Das B - Search Results

1

Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models.

Mandal N, Adak S, Das DK, Sahoo RN, Mukherjee J, Kumar A, Chinnusamy V, Das B, Mukhopadhyay A, Rajashekara H, Gakhar S. Mandal N, et al. Among authors: das dk, das b. Front Plant Sci. 2023 Feb 23;14:1067189. doi: 10.3389/fpls.2023.1067189. eCollection 2023. Front Plant Sci. 2023. PMID: 36909416 Free PMC article.

2

Polycythemia in renal transplant recipients.

Sahoo RN, Das BK, Mohapatra MK, Das GC. Sahoo RN, et al. Among authors: das bk, das gc. Transplant Proc. 1992 Oct;24(5):1772. Transplant Proc. 1992. PMID: 1412836 No abstract available.

3

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics.

Das B, Sahoo RN, Pargal S, Krishna G, Verma R, Chinnusamy V, Sehgal VK, Gupta VK, Dash SK, Swain P. Das B, et al. Spectrochim Acta A Mol Biomol Spectrosc. 2018 Mar 5;192:41-51. doi: 10.1016/j.saa.2017.10.076. Epub 2017 Oct 31. Spectrochim Acta A Mol Biomol Spectrosc. 2018. PMID: 29126007

4

Spectroscopy based novel spectral indices, PCA- and PLSR-coupled machine learning models for salinity stress phenotyping of rice.

Das B, Manohara KK, Mahajan GR, Sahoo RN. Das B, et al. Spectrochim Acta A Mol Biomol Spectrosc. 2020 Mar 15;229:117983. doi: 10.1016/j.saa.2019.117983. Epub 2019 Dec 23. Spectrochim Acta A Mol Biomol Spectrosc. 2020. PMID: 31896051

5

Evaluation of different water absorption bands, indices and multivariate models for water-deficit stress monitoring in rice using visible-near infrared spectroscopy.

Das B, Sahoo RN, Pargal S, Krishna G, Verma R, Viswanathan C, Sehgal VK, Gupta VK. Das B, et al. Spectrochim Acta A Mol Biomol Spectrosc. 2021 Feb 15;247:119104. doi: 10.1016/j.saa.2020.119104. Epub 2020 Oct 24. Spectrochim Acta A Mol Biomol Spectrosc. 2021. PMID: 33161273

6

Vis-NIR spectroscopy based rapid and non-destructive method to quantitate microplastics: An emerging contaminant in farm soil.

Saha ND, Kumari P, Das B, Sahoo RN, Kumar R, Golui D, Singh B, Jain N, Bhatia A, Chaudhary A, Chakrabarti B, Bhowmik A, Saha P, Islam S. Saha ND, et al. Among authors: das b. Sci Total Environ. 2024 Jun 1;927:172088. doi: 10.1016/j.scitotenv.2024.172088. Epub 2024 Mar 29. Sci Total Environ. 2024. PMID: 38554975

7

Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India.

Das B, Nair B, Reddy VK, Venkatesh P. Das B, et al. Int J Biometeorol. 2018 Oct;62(10):1809-1822. doi: 10.1007/s00484-018-1583-6. Epub 2018 Jul 24. Int J Biometeorol. 2018. PMID: 30043218

8

Combining biophysical parameters with thermal and RGB indices using machine learning models for predicting yield in yellow rust affected wheat crop.

Singh RN, Krishnan P, Singh VK, Sah S, Das B. Singh RN, et al. Among authors: das b. Sci Rep. 2023 Nov 1;13(1):18814. doi: 10.1038/s41598-023-45682-3. Sci Rep. 2023. PMID: 37914800 Free PMC article.

9

Comparative evaluation of linear and nonlinear weather-based models for coconut yield prediction in the west coast of India.

Das B, Nair B, Arunachalam V, Reddy KV, Venkatesh P, Chakraborty D, Desai S. Das B, et al. Int J Biometeorol. 2020 Jul;64(7):1111-1123. doi: 10.1007/s00484-020-01884-2. Epub 2020 Mar 9. Int J Biometeorol. 2020. PMID: 32152727

10

Novel combination artificial neural network models could not outperform individual models for weather-based cashew yield prediction.

Das B, Murgaonkar D, Navyashree S, Kumar P. Das B, et al. Int J Biometeorol. 2022 Aug;66(8):1627-1638. doi: 10.1007/s00484-022-02306-1. Epub 2022 May 31. Int J Biometeorol. 2022. PMID: 35641796

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