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Ramesh, K. V.

MP-TRACS Crops

A Weather-Based Forecast Model for Capsule Rot of Small Cardamom

Abstract Views :260 | PDF Views:78

Authors

Prashant Goswami ¹, Renu Goyal ¹, E. V. S. Prakasa Rao ¹, K. V. Ramesh ¹, M. R. Sudarshan ², D. Ajay ²

Affiliations
1 CSIR Centre for Mathematical Modelling and Computer Simulation, Wind Tunnel Road, Bangalore 560 037, IN
2 Indian Cardamom Research Institute, Kailasanadu (P.O), Myladumpara 685 553, IN

Source

Current Science, Vol 107, No 6 (2014), Pagination: 1013-1019

Abstract

Small cardamom is an economically important spice crop. However, cardamom is susceptible to several diseases that significantly reduce yield. Proactive prevention of these diseases based on advance warning can enhance the efficiency of disease control and reduce environmental load of pesticides. Many of these diseases are governed by weather variables (for example, through control of fungal growth). This work presents a disease (capsule rot of cardamom) forecast model based on a set of meteorological variables.While no single weather variable provides successful simulation, an optimal combination of weather variables provides sufficient skill for advance warning of the disease.

Keywords

Capsule Rot Disease, forecasting, Meteorological Variables, Small Cardamom.

Full Text

Computational Studies of Mycorrhizal Protein: GiHsp60 and Its Interaction With Soil Organic Matter

Abstract Views :223 | PDF Views:77

Authors

Dipti Mothay ¹, K. V. Ramesh ¹

Affiliations
1 Department of Biotechnology, Jain (deemed to be University), School of Sciences, Jayanagar 3rd Block, Bengaluru 560 011, IN

Source

Current Science, Vol 120, No 2 (2021), Pagination: 389-397

Abstract

This study uses homology modelling and molecular docking approaches to explore the binding mechanism of glomalin-related soil protein from Rhizophagus irregularis (GiHsp60) with soil organic matter (SOM) and the role played by soil protein in the sequestration of common soil pollutants. Conserved domain analysis reveals that GiHsp60 belongs to chaperonin-like super-family having binding sites for ATP/Mg²⁺. Three-dimensional model of GiHsp60 was reasonably good based on reports generated by different validation servers. Docking results suggest that Van der Waals force is primarily responsible for the interaction between GiHsp60 and SOM. The study also reveals the role played by GiHsp60 in the sequestration of dif-ferent soil pollutants.

Keywords

Docking Studies, Homology Modelling, Heat Shock Protein, Mycorrhizal Fungi, Soil Pollutants.

Full Text

References

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Crop production estimation using deep learning technique

Abstract Views :237 | PDF Views:78

Authors

Ashapurna Marndi ¹, K. V. Ramesh ¹, G. K. Patra ¹

Affiliations
1 CSIR-Fourth Paradigm Institute, Bengaluru 560 037, India and Academy of Scientific and Innovative Research, Ghaziabad 201 002, India, IN

Source

Current Science, Vol 121, No 8 (2021), Pagination: 1073-1079

Abstract

Reliable estimation of crop requirement and production in advance, help policy makers to adopt timely decision for trade as export–import, which is a basic building block to assure food security of a country. A powerful and robust algorithm is essential to predict the future demand and production of a particular crop for subsequent years. Deep learning methods are used successfully in solving different prediction problems of various applications. This study attempts to design an efficient AI based technique specifically using long short-term memory, a deep learning approach for estimation of crop production using crop production information of neighbouring countries, which are part of the South Asian monsoon system. Detailed sensitivity analysis is conducted to identify the optimal combination of crop production of neighbouring countries that directly and indirectly impact the crop production of India. Here, we designed and developed a predictive model for rice production of India with lead time of one year using deep learning technique. Along with that, as there are significant influences of local climate (i.e. rainfall data) on crop production, that information was also considered along with crop production of neighbouring countries. The results indicated that local and regional scale parameters jointly improve the prediction capability for future years. Capability of the proposed model was validated with export–import data on crop of India and neighbouring countries, and the validation result showed that our proposed technique was efficient and robust in nature

Keywords

Artificial intelligence, crop production model, deep neural networks, long short-term memory, sensitivity analysis.

Full Text

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