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Ray, Mrinmoy

Modelling and forecasting cotton production using tuned-support vector regression

Abstract Views :558 | PDF Views:221

Authors

Amit Saha ¹, K. N. Singh ², Mrinmoy Ray ², Santosha Rathod ³, Sharani Choudhury ⁴

Affiliations
1 Central Sericultural Research and Training Institute, Central Silk Board, Srirampura, Mysuru 570 008, India, IN
2 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India, IN
3 ICAR-Indian Institute of Rice Research, Hyderabad 500 030, India, IN
4 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India, IN

Source

Current Science, Vol 121, No 8 (2021), Pagination: 1090-1098

Abstract

India is the largest producer of cotton in the world. For proper planning and designing of policies related to cotton, robust forecast of future production is utmost necessary. In this study, an effort has been made to model and forecast the cotton production of India using tuned-support vector regression (Tuned-SVR) model, and the importance of tuning has also been pointed out through this study. The Tuned-SVR performed better in both modelling and forecasting of cotton production compared to auto regressive integrated moving average and classical SVR models

Keywords

ARIMA, cotton production forecasting, SVR, time series, tuned-SVR.

Full Text

References

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Niu, D., Wang, Y. and Wu, D. D., Power load forecasting using support vector machine and ant colony optimization. Exp. Syst. Appl., 2010, 37, 2531–2539.

Saha, A., Singh, K. N., Ray, M. and Rathod, S., A hybrid spatiotemporal modelling: an application to space-time rainfall forecasting. Theor. Appl. Climatol., 2020, 142, 1271–1282.

Saha, A. and Bhattacharyya, S., Artificial insemination for milk production in India: a statistical insight. Indian J. Anim. Sci., 2021, 90, 1186–1190.

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Ortiz-Garcia, E. G., Salcedo-Sanz, S. and Casanova-Mateom, C., Accurate precipitation prediction with support vector classifiers: a study including novel predictive variables and observational data. Atmos. Res., 2014, 139, 128–136.

Kumar, T. L. M. and Prajneshu, Development of hybrid models for forecasting time-series data using nonlinear SVR enhanced by PSO. J. Stat. Theory Prac., 2015, 9(4), 699–711.

Rathod, S., Singh, K. N., Patil, S. G., Naik, R. H., Ray, M. and Meena, V. S., Modeling and forecasting of oilseed production of India through artificial intelligence techniques. Indian J. Agric. Sci., 2018, 88(1), 22–27.

De Giorgi, M. G., Campilongo, S., Ficarella, A. and Congedo, P. M., Comparison between wind power rediction models based on wavelet decomposition with least-squares support vector machine (LS-SVM) and artificial neural network (ANN). Energy, 2014, 7, 5251–5272.

Balasundaram, S. and Gupta, D., Lagrangian support vector regression via unconstrained convex minimization. Neural Networks, 2014, 51, 67–79.

Balasundaram, S. and Gupta, D., On implicit Lagrangian twin support vector regression by Newton method. Int. J. Comput. Intel. Syst., 2014, 7(1), 50–64.

Balasundaram, S. and Gupta, D., Training Lagrangian twin support vector regression via unconstrained convex minimization. Knowl.-Based Syst., 2014, 59, 85–96.

Balasundaram, S. and Gupta, D., On optimization based extreme learning machine in primal for regression and classification by functional iterative method. Int. J. Mach. Learn. Cybernet., 2016, 7(5), 707–728.

Gupta, D., Richhariya, B. and Borah, P., A fuzzy twin support vector machine based on information entropy for class imbalance learning. Neural. Comput. Appl., 2019, 31(11), 7153–7164.

Gupta, U. and Gupta, D., An improved regularization based Lagrangian asymmetric ν-twin support vector regression using pinball loss function. Appl. Intell., 2019, 49(10), 3606–3627.

Hou, Q., Zhang, J., Liu, L., Wang, Y. and Jing, L., Discriminative information-based nonparallel support vector machine. Signal. Process., 2019, 162, 169–179.

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Pattern of crop diversification and its implications on undernutrition in India

Abstract Views :467 | PDF Views:230

Authors

A. R. Anuja ¹, G. P. Shivaswamy ¹, Mrinmoy Ray ¹, K. N. Singh ¹

Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN

Source

Current Science, Vol 122, No 10 (2022), Pagination: 1154-1160

Abstract

The present study explores the pattern and extent of food-crop diversification and its implications on nutritional indicators in India using district-level data for the most recent period. It relied on data from land-use statistics and the National Family Health Survey 2015–16. We estimated the Simpson index for food-crop diversification and undernutrition index for nutritional status. The association of crop diversification and nutritional status was analysed employing bivariate copula function. The findings show striking regional differences in the extent of food-crop diversification and nutritional outcomes. The results of the copula function indicate a significant inverse relationship between crop diversification and undernutrition

Keywords

Bivariate copula, crop diversification, land use, nutritional status, undernutrition index.

Full Text

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Genetic Algorithms-Based Fuzzy Analytical Hierarchical Process (GA-FAHP) for Evaluating Biofortified Crop Promotion Strategies

Abstract Views :347 | PDF Views:189

Authors

K. N. Singh ¹, Mrinmoy Ray ¹, Satyapriya ², Shashi Dahiya ¹, Jaya Pandey ³, Rajeev R. Kumar ¹

Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
3 Indian Council of Medical Research, New Delhi 110 029, IN

Source

Current Science, Vol 125, No 3 (2023), Pagination: 317-320

Abstract

In developing nations such as India, malnutrition is a major nutritional and health challenge. Biofortification has the potential to be an effective instrument in India’s attempts to combat malnutrition. Expert opinion must be used to evaluate the factors related to the promotion, distribution and adoption of biofortified crops. The analytical hierarchy process (AHP) is one of the most often employed decision-making methods. However, conventional AHP is incapable of identifying ambiguity in human judgements. Fuzzy AHP has already been devised to overcome this limitation. Fuzzy AHP necessitates information in pairwise comparisons, which is not always easy to gather. In this context, the Fuzzy AHP technique based on the genetic algorithm has been proposed, which can compute the priority weight without using a pairwise comparison matrix by directly dealing with expert-provided data. The proposed approach has been illustrated using the opinions of 1600 farmers from Odisha, India.

Keywords

Biofortified Crops, Fuzzy AHP, Genetic Algorithm, Malnutrition.

Full Text

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High-resolution reconstruction of images for estimation of plant height in wheat using RGB-D camera and machine learning approaches

Abstract Views :251 |

Authors

Preety Dagar ¹, Alka Arora ¹, Mrinmoy Ray ¹, Sudhir Kumar ², Himanshushekhar Chourasia ³, Mohit Kumar ¹, Sudeep Marwaha ¹, Rajni Jain ¹, Viswanathan Chinnusamy ²

Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
3 ICAR-Central Institute for Research on Cotton, Mumbai 400 019, IN

Source

Current Science, Vol 127, No 12 (2024), Pagination: 1440-1446

Abstract

In this study, a pipeline has been proposed where colour image and depth information of wheat plants are captured using an red green blue-depth (RGB-D) camera; later these two are combined to create a three-dimensional point cloud of the plant. The point clouds were processed to calculate the plant height. The results were then statistically analysed with the help of machine learning algorithms, viz. linear regression, support vector machine and artificial neural network (ANN). The comparison of the results shows that ANN performed better than the other two models with mean squared error 189.94, root mean squared error 13.70, mean absolute error 11.40 and mean absolute percentage error 18.73. The proposed study shows a high-precision and low-cost technology that can be widely used for non-destructive measurement of phenotyping parameters for wheat and other crops.

Keywords

3D reconstruction, image processing, Open3D, plant phenotyping, RGB-D imaging.

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