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  1. Article ; Online: Determination of optimal daily light integral (DLI) for indoor cultivation of iceberg lettuce in an indigenous vertical hydroponic system

    Kishor P. Gavhane / Murtaza Hasan / Dhirendra Kumar Singh / Soora Naresh Kumar / Rabi Narayan Sahoo / Wasi Alam

    Scientific Reports, Vol 13, Iss 1, Pp 1-

    2023  Volume 15

    Abstract: Abstract The indoor cultivation of lettuce in a vertical hydroponic system (VHS) under artificial lighting is an energy-intensive process incurring a high energy cost. This study determines the optimal daily light integral (DLI) as a function of ... ...

    Abstract Abstract The indoor cultivation of lettuce in a vertical hydroponic system (VHS) under artificial lighting is an energy-intensive process incurring a high energy cost. This study determines the optimal daily light integral (DLI) as a function of photoperiod on the physiological, morphological, and nutritional parameters, as well as the resource use efficiency of iceberg lettuce (cv. Glendana) grown in an indoor VHS. Seedlings were grown in a photoperiod of 12 h, 16 h, and 20 h with a photosynthetic photon flux density (PPFD) of 200 µmol m−2 s−1 using white LED lights. The results obtained were compared with VHS without artificial lights inside the greenhouse. The DLI values for 12 h, 16 h, and 20 h were 8.64, 11.5, and 14.4 mol m−2 day−1, respectively. The shoot fresh weight at harvest increased from 275.5 to 393 g as the DLI increased from 8.64 to 11.5 mol m−2 day−1. DLI of 14.4 mol m−2 day−1 had a negative impact on fresh weight, dry weight, and leaf area. The transition from VHS without artificial lights to VHS with artificial lights resulted in a 60% increase in fresh weight. Significantly higher water use efficiency of 71 g FW/L and energy use efficiency of 206.31 g FW/kWh were observed under a DLI of 11.5 mol m−2 day−1. The study recommends an optimal DLI of 11.5 mol m−2 day−1 for iceberg lettuce grown in an indoor vertical hydroponic system.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2023-07-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Synthesis and Performance Evaluation of Novel Bentonite-Supported Nanoscale Zero Valent Iron for Remediation of Arsenic Contaminated Water and Soil

    Md Basit Raza / Siba Prasad Datta / Debasis Golui / Mandira Barman / Tapas Kumar Das / Rabi Narayan Sahoo / Devi Upadhyay / Mohammad Mahmudur Rahman / Biswaranjan Behera / A Naveenkumar

    Molecules, Vol 28, Iss 2168, p

    2023  Volume 2168

    Abstract: Groundwater arsenic (As) pollution is a naturally occurring phenomenon posing serious threats to human health. To mitigate this issue, we synthesized a novel bentonite-based engineered nano zero-valent iron (nZVI-Bento) material to remove As from ... ...

    Abstract Groundwater arsenic (As) pollution is a naturally occurring phenomenon posing serious threats to human health. To mitigate this issue, we synthesized a novel bentonite-based engineered nano zero-valent iron (nZVI-Bento) material to remove As from contaminated soil and water. Sorption isotherm and kinetics models were employed to understand the mechanisms governing As removal. Experimental and model predicted values of adsorption capacity ( q e or q t ) were compared to evaluate the adequacy of the models, substantiated by error function analysis, and the best-fit model was selected based on corrected Akaike Information Criterion (AICc). The non-linear regression fitting of both adsorption isotherm and kinetic models revealed lower values of error and lower AICc values than the linear regression models. The pseudo-second-order (non-linear) fit was the best fit among kinetic models with the lowest AICc values, at 57.5 (nZVI-Bare) and 71.9 (nZVI-Bento), while the Freundlich equation was the best fit among the isotherm models, showing the lowest AICc values, at 105.5 (nZVI-Bare) and 105.1 (nZVI-Bento). The adsorption maxima ( q max ) predicted by the non-linear Langmuir adsorption isotherm were 354.3 and 198.5 mg g −1 for nZVI-Bare and nZVI-Bento, respectively. The nZVI-Bento successfully reduced As in water (initial As concentration = 5 mg L −1

    adsorbent dose = 0.5 g L −1 ) to below permissible limits for drinking water (10 µg L −1 ). The nZVI-Bento @ 1% ( w / w ) could stabilize As in soils by increasing the amorphous Fe bound fraction and significantly diminish the non-specific and specifically bound fraction of As in soil. Considering the enhanced stability of the novel nZVI-Bento (upto 60 days) as compared to the unmodified product, it is envisaged that the synthesized product could be effectively used for removing As from water to make it safe for human consumption.
    Keywords adsorption isotherm ; ageing ; drinking water ; error analysis ; kinetics ; super-adsorbent ; Organic chemistry ; QD241-441
    Subject code 333
    Language English
    Publishing date 2023-02-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Rapid prediction of soil available sulphur using visible near-infrared reflectance spectroscopy

    BHABANI PRASAD MONDAL / RABI NARAYAN SAHOO / NAYAN AHMED / RAJIV KUMAR SINGH / BAPPA DAS / NILIMESH MRIDHA / SHALINI GAKHAR

    The Indian Journal of Agricultural Sciences, Vol 91, Iss

    2021  Volume 9

    Abstract: 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- ... ...

    Abstract 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 develop spectral models for S prediction from spectral dataset. Several statistical diagnostics like coefficient of determination (R2), root mean square error (RMSE), ratio of performance deviation (RPD) and ratio of performance to interquartile distance (RPIQ) were used to evaluate the performances of two models. The best prediction of S was achieved from nonlinear RF model (R2 = 0.71, RMSE = 8.86, RPD =1.18, RPIQ = 1.69) as compared to linear PLSR model (R2 = 0.53, RMSE = 9.04, RPD = 1.16, RPIQ = 1.66) datasets. Therefore, the result suggested applying non-linear multivariate model (RF) for obtaining best predictability for S from spectroscopic technique.
    Keywords Available sulphur ; Multivariate models ; PLSR ; Reflectance spectroscopy ; RF ; Agriculture ; S
    Language English
    Publishing date 2021-09-01T00:00:00Z
    Publisher Indian Council of Agricultural Research
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Inversion of radiative transfer model for retrieval of wheat biophysical parameters from broadband reflectance measurements

    Vinay Kumar Sehgal / Debasish Chakraborty / Rabi Narayan Sahoo

    Information Processing in Agriculture, Vol 3, Iss 2, Pp 107-

    2016  Volume 118

    Abstract: 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) ... ...

    Abstract 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.
    Keywords PROSAIL ; Look up table ; Neural network ; Leaf area index ; Chlorophyll content ; Target diagram ; IRS LISS-3 ; Agriculture (General) ; S1-972 ; Information technology ; T58.5-58.64
    Language English
    Publishing date 2016-06-01T00:00:00Z
    Publisher KeAi Communications Co., Ltd.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article: Inversion of radiative transfer model for retrieval of wheat biophysical parameters from broadband reflectance measurements

    Sehgal, Vinay Kumar / Debasish Chakraborty / Rabi Narayan Sahoo

    China Agricultural University Information processing in agriculture. 2016 June, v. 3, no. 2

    2016  

    Abstract: 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) ... ...

    Abstract 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.
    Keywords canopy ; chlorophyll ; image analysis ; leaf area index ; leaf wetness ; leaves ; neural networks ; protocols ; radiative transfer ; reflectance ; remote sensing ; scanners ; spectroradiometers ; wheat
    Language English
    Dates of publication 2016-06
    Size p. 107-118.
    Publishing place Elsevier B.V.
    Document type Article
    ISSN 2214-3173
    DOI 10.1016/j.inpa.2016.04.001
    Database NAL-Catalogue (AGRICOLA)

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  6. Article: Trends and Change-Point in Satellite Derived Phenology Parameters in Major Wheat Growing Regions of North India During the Last Three Decades

    Chakraborty, Debasish / Vinay Kumar Sehgal / Rajkumar Dhakar / Deb Kumar Das / Rabi Narayan Sahoo

    Journal of the Indian Society of Remote Sensing. 2018 Jan., v. 46, no. 1

    2018  

    Abstract: 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 ... ...

    Abstract 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.
    Keywords agroecosystems ; climate change ; data collection ; early development ; farmers ; food security ; growing season ; heat stress ; phenology ; remote sensing ; satellites ; sowing ; vegetation ; wheat ; India
    Language English
    Dates of publication 2018-01
    Size p. 59-68.
    Publishing place Springer India
    Document type Article
    ZDB-ID 2439566-3
    ISSN 0974-3006 ; 0255-660X
    ISSN (online) 0974-3006
    ISSN 0255-660X
    DOI 10.1007/s12524-017-0684-8
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  7. Article: Relationship of Hyperspectral Reflectance Indices with Leaf N and P Concentration, Dry Matter Accumulation and Grain Yield of Wheat

    Hussain, Ashaq / Rabi Narayan Sahoo / Dinesh Kumar / Sanatan Pradhan

    Journal of the Indian Society of Remote Sensing. 2017 Oct., v. 45, no. 5

    2017  

    Abstract: 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 ... ...

    Abstract 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 R² 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, ND₇₀₅, PRI, PSNDc and REIP had higher R² (>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 ND₇₀₅ had almost a similar degree of relationship with DM accumulation with R² 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 R² of 0.85, 0.81 and 0.95 (P = ≤0.01), 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 R² values.
    Keywords dry matter accumulation ; field experimentation ; grain yield ; leaves ; nitrogen ; nitrogen content ; nutrient deficiencies ; phosphorus ; potassium ; reflectance ; remote sensing ; vegetation index ; wheat ; zinc ; India
    Language English
    Dates of publication 2017-10
    Size p. 773-784.
    Publishing place Springer India
    Document type Article
    ZDB-ID 2439566-3
    ISSN 0974-3006 ; 0255-660X
    ISSN (online) 0974-3006
    ISSN 0255-660X
    DOI 10.1007/s12524-016-0633-y
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  8. Article ; Online: SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging

    Tanuj Misra / Alka Arora / Sudeep Marwaha / Viswanathan Chinnusamy / Atmakuri Ramakrishna Rao / Rajni Jain / Rabi Narayan Sahoo / Mrinmoy Ray / Sudhir Kumar / Dhandapani Raju / Ranjeet Ranjan Jha / Aditya Nigam / Swati Goel

    Plant Methods, Vol 16, Iss 1, Pp 1-

    2020  Volume 20

    Abstract: Abstract Background High throughput non-destructive phenotyping is emerging as a significant approach for phenotyping germplasm and breeding populations for the identification of superior donors, elite lines, and QTLs. Detection and counting of spikes, ... ...

    Abstract Abstract Background High throughput non-destructive phenotyping is emerging as a significant approach for phenotyping germplasm and breeding populations for the identification of superior donors, elite lines, and QTLs. Detection and counting of spikes, the grain bearing organs of wheat, is critical for phenomics of a large set of germplasm and breeding lines in controlled and field conditions. It is also required for precision agriculture where the application of nitrogen, water, and other inputs at this critical stage is necessary. Further, counting of spikes is an important measure to determine yield. Digital image analysis and machine learning techniques play an essential role in non-destructive plant phenotyping analysis. Results In this study, an approach based on computer vision, particularly object detection, to recognize and count the number of spikes of the wheat plant from the digital images is proposed. For spike identification, a novel deep-learning network, SpikeSegNet, has been developed by combining two proposed feature networks: Local Patch extraction Network (LPNet) and Global Mask refinement Network (GMRNet). In LPNet, the contextual and spatial features are learned at the local patch level. The output of LPNet is a segmented mask image, which is further refined at the global level using GMRNet. Visual (RGB) images of 200 wheat plants were captured using LemnaTec imaging system installed at Nanaji Deshmukh Plant Phenomics Centre, ICAR-IARI, New Delhi. The precision, accuracy, and robustness (F1 score) of the proposed approach for spike segmentation are found to be 99.93%, 99.91%, and 99.91%, respectively. For counting the number of spikes, “analyse particles”—function of imageJ was applied on the output image of the proposed SpikeSegNet model. For spike counting, the average precision, accuracy, and robustness are 99%, 95%, and 97%, respectively. SpikeSegNet approach is tested for robustness with illuminated image dataset, and no significant difference is observed in the segmentation ...
    Keywords Deep learning ; Encoder-decoder deep network ; Image analysis ; Non-destructive plant phenotyping ; Wheat spikes identification and count ; Plant culture ; SB1-1110 ; Biology (General) ; QH301-705.5
    Subject code 006
    Language English
    Publishing date 2020-03-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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