Author Details

Scroll

Refine your search

Collections

Basic & Applied Science Collection

Co-Authors

Journals

Current Science

Year

Authors

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All

Geethalakshmi, V.

Evaluation of Weather-Based Crop Insurance Products for Kharif Groundnut

Abstract Views :249 | PDF Views:89

Authors

S. Kokilavani ¹, V. Geethalakshmi ¹, K. Bhuvaneswari ¹, A. Lakshmanan ¹

Affiliations
1 Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore 641 003, IN

Source

Current Science, Vol 107, No 11 (2014), Pagination: 1866-1871

Abstract

Weather-based crop insurance scheme (WBCIS) products proposed by four insurance providers was compared and evaluated using historical weather data for piloting WBCIS on kharif groundnut in Coimbatore, Dharmapuri, Theni, Tirunelveli and Virudhunagar districts of Tamil Nadu. Water deficits during the vegetative phases of groundnut crop generally delay flowering and maturity thereby reducing the crop growth and yield. The study revealed that the deficit rainfall risk was more pronounced in all the abovementioned districts, whereas the risk of excess rainfall impact could be clearly observed in Theni district. Though the occurrence of strike events was for phase- I of deficit rainfall cover, the rate per mm of rainfall fixed by IFFCO-TOKYO was quite low. The product designed for HDFC-ERGO and MS-Cholamandalam was similar, whereas the product for AIC and IFFCO-TOKYO was designed with little variation in context to excess rainfall cover and consecutive dry days. The compensation benefit realized by the farmers of Virudhunagar and Dharmapuri districts was higher followed by Theni because the compensation rate per mm of rainfall fixed by the company was higher, which favours the farmers.

Keywords

Insurance Companies, Payout, Product Design, Strike Events.

Full Text

Effect of Elevated Temperature on Life-History Parameters of Rice Yellow Stem Borer (Scirpophaga incertulas Walker)

Abstract Views :283 | PDF Views:75

Authors

Narayanasamy Manikandan ¹, J. S. Kennedy ², V. Geethalakshmi ¹

Affiliations
1 Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore 641 003, IN
2 Department of Agricultural Entomology, Tamil Nadu Agricultural University, Coimbatore 641 003, IN

Source

Current Science, Vol 110, No 5 (2016), Pagination: 851-857

Abstract

A study was undertaken to understand the effect of increasing temperature on population dynamics of yellow stem borer (YSB), Scirpophaga incertulas. Experiments were carried out in a temperature control chamber with five different constant temperatures (28°C, 30°C, 32°C, 34°C and 36°C). The data on agespecific life table at varying temperature regimes revealed that the total lifespan of YSB extended to a maximum of 52 days at 28°C followed by 49 days at 30°C and 46 days at 32°C. In general, survival of the YSB decreased with increasing temperature. Preoviposition period for YSB also decreased with increasing temperature. However, the total number of eggs laid by YSB increased with increasing temperature. Also, 50% fecundity in YSB was recorded on 49.7 days after incubation at 28°C, whereas it was observed as early as 34.4 days at 36°C. All the growth parameters were observed to decrease at 36°C, which reveals that temperature increase above 34°C is detrimental to the development of YSB. The above results reveal that, if the global warming continues at the present phase, it will negatively influence YSB and the population growth will be severely affected in the near future.

Keywords

Global Warming, Life and Fecundity Tables, Population Dynamics, Temperature Regime, Yellow Stem Borer.

Full Text

Assessment of Climate Change Impact on Rice Using Controlled Environment Chamber in Tamil Nadu, India

Abstract Views :264 | PDF Views:98

Authors

V. Geethalakshmi ¹, K. Bhuvaneswari ¹, A. Lakshmanan ¹, Nagothu Udaya Sekhar ²

Affiliations
1 Tamil Nadu Agriculture University, Coimbatore 641 003, IN
2 Bioforsk, Norwegian Institute for Agricultural and Environmental Research, Fr. A. Dahlsvei 20, NO-1430 Ås, NO

Source

Current Science, Vol 112, No 10 (2017), Pagination: 2066-2072

Abstract

Impacts of elevated temperature and carbon dioxide (CO₂) enrichment on rice were assessed by carrying out an experiment with four dates of planting (1 June and 15 June, 1 and 15 July) during 2014 under two different environmental conditions, viz. ambient and modified (climate control chamber) with +4°C compared to the ambient temperature and CO₂ enrichment of 650 ppm. Crops grown under modified environment recorded reduced growth characters (leaf area index, dry matter production, number of tillers m^-2), lesser dry matter partitioning towards grain, yield attributes (number of productive tillers m^-2, number of filled grains panicle^-1) and lower grain yields compared to those grown under ambient condition. Crops subjected to elevated temperature and enriched CO₂ attained panicle initiation, flowering and maturity earlier than those under open ambient condition.

Keywords

Ambient and Modified Environment, Climate Change Impact, Elevated Temperature, Enriched Carbon Dioxide, Rice.

Full Text

References

IPCC, Summary for Policymakers, In Climate Change: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (eds Solomon, S. et al.), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2007.

International Rice Research Institute (IRRI). Rice Almanac, Manila, Philippines: International Rice Research Institute, 1997, 2nd edn.

Peng, S., Ingram, K. T., Neue, H. U. and Ziska, L. H. (eds), Climate Change and Rice. Los Banos, Philippines, 1996, pp. 3–8.

Alam, M. et al., Impacts, Vulnerability and Adaptation to Climate Change in Asia: Background Paper for Meeting on Adaptation, Beijing, China, United Nations Framework Convention on Climate Change (UNFCCC), 11–13 April 2007.

Ferrero, A. and Nguyen, V. N., The sustainable development of rice-based production systems in Europe. IRC Newsl., 2004, 54, 115–124.

Matsushima, S. and Tsunoda, K., Analysis of developmental factors determining yield and application of yield prediction and culture improvement of lowland rice XLV. Effects of temperature and its daily range in different growth stages upon the growth, grain yield, and constitutional factors in rice plants. Proc. Crop Sci. Soc. Jpn, 1958, 26, 243–244.

Mohandass, S., Kareem, A. A., Ranganathan, T. B. and Jeyaraman, S., Rice production in India under the current and future climate. In Modeling the Impact of Climate Change on Rice Production in Asia (eds Mathews, R. B. et al.), United Kingdom, CAB International, 1995, pp. 165–181.

Ainsworth, E. A., Rice production in a changing climate: a metaanalysis of responses to elevated carbon dioxide and elevated ozone concentration. Global Change Biol., 2008, 14, 1642–1650.

Kim, H. Y., Lim, S. S., Kwak, J. H., Lee, D. S., Lee, S. M., Ro, H. M. and Choi, W. J., Dry matter and nitrogen accumulation and partitioning in rice (Oryza sativa L.) exposed to experimental warming with elevated CO2. Plant Soil, 2011, 342(1–2), 59–71.

Core writing team, Pachauri, R. K. and Reisinger, A., Contribution of working groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change IPCC, Switzerland, 2007, p. 104.

Tripathy, Rojalin, K., Chaudhari, N. and Patel, N. K., Evaluation of different methods to estimate incoming solar radiation. J. Agrometeorol., 2008, 10 (Special Issue, Part 1), p. 174.

Geethalakshmi, V., Kokilavani, S., Nagarajan, R., Babu, C. and Poornima, S., Impacts of climate change on rice and ascertaining adaptation opportunities for Tamil Nadu state. In The Proceedings of the International Symposium on Agro Meteorology and Food Security Conducted by CRIDA, Hyderabad, 2008, pp. 21–22.

Houghton, J. T., MeiraFilho, L. G., Callander, B. A., Harris, N., Kattenberg, A. and Maskell, K. (eds). Climate Change. Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge, 1995.

Keeling, C. D., Whorf, T. P., Wehlen, M. and Van der Pliht, J., Inter annual extremes in the rate of rise in atmospheric carbon dioxide since. Nature, 1995, 375, 666–670.

Morison, J. I. L. and Lawlor, D. W., Interactions between increasing CO2 concentration and temperature on plant growth. Plant Cell Environ., 1999, 22, 659–682.

Prinn, R. G., Integrated global system model for climate policy assessment feedback and sensitivity analysis. Clim. Change, 1998.

Rupa Kumar, K. et al., High-resolution climate change scenarios for India for the 21st century. Curr. Sci., 2006, 90, 334–345.

Palanisamy, K. M. and Gomez, K. A., Length and width method for estimating leaf area of rice. Agron. J., 1974, 66, 430–433.

Gomez, K. A. and Gomez, A. A., Statistical Procedure for Agricultural Research, John-Wiley and Sons Inc., New York, 1984, p. 680.

Mandal, B. K., Sainik, T. R. and Ray, P. K., Effect of age of seedlings and levels of nitrogen on the productivity of rice. Oryza, 1984, 21(2), 232–252.

Kim, H. R. and Young, H. Y., CO₂ concentration and temperature on growth, yield and physiological responses of rice. Adv. Biol. Res., 2010, 1(2), 48.

Horie, T., Crop ontogeny and development. In Physiology and Determination of Crop Yield (eds Boot, K. J. et al.), American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, Wisconsin, 1994, pp. 153–1804.

Rainfall and Temperature Projections and their Impact Assessment Using CMIP5 Models under Different RCP Scenarios for The Eastern Coastal Region of India

Abstract Views :309 | PDF Views:91

Authors

S. Vijayakumar ¹, A. K. Nayak ¹, A. P. Ramaraj ², C. K. Swain ¹, V. Geethalakshmi ³, S. Pazhanivelan ³, Rahul Tripathi ¹, N. S. Sudarmanian ³

Affiliations
1 ICAR-National Rice Research Institute, Cuttack 753 006, IN
2 International Crops Research Institute for the Semi-Arid Tropics, Hyderabad 502 324, IN
3 Tamil Nadu Agricultural University, Coimbatore 641 003, IN

Source

Current Science, Vol 121, No 2 (2021), Pagination: 222-232

Abstract

Trend analysis of annual rainfall over the coastal districts of Odisha, India showed statistically nonsignificant increasing trend in all districts, except Ganjam. Whereas the maximum and minimum temperature showed significant increasing trend. Warming in these districts is mainly due to increasing minimum temperature during summer and rainy season, and maximum temperature during winter. Future climate projection results revealed, the annual mean rainfall is expected to change by 0.1–2.2%, –0.3–0.7% and 1.5–3.2% (RCP 4.5), and 3.6–7.9%, 3.7–6.6% and 8.5–14% (RCP 8.5) during the near (2011–39), mid (2040–69) and late (2070–99) centuries respectively. Anticipate climate change will have a marginal impact on total rainfall, and a major impact on its distribution. The annual mean minimum temperature is expected to increase by 0.60–0.73°C, 0.71–0.88°C, 1.20–1.42°C (RCP 4.5), and 1.77–2.14°C, 1.56–1.68°C, 3.06–3.73°C (RCP 8.5) during near, mid and late centuries respectively. Similarly, the annual mean maximum temperature is expected to increase by 0.61–0.66°C, 0.68–0.72°C and 1.35–1.55°C (RCP 4.5), and 1.79–1.97°C, 1.73–2.01°C and 3.08–3.44°C (RCP 8.5) during near, mid and late centuries respectively. Season-wise projection revealed that the change in rainfall and temperature is expected to be more in winter and summer under both the RCP scenarios. The projected future climate change will have both positive and negative impacts on agriculture. The negative impacts are expected to be more pronounced during kharif in comparison to rabi.

Keywords

Climate Projection, Coastal Districts, Rainfall, Temperature, Trend Analysis.

Full Text

References

GOI, Agricultural statistics at a glance, Government of India Ministry of Agriculture and Farmers Welfare Department of Agriculture, Cooperation and Farmers Welfare, Directorate of Economics and Statistics. Government of India, New Delhi, 2009.

GOI, Report on status of ground water quality in coastal aquifers of India, Government of India, Ministry of water resources, Central Ground Water Board, Faridabad, 2014, pp. 1–130.

Patnaik, U., Das, P. K. and Bahinipati, C. S., Analyzing vulnerability to climatic variability and extremes in the coastal districts of Odisha, India. Rev. Dev. Change, 2013, 18, 173–189.

IPCC, Change, IPOC, Climate change 2007: The physical science basis: Summary for policymakers. Geneva, IPCC, 2007.

Dupuis, I. and Dumas, C., Influence of temperature stress on in vitro fertilization and heat shock protein synthesis in maize (Zea mays L.) reproductive tissues. Plant Physiol., 1990, 94(2), 665–670.

Kim, H. Y., Horie, T., Nakagawa, H. and Wada, K., Effects of elevated CO2 concentration and high temperature on growth and yield of rice: II. The effect on yield and its components of Akihikari rice. Jpn. J. Crop Sci., 1996, 65(4), 644–651.

Barlow, K. M., Christy, B. P., O’leary, G. J., Riffkin, P. A. and Nuttall, J. G., Simulating the impact of extreme heat and frost events on wheat crop production: a review. Field Crops Res., 2015, 171, 109–119.

Venkateswarlu, B. and Prasad, J. V. N. S., Carrying capacity of Indian Agriculture: issues related to rainfed farming. Curr. Sci., 2012, 102(6), 882–888.

Mishra, P. K., Socio-economic impacts of climate change in Odisha: issues, challenges and policy options. Am. J. Clim. Change, 2017, 3(1), 93–107.

Jena, P. P., Climate change and its worst effect on coastal Odisha – an overview of its impact in Jagatsinghpur district. IOSR J. Humanities Soc. Sci., 2018, 23, 1–15.

Singh, B. R. and Singh, O., Study of impacts of global warming on climate change: rise in sea level and disaster frequency. Global warming – impacts and future perspective, 2012.

Taylor, K. E., Stouffer, R. J. and Meehl, G. A., An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc., 2012, 93, 485–498.

Meehl, G. A. et al., Decadal prediction: can it be skillful? Bull. Am. Meteorol. Soc., 2009, 90, 1467–1486.

Taylor, K., Stouffer, R. and Meehl, G., A summary of the CMIP5 experiment design, Program for Climate Model Diagnosis and Intercomparison (PCMDI), 2011.

Ramaraj, A., Geethalakshmi, V. and Bhuvaneswari, K., Understanding the uncertainty cascaded in climate change projections for agricultural decision making. Mausam, 2017, 68, 223–234.

Jena, P., Azad, S. and Rajeevan, M. N., CMIP5 projected changes in the annual cycle of Indian monsoon rainfall. Climate, 2016, 4, 14.

Moss, R. H. et al., The next generation of scenarios for climate change research and assessment. Nature, 2010, 463, 747–756.

Mishra, S. K., Sahany, S., Salunke, P., Kang, I. S. and Jain, S., Fidelity of CMIP5 multi-model mean in assessing Indian monsoon simulations. Npj Climate Atmos. Sci., 2018, 1(1), 1–8.

Rosenzweig, C. et al., The agricultural model intercomparison and improvement project (AgMIP): protocols and pilot studies. Agric. Forest Meteorol., 2013, 170, 166–182.

Ramaraj, A. P. and Geethalakshmi, V., Analysing the uncertainty in climate projected by RCP 4.5 over Coimbatore. Ecol., Environ. Conserv., 2014, 20(3), 125–128.

Chiew, F. H. S. and Siriwardena, L., Estimation of SIMHYD Parameter Values for Application in Ungauged Catchments 1, 2005.

Wahid, A., Gelani, S., Ashraf, M. and Foolad, M. R., Heat tolerance in plants: an overview. Environ. Exp. Bot., 2007, 61, 199– 223.

Pathak, A., Pathak, P. and Sharma, K., Recent development in Boro rice improvement and production for raising rice yield in Assam. Boro Rice IRRI-India Office, New Delhi, 2003.

Jagadish, S., Muthurajan, R., Oane, R., Wheeler, T. R., Heuer, S., Bennett, J. and Craufurd, P. Q., Physiological and proteomic approaches to address heat tolerance during anthesis in rice (Oryza sativa l). J. Exp. Bot., 2010, 61, 143–156.

Lele, U., Food Security for a Billion Poor, American Association for the Advancement of Science, 2010.

Bahuguna, R. N., Jha, J., Pal, M., Shah, D., Lawas, L. M., Khetarpal, S. and Jagadish, K. S., Physiological and biochemical characterization of nerica‐l‐44: A novel source of heat tolerance at the vegetative and reproductive stages in rice. Physiol. Plant., 2015, 154, 543–559.

Matsui, T., Namuco, O. S., Ziska, L. H. and Horie, T., Effects of high temperature and CO2 concentration on spikelet sterility in indica rice. Field Crops Res., 1997, 51, 213–219.

Xie, X., Li, B., Li, Y. and Shen, S., High temperature harm at flowering in Yangtze river basin in recent 55 years. Jiangsu J. Agric. Sci., 2009, 25, 28–32.

Lin, C.-J., Li, C.-Y., Lin, S.-K., Yang, F.-H., Huang, J.-J., Liu, Y.H. and Lur, H.-S., Influence of high temperature during grain filling on the accumulation of storage proteins and grain quality in rice (Oryza sativa l). J. Agric. Food Chem., 2010, 58, 10545– 10552.

Rani, B. A. and Maragatham, N., Effect of elevated temperature on rice phenology and yield. Indian J. Sci. Technol., 2013, 6, 5095–5097.

Ahmed, N. et al., Effect of high temperature on grain filling period, yield, amylose content and activity of starch biosynthesis enzymes in endosperm of basmati rice. J. Sci. Food Agric., 2015, 95, 2237–2243.

Yoshida, S., Fundamentals of rice crop science. Int. Rice Res. Inst., 1981, pp. 1–279.

Prasad, P., Boote, K., Allen Jr, L., Sheehy, J. and Thomas, J., Species, ecotype and cultivar differences in spikelet fertility and harvest index of rice in response to high temperature stress. Field Crops Res., 2006, 95, 398–411.

Chaturvedi, A. K., Bahuguna, R. N., Shah, D., Pal, M. and Jagadish, S. K., High temperature stress during flowering and grain filling offsets beneficial impact of elevated CO2 on assimilate partitioning and sink-strength in rice. Sci. Rep., 2017, 7, 1–13.

Zhang, B., Zheng, J., Huang, S., Tian, Y., Peng, L., Bian, X. and Zhang, W., Temperature differences of air-rice plant under different irrigated water depths at spiking stage. J. Appl. Ecol., 2008, 19, 87–92.

Khan, S. et al., Mechanisms and adaptation strategies to improve heat tolerance in rice. A review. Plants, 2019, 8, 508.

Kumar, P., Geneletti, D. and Nagendra, H., Spatial assessment of climate change vulnerability at city scale: a study in Bangalore, India. Land Use Policy, 2016, 58, 514–532.

Mishra, A. K. and Singh, V. P., Drought modelling – a review. J. Hydrol., 2011, 403(1–2), 157–175.

Khichar, M. L. and Niwas, R., Thermal effect on growth and yield of wheat under different sowing environments and planting systems. Indian J. Agric. Res., 2007, 41, 92–96.

Al-Karaki, G. N., Phenological development-yield relationships in durum wheat cultivars under late-season high-temperature stress in a semiarid environment. Int. Scholarly Res. Network, 2012, 2012, 1–7; doi:10.5402/2012/456856.

Das, R., Ali, M. E., Abd Hamid, S. B., Ramakrishna, S. and Chowdhury, Z. Z., Carbon nanotube membranes for water purification: a bright future in water desalination. Desalination, 2014, 336, 97–109.

Effect of temperature on brown planthopper Infestation in rice using hyperspectral remote Sensing

Abstract Views :93 | PDF Views:61

Authors

S. Sivaranjani ¹, V. Geethalakshmi ¹, S. Pazhanivelan ¹, J. S. Kennedy ¹, S. P. Ramanathan ¹, R. Gowtham ², K. Pugazenthi ¹

Affiliations
1 Tamil Nadu Agricultural University, Coimbatore 641 003, India., IN
2 Indian Farmers Fertilizers Cooperative Limited, Coimbatore 641 003, India., IN

Source

Current Science, Vol 124, No 10 (2023), Pagination: 1194-1200

Abstract

Hyperspectral remote sensing captures images in multiple wavelengths and is widely used to detect plant stress in agriculture. A study was conducted on brown planthopper (BPH) infestation in rice at various temperature regimes (15°C, 20°C, 25°C, 30°C and 35°C). The experimentation was done in the Environmental Control Chamber, Tamil Nadu Agricultural University, Coimbatore, India. The field spectroradiometer and vegetation indices were used to study the early and late infestations of BPH in rice. The results reveal that reflectance at certain wavelengths (550, 670 and 700 nm) indicates plant stress. Among the vegetation indices, MCARI performed better than NDVI, PRI, NDRE and SR for the detection of early and late infestation of BPH. Hence, hyperspectral reflectance from rice has been used to detect pest damage and improve management policies.

Keywords

Brown planthopper, hyperspectral sensor, Plant stress, rice, vegetation indices.

Full Text

References

Oghaz, M. M. D., Razaak, M., Kerdegari, H., Argyriou, V. and Remagnino, P., Scene and environment monitoring using aerial im-agery and deep learning. IEEE, 2019.

Childs, N. and LeBeau, B., Rice Outlook, Report, FAO, 2022.

Saravanakumar, V., Lohano, H. D. and Balasubramanian, R., A dis-trict-level analysis for measuring the effects of climate change on production of rice: evidence from southern India. Theor. Appl. Cli-matol., 2022, 150(3–4), 941–953.

Min, S., Lee, S. W., Choi, B.-R., Lee, S. H. and Kwon, D. H., In-secticide resistance monitoring and correlation analysis to select appropriate insecticides against Nilaparvata lugens (Stål), a migra-tory pest in Korea. J. Asia-Pac. Entomol., 2014, 17(4), 711–716.

Cabauatan, P. Q., Cabunagan, R. C. and Choi, I.-R., Rice viruses transmitted by the brown planthopper Nilaparvata lugens. In Planthoppers: New Threats to the Sustainability of Intensive Rice Production Systems in Asia, IRRI Books, International Rice Res-earch Institute, 2009, pp. 357–368.

Mohapatra, S. D. et al., Eco-smart pest management in rice farming: prospects and challenges. Oryza, 2019, 56(Special Issue), 143–155.

Muller, A., Prakash, A., Lazutkaite, E. M. D., Amdihun, A. and Ouma, J., Scientific linkages between climate change and (trans-boundary) crop pest and disease outbreaks. In TMG Working Paper, 2022, p. 29.

Abd El-Ghany, N. M., Abd El-Aziz, S. E. and Marei, S. S., A review: application of remote sensing as a promising strategy for insect pests and diseases management. Environ. Sci. Pollut. Res., 2020, 27, 33503–33515.

Wang, F. M., Huang, J. F. and Wang, X. Z., Identification of optimal hyperspectral bands for estimation of rice biophysical parameters. J. Integr. Plant Biol., 2008, 50(3), 291–299.

Katsoulas, N., Elvanidi, A., Ferentinos, K. P., Kacira, M., Bartzanas, T. and Kittas, C., Crop reflectance monitoring as a tool for water stress detection in greenhouses: a review. Biosyst. Eng., 2016, 151, 374–398.

Curran, P., Principles of Remote Sensing, Longman, London, UK, 1985.

Prasannakumar, N. R., Chander, S., Sahoo, R. N. and Gupta, V. K., Assessment of brown planthopper (Nilaparvata lugens) damage in rice using hyperspectral remote sensing. Int. J. Pest Manage., 2013, 59(3), 180–188.

Seager, S., Turner, E. L., Schafer, J. and Ford, E. B., Vegetation’s red edge: a possible spectroscopic biosignature of extraterrestrial plants. Astrobiology, 2005, 5(3), 372–390.

Yang, C. M. and Chen, R. K., Differences in growth estimation and yield prediction of rice crop using satellites data simulated from near ground hyperspectral reflectance. J. Photogramm. Remote Sensing, 2007, 12(1), 93–105.

Liu, X. D. and Sun, Q. H., Early assessment of the yield loss in rice due to the brown planthopper using a hyperspectral remote sensing method. Int. J. Pest Manage., 2016, 62(3), 205–213.

Liu, J., Han, J., Chen, X., Shi, L. and Zhang, L., Nondestructive de-tection of rape leaf chlorophyll level based on vis–NIR spectroscopy. Spectrochim. Acta Part A, 2019, 222, 117202.

Huang, J., Liao, H., Zhu, Y., Sun, J., Sun, Q. and Liu, X., Hyper-spectral detection of rice damaged by rice leaf folder (Cnaphalo-crocis medinalis). Comput. Electron. Agric., 2012, 82, 100–107.

Abdel-Rahman, E. M., Ahmed, F. B., van den Berg, M. and Way, M. J., Potential of spectroscopic data sets for sugarcane thrips (Fulmekiola serrata Kobus) damage detection. Int. J. Remote Sens-ing, 2010, 31(15), 4199–4216.

Madasamy, B., Balasubramaniam, P. and Dutta, R., Microclimate-based pest and disease management through a forewarning system for sustainable cotton production. Agriculture, 2020, 10(12), 641.

Penuelas, J., Gamon, J. A., Griffin, K. L. and Field, C. B., Assessing community type, plant biomass, pigment composition, and photo-synthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sensing Environ., 1993, 46(2), 110–118.

Daughtry, C. S. T., Walthall, C. L., Kim, M. S., DeColstoun, E. B. and McMurtrey Iii, J. E., Estimating corn leaf chlorophyll concen-tration from leaf and canopy reflectance. Remote Sensing Environ., 2000, 74(2), 229–239.

Rouse, J. W., Haas, R. H., Schell, J. A. and Deering, D. W., Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ., 1974, 351(1), 309.

Gates, D. M., Keegan, H. J., Schleter, J. C. and Weidner, V. R., Spectral properties of plants. Appl. Opt., 1965, 4(1), 11–20.

Fitzgerald, G. J., Rodriguez, D., Christensen, L. K., Belford, R., Sadras, V. O. and Clarke, T. R., Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environ-ments. Precis. Agric., 2006, 7, 233–248.

Carter, G. A., Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Remote Sensing, 1994, 15(3), 697–703.

Yang, C. M., Cheng, C. H. and Chen, R. K., Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder. Crop Sci., 2007, 47(1), 329–335.

Sahoo, R. N., Ray, S. S. and Manjunath, K. R., Hyperspectral re-mote sensing of agriculture. Curr. Sci., 2015, 108(5), 848–859.

Hunt Jr, E. R. and Rock, B. N., Detection of changes in leaf water content using near-and middle-infrared reflectances. Remote Sens-ing Environ., 1989, 30(1), 43–54.

Clarke, A. and Fraser, K. P. P., Why does metabolism scale with temperature? Funct. Ecol., 2004, 18(2), 243–251.

Taylor, R. A. J., Herms, D. A., Cardina, J. and Moore, R. H., Climate change and pest management: unanticipated consequences of trophic dislocation. Agronomy, 2018, 8(1), 7.

Hannigan, S., Nendel, C. and Krull, M., Effects of temperature on the movement and feeding behaviour of the large lupine beetle, Sitona gressorius. J. Pest Sci., 2022, 1–14.

Priyadarshini, S., Ghosh, S. K. and Nayak, A. K., Field screening of different chilli cultivars against important sucking pests of chilli in West Bengal. Bull. Environ., Pharmacol. Life Sci., 2019, 8(7), 134–140.

Yan, T., Xu, W., Lin, J., Duan, L., Gao, P., Zhang, C. and Lv, X., Com-bining multi-dimensional convolutional neural network (CNN) with visualization method for detection of Aphis gossypii Glover infec-tion in cotton leaves using hyperspectral imaging. Front. Plant Sci., 2021, 12, 604.

Polivova, M. and Brook, A., Detailed investigation of spectral vegeta-tion indices for fine field-scale phenotyping. Vegetation Index Dyna-mics, 2021.

Huang, J. R., Sun, J. Y., Liao, H. J. and Liu, X.-D., Detection of brown planthopper infestation based on SPAD and spectral data from rice under different rates of nitrogen fertilizer. Precis. Agric., 2015, 16, 148–163.

Vanegas, F., Bratanov, D., Powell, K., Weiss, J. and Gonzalez, F., A novel methodology for improving plant pest surveillance in vine-yards and crops using UAV-based hyperspectral and spatial data. Sensors, 2018, 18(1), 260.

Pinter Jr, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S. T. and Upchurch, D. R., Remote sensing for crop management, 2003.

Broge, N. H. and Leblanc, E., Comparing prediction power and sta-bility of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing Environ., 2001, 76(2), 156–172.

de Lima, I. P., Jorge, R. G. and de Lima, J. L. M. P., Remote sensing monitoring of rice fields: Towards assessing water saving irrigation management practices. Front. Remote Sensing, 2021, 2, 762093.

Kurbanov, R. and Zakharova, N., Justification and selection of vegetation indices to determine the early soybeans readiness for harvesting. EDP Sciences, 2021.

Luo, J., Huang, W., Zhao, J., Zhang, J., Zhao, C. and Ma, R., Detecting aphid density of winter wheat leaf using hyperspectral measure-ments. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2013, 6(2), 690–698.

Prabhakar, M., Prasad, Y., Desai, S. and Thirupathi, M., Spectral and spatial properties of rice brown plant hopper and groundnut late leaf spot disease infestation under field conditions. J. Agrome-teorol., 2013, 15, 57–62.

Sogawa, K., The rice brown planthopper: feeding physiology and host plant interactions. Ann. Rev. Entomol., 1982, 27(1), 49–73.

Watanabe, T. and Kitagawa, H., Photosynthesis and translocation of assimilates in rice plants following phloem feeding by the planthopper Nilaparvata lugens (Homoptera: Delphacidae). J. Econ. Entomol., 2000, 93(4), 1192–1198.

Liu, J. L., Yu, J. F., Wu, J. C., Yin, J. L. and Gu, H. N., Physiological responses to Nilaparvata lugens in susceptible and resistant rice varie-ties: allocation of assimilates between shoots and roots. J. Econ. Entomol., 2008, 101(2), 384–390.

Vanitha, K., Suresh, S. and Gunathilagaraj, K., Influence of brown planthopper Nilaparvava lugens feeding on nutritional biochemis-try of rice plant. ORYZA – Int. J. Rice, 2011, 48(2), 142–146.

Username
Password
Remember me