Papers by Rabi Narayan Sahoo
Journal of the Indian Society of Agricultural Statistics, May 10, 2024
Additionally, determining the optimal timing for harvesting during the grain maturity stage is cr... more Additionally, determining the optimal timing for harvesting during the grain maturity stage is crucial to maximize the economic value of the crop. Understanding and identifying different stages of paddy enables researchers to develop appropriate crop management practices such as optimized nutrient application and irrigation strategies. Different stages of
International Journal of Environment and Climate Change
Soil microbiological properties viz. soil microbial biomass carbon (MBC) and dehydrogenase activi... more 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 s...
Frontiers in Plant Science
Among seed attributes, weight is one of the main factors determining the soybean harvest index. R... more Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually ...
Plants
Drought is a detrimental factor to gaining higher yields in rice (Oryza sativa L.), especially am... more Drought is a detrimental factor to gaining higher yields in rice (Oryza sativa L.), especially amid the rising occurrence of drought across the globe. To combat this situation, it is essential to develop novel drought-resilient varieties. Therefore, screening of drought-adaptive genotypes is required with high precision and high throughput. In contemporary emerging science, high throughput plant phenotyping (HTPP) is a crucial technology that attempts to break the bottleneck of traditional phenotyping. In traditional phenotyping, screening significant genotypes is a tedious task and prone to human error while measuring various plant traits. In contrast, owing to the potential advantage of HTPP over traditional phenotyping, image-based traits, also known as i-traits, were used in our study to discriminate 110 genotypes grown for genome-wide association study experiments under controlled (well-watered), and drought-stress (limited water) conditions, under a phenomics experiment in a c...
Indian Journal of Agricultural Sciences, Oct 22, 2019
Prediction of fresh biomass is the key for evaluation of the response of crop genotypes to divers... more Prediction of fresh biomass is the key for evaluation of the response of crop genotypes to diverse input and stress conditions, and forms basis for calculating net primary production. Hence, accurate and high throughput estimation of fresh biomass is critical for plant phenotyping. As conventional phenotyping approaches for measuring fresh biomass is time consuming, laborious and destructive, image based phenotyping methods are being widely used now in plant phenotyping. However, current approaches for estimating fresh biomass of plants are based on projected shoot area estimated from the visual (VIS) image. These approaches do not consider the water content of the plant tissues which are about 70-80% in leafy vegetation. Since water absorbs radiation in the Near Infra-Red (NIR) (900-1700 nm) region, it has been hypothesized that combined use of VIS and NIR imaging can predict the fresh biomass more accurately that the VIS image alone. In this study, VIS and NIR imaging were captured for rice leaves with different moisture content as a test case. For background subtraction from NIR image, PlantCV v2 NIR imaging algorithm was implemented in MATLAB software (version 2015b). The proposed image derived parameter, viz. Green Leaf Proportion (GLP) from VIS image and mean gray value/intensity (NIR_MGI) from NIR image were used as input to develop Artificial Neural Network (ANN) model to estimate the Leaf Fresh Weight (LFW). This proposed approach significantly enhanced the fresh biomass prediction as compared to the conventional regression technique based on projected shoot area derived from VIS image.
International Journal of Plant & Soil Science
Phosphorus (P) and potassium (K) are two major nutrients for agricultural productivity and sustai... more 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 fol...
The Indian Journal of Agricultural Sciences
Rapid and accurate prediction of soil available S, an important secondary nutrient, is crucial fo... more Rapid and accurate prediction of soil available S, an important secondary nutrient, is crucial for its site-specific management in a cultivated region. Although traditional chemical analysis of any nutrient is an accurate method, but often costly, time-consuming and destructive in nature. Recently visible near-infrared (VIS-NIR) reflectance spectroscopic technique has gained its popularity for rapid, non-destructive and cost-effective assessment of soil nutrients. Hence, a study was carried out in an intensively cultivated region of Katol block of Nagpur, Maharashtra, during 2018-20 for rapid prediction of soil available S using spectroscopic technique. Both spectroscopic and chemical analyses were carried out using 132 georeferenced surface soil samples (0-15 cm depth). The descriptive statistical analysis showed that the available S content varied from 1.09 to 47.88 mg/kg. Multivariate models namely partial least square regression (PLSR) and random forest (RF) were applied to deve...
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
Leaf count is one of the crucial tasks in plant phenotyping, and leaves are the basic unit of pla... more Leaf count is one of the crucial tasks in plant phenotyping, and leaves are the basic unit of plant architecture involved in photosynthesis, growth, and yield of a plant. Therefore, the total number of leaves per plant is considered as one of the essential physio-morphological plant traits for phenotyping. The current work proposes to estimate the total number of leaves of a rice plant by detecting their leaves tips. A rice plant has a single tip for a single leaf. Hence, this proposed framework counts the total number of leaves by counting the number of leaves tips equal to the number of leaves. You Only Look Once (YOLO) algorithm is used for the detection of the leaves tips as an object. This hypothesis builds a basis for counting the total number of leaves in a plant like rice, and similar field crops such as wheat (Triticum aestivum L), maize (Zea mays L.), sorghum (Sorghum bicolor), barley (Hordeum vulgare L.). The model detected leaves of a rice plant (RGB images) by detecting corresponding leaves tips with YOLO having average accuracy up to 82% and IOU around 0.53-0.60 and estimates the number of leaves in a plant by counting predicted bounding boxes around tips. The model also performed well with the wheat crop.
Additional file 1. Names of the genotypes used for the study.
Advances in Bioremediation and Phytoremediation for Sustainable Soil Management, 2022
Remote Sensing, 2021
Conventional methods of plant nutrient estimation for nutrient management need a huge number of l... more Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An appr...
Geocarto International, 2019
This study assessed the effect of atmospheric correction algorithms, inversion techniques and ima... more This study assessed the effect of atmospheric correction algorithms, inversion techniques and image spatial and spectral resolution on wheat crop LAI retrieval using Sentinel-2 MSI and Landsat-8 OLI imagery. The LAI retrievals were validated with insitu measurements collected in farmers' fields. The MSI-based LAI retrievals improved significantly when images were atmospherically corrected using MODTRAN than using the libRadtran code. Among the two PROSAIL inversion approaches, look-up table outperforms artificial neural network for LAI retrievals. Using the best strategy of atmospheric correction and inversion, the effect of spatial resolution from 20 m (MSI) to 30 m (OLI) while using common six bands, showed non-significant improvement in LAI retrievals. The inclusion of additional two red-edge bands as available in MSI significantly reduced the uncertainly in LAI retrievals over that obtained by using six bands, while inclusion of only additional VNIR band did not show any significant effect on LAI retrievals.
Journal of the Indian Society of Remote Sensing, 2017
The virtual certainty of the anticipated climate change will continue to raise many questions abo... more The virtual certainty of the anticipated climate change will continue to raise many questions about its aggregated impact of environmental changes on our regional food security in imminent future. Crop responses to these changes are certain, but its exact characteristics are hardly understood at regional scale due to complex overlapping effects of climate change and anthropogenic manipulation of agro-ecosystem. This study derived phenology of wheat in north India from satellite data and analyzed trends of phenology parameters over last three decades. The most striking change-point period in phenology trends were also derived. The phenology was derived from two sources: (1) STAR-Global vegetation Health Products-NDVI, and (2) GIMMS-NDVI. The results revealed significant earliness in start of growing season (SOS) in Punjab and Haryana while delay was found in Uttar Pradesh (UP). End of the wheat season almost always occurred early, to even those place where SOS was delayed. Length of growing season increased in most of Punjab and northern Haryana whereas its decrease dominated in UP. The early sowing practice of the farmers of the Punjab and Haryana may be one of the adaptation strategies to manage the terminal heat stress in reproductive stage of the crop in the region. The change-point occurred in late 1990s (1998–2000) in Punjab and Haryana, while in eastern UP it was in early 1990s (1990–1995). Despite the difference in temporal aggregation and spatial resolution, both the datasets yielded similar trends, confirming both the robustness of the results and applicability of the datasets over the region. The results demands further research for proper attribution of the effects into its causes and may help devising crop adaption practices to climatic stresses.
Journal of the Indian Society of Soil Science, 2016
The hydraulic properties of vadoze zone like hydraulic conductivity, diffusivity and soil water r... more The hydraulic properties of vadoze zone like hydraulic conductivity, diffusivity and soil water retention characteristics are required to study soil water, nutrient and pollutant dynamics and their management. Surface layer of ten master profiles of Indian Agricultural Research Institute (IARI) farm, New Delhi were studied for different physicochemical and hydraulic properties. All ten master profiles were grouped under the order Inceptisol, subgroup Typic Haplustept and hyperthermic soil temperature regime. The soil water retention data were measured for all the sample layers and fitted to van Genuchten model using RETC software. The R2 value of fitted curve varied between 0.98 to 0.99 with residual sum of squares from 0.001 to 0.003. The θr value varied between 0.0332 to 0.0760 cm3 cm−3 and the saturated water content between 0.4665 to 0.3613 cm3 cm−3. In the present study, α ranged between 0.0018 to 0.0164 cm−1. The n value varied between 1.381 to 2.437. Combined for all profiles the prediction equation developed for K(θ) is K(θ) = 1.17×106×θ11.93 and D(θ) is D(θ) = 2.07×107× θ7.71 with R2 value of 0.77 both for K(θ) and D(θ). The Sindex varied between 0.068 to 0.128. The S-index was significantly positively correlated with θr (r = 0.73*), θs (r = 0.64*), n (r = 0.97**) and significantly negatively correlated with α (r = -0.80**). The S-index value for all the 10 master profiles were more than 0.050 indicating very good physical quality for all the 10 master profiles of IARI experimental farm.
Journal of the Indian Society of Remote Sensing, 2016
A field experiment was conducted on wheat during rabi season of year 2010-2011 and 2011-2012 at I... more A field experiment was conducted on wheat during rabi season of year 2010-2011 and 2011-2012 at IARI, New Delhi to study the reflectance response of wheat to the nutrient omissions and identify the appropriate indices for assessing the nutrient deficiencies. Treatments comprised omission of N, P, K, S and Zn, 50% omission of N, P, and K, absolute control and optimum dose of nutrition (150-26.4-50-15-3 kg/ha N-P-K-S-Zn). The R 2 were significant and higher for the hyperspectral indices than the broad band vegetation indices. GMI-I, RI-2 dB and RI-3d, GNDVI, VOGa, VOGb, VOGc, ND 705 , PRI, PSNDc and REIP had higher R 2 ([0.61) for the leaf N concentration. The hyperspectral indices having highly significant correlation with leaf P concentration were PSSRc, GMI-1, ZM, RI-half, VOGa, VOGb, VOGc, mSR and REIP. Among the indices analysed PSSRc, GMI-I, VOGa, RI-2 dB, RI-3 dB, GNDVI, VOGb, VOGc and ND 705 had almost a similar degree of relationship with DM accumulation with R 2 values ranging from 0.70 to 0.73. However, REIP displayed a higher degree of relationship with leaf N concentration, drymatter accumulation and grain yield as indicated by R 2 of 0.85, 0.81 and 0.95 (P = B0.01), respectively. It can be concluded from the study that among the hyperspectral indices REIP had a highly significant relationship with leaf N concentration, DM accumulation and grain yield. However, for leaf P concentration several hyperspectral indices viz PSSRc, GMI-1, ZM, RI-half, VOGa, VOGb, VOGc, mSR had though significant but almost similar R 2 values.
: Leaf area index (LAI) of vegetation influences radiation interception, latent and sensible heat... more : Leaf area index (LAI) of vegetation influences radiation interception, latent and sensible heat fluxes, and CO2 exchange between terrestrial ecosystems and atmosphere. LAI is used as a key input parameter in many crop growth simulation and radiative transfer models. Conventional LAI measurements are usually time-consuming, and is taken at few representative sample sites only.Alternately, non invasiveestimation of LAIfrom digital image can be an inexpensive and reliable and faster option.The present study attempts to develop an approach for estimation of LAI using top-of canopy digital colour photography over wheat canopy. Different colour based vegetation indices such as Excess Green (ExG), Excess Red (ExR),Normalized Difference(NDI) and Excess Green minus Excess Red (ExG-ExR) Indices were developed from digital images. A histogram-based threshold technique was used to separate green vegetation tissues from background soil in order to derive the canopy vertical gap fraction. The i...
International Agrophysics, 2015
The inversion of canopy reflectance models is widely used for the retrieval of vegetation propert... more The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural ...
ABSTRACT Field study was conducted at PDCSR, Modipuram (U.P.) in we\! drained sandy loam soil (Ty... more ABSTRACT Field study was conducted at PDCSR, Modipuram (U.P.) in we\! drained sandy loam soil (Typic Ustochrept) in pigeon pea based cropping system with wheat (cv. PBW-226) in split-plot design. Treatments consisted of three planting geometry (main-plot) viz., bed planting system (M l), paired row system of planting (M 2) and conventional planting eM3) and four levels of nitrogen (sub-plot) viz., 0 (No)' 60 (N,) ,120 (N 2), and 180 (N 3) kg hal were replicated thrice. Due to exceptionally very high and well distributed precipitation of 168.2 mm between the harvest of preceding short duration pigeon pea and sowing of next wheat, it could be planted very late in the tirst week of January without pre-sowing irrigation. Crop emergence was delayed considerably and could be initiated on 12th day and completed by 18 th day after sowing due to excessive soil moisture. Relatively higher soil moisture content in the profile was found in M3 compared to M2 and it was appreciably higher in No compared to N 3 . Higher xylem water potential (XWp)'was observed in M j compared to M:! and was maximum in No and the minimum in N 3 . Highest solar radiation interception was found in M3 and the lowest in M2 and appreciably higher in N3 than No. Crop growth was superior in M 3 0ver M2 and considerably higher in N3 compared to NO' There was a net saving of 49. 1% of water in M1 compared to My Substantially higher grain yield (27.9%) was recorded in M3 compared to M 2 . Almost doubling of yield (97.6%) was found in N3 over No.
Precision Agriculture, 2014
ABSTRACT In situ, non-destructive and real time mineral nutrient stress monitoring is an importan... more ABSTRACT In situ, non-destructive and real time mineral nutrient stress monitoring is an important aspect of precision farming for rational use of fertilizers. Studies have demonstrated the ability of remote sensing to monitor nitrogen (N) in many crops, phosphorus (P) and potassium (K) in very few crops and none so far to monitor sulphur (S). Specially designed (1) fertility gradient experiment and (2) test crop experiments were used to check the possibility of mineral N–P–S–K stress detection using airborne hyperspectral remote sensing. Leaf and canopy hyperspectral reflectance data and nutrient status at booting stage of the wheat crop were recorded. N–P–S–K sensitive wavelengths were identified using linear correlation analysis. Eight traditional vegetation indices (VIs) and three proposed (one for P and two for S) were evaluated for plant N–P–S–K predictability. A proposed VI (P_1080_1460) predicted P content with high and significant accuracy (correlation coefficient (r) 0.42 and root means square error (RMSE) 0.180 g m−2). Performance of the proposed S VI (S_660_1080) for S concentration and content retrieval was similar whereas prediction accuracies were higher than traditional VIs. Prediction accuracy of linear regressive models improved when biomass-based nutrient contents were considered rather than concentrations. Reflectance in the SWIR region was found to monitor N–P–S–K status in plants in combination with reflectance at either visible (VIS) or near infrared (NIR) region. Newly developed and validated spectral algorithms specific to N, P, S and K can further be used for monitoring in a wheat crop in order to undertake site-specific management.
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Papers by Rabi Narayan Sahoo