across the world. Cotton (Gissypium hirsutum L.) is one of the major commercial crops grown in India, and supports about 60 million people in the country directly or indirectly through the process of production, processing, marketing and trade (Prabhakar et al., 2011). India ranks first in global acreage, occupying about 33% of world cotton area. With regard to production it is ranked second next to China. In recent years, farmers are facing many challenges because of rising incidents of white flies, jassid, leafhoppers, aphids, mealybugs and stainers. Whiteflies are tiny, sap- sucking insects that may become abundant in vegetable and ornamental plantings, especially during warm weather. They excrete sticky honeydew and cause yellowing or death of leaves. Outbreaks often occur when the natural biological control is disrupted. Management is difficult once populations are high. White flies develop rapidly in warm weather, and populations can build up quickly in situations where natural enemies are ineffective and when weather and host plants favor outbreaks. Large colonies often develop on the undersides of leaves. The most common pest species such as greenhouse white fly (Trialeurodes vaporariorum) and sweet potato white fly (Bemisia tabaci) have a wide host range that includes many weeds and crops. White flies normally lay their tiny oblong eggs on the undersides of leaves. The eggs hatch, and the young white flies gradually increase in size through four nymphal stages called instars. The first nymphal stage (crawler) is barely visible even with a hand lens. The crawlers move around for several hours before settling to begin feeding. Later nymphal stages are immobile, oval, and flattened, with greatly reduced legs and antennae, like small scale insects. The winged adult emerges from the last nymphal stage (sometimes called a pupa, although whiteflies don’t have a true complete metamorphosis). All stages feed by sucking plant juices from leaves and excreting excess liquid as drops of honeydew as they feed. White flies use their piercing, needle like mouthparts to suck sap from phloem, the food-conducting tissues in plant stems and leaves. Large populations can cause leaves to turn yellow, appear dry, or fall off plants. Like aphids, white flies excrete sugary liquid called honeydew, so leaves may be sticky or covered with black sooty mold that grows on honeydew. The honeydew attracts ants, which interfere with the activities of natural enemies that may control white flies and other pests. High white fly infestation was reported at several locations in Punjab during year 2015. The application of non-destructive methods to detect vegetation stress at an early stage of its development is very important for pest management in commercially important crops. Earlier few studies have been done to characterize reflectance spectra of nutrient stress nitrogen deficiency and irrigation management for cotton but no literature is available regarding characterization of spectral reflectance to study white fly infestation. Therefore, the primary objectives of this study are: (i) to study changes in chlorophyll content and water content due to white fly infestation. (ii) characterization of spectral signature from cotton crop infested by white fly, (iii) establishment of most sensitive wavebands to white fly infestation.

Snow Model for AWiFS Derived Broad Band Albedo with an Application to Onset Seasonal Melt in North-West Himalaya

Albedo is a critical snow physical parameter that affects the earth’s climate system directly by altering the energy balance at the ground surface and indirectly by controlling the ecosystem processes. Thus spatial variability of snow albedo has immense importance in the study of geomorphology, climate dynamics, seasonal snow melt and hydrology. This paper describes and examines the retrieval of snow albedo for the period October 2012 to May 2013 by using multispectral Advance Wide Field Sensor (AWiFS) on board IRS-P6 of RESOURCESAT-1. The analysis procedure to compute spectral reflectance is achieved by converting the digital numbers after an image based atmospheric and topographic correction that include dark object subtraction (DOS) and effect of Rayleigh scattering on the transmissivity in different spectral bands of AWiFS images. Snow spectral reflectance and satellite derived snow albedo has been validated with in-situ data at the time of satellite pass over the study area using spectroradiometer, pyronometer and Automated Weather Station (AWS) respectively. A fine agreement between satellite derived snow albedo and in-situ measurements shows the relative accuracy of model. Present study also reveal the temporal and spatial variability of snow albedo in a basin which is an evidently indication of the seasonal melt due to decreasing trend in snow depth, snow cover area (SCA) and increased degree day temperature.