Web enabled and weather based forewarning of yellow stem borer [Scirpophaga incertulas (Walker)] and leaf folder [Cnaphalocrcis medinalis (Guenee)] for different rice growing locations of India

Kumar, Amrender; Misra, Tanuj; Batra, Kamal; Sharma, Raman; Mishra, A. K.; Vennila, S.; Tanwar, R. K.; Singh, NiIRANJAN; Wahi, Priyanka; Rajendran, R.; Gowda, D. K. S.; Sarao, Preetinder Singh; Jalgaonkar, VN; Roy, Sujit; Chattopadhyay, C.

doi:10.54302/mausam.v67i4.1413

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…The models may depict both low and high intensities of pest outbreaks (Chattopadhyay et al, 2005). The prediction values obtained from the developed model were found to be pretty similar to the actual measured values in the field, as also reported by Kumar et al (2016Kumar et al ( , 2018.…”

Section: Web-enabled Systemsupporting

confidence: 84%

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Predicting the incidence and severity of wheat aphids and development of a web-enabled forewarning system in India

Kumar¹,

Sharma²,

Singh³

et al. 2023

JEB

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Aim: Forecasting the incidence and severity of aphids, the major insect pest of wheat, is expected to significantly help in their management. In the present study, a set of weather-based models were developed to predict the timing and severity of Rhopalosiphum maidis infestation at Ludhiana falling under the North Western Plain Zone and R. padi at Niphad in the Peninsular Zone of India. Methodology: The weather indices-based regression models for two locations, Ludhiana and Niphad, were developed using the aphid population and weather data gathered over eight years (2006–14), and the models' predictive accuracy was successfully tested over four additional years (2014-18). The developed statistical models were transformed into three-tier architecture, web-based system, i.e. Presentation, application and data tier for dissemination of information. Results: The developed models can predict the crop’s age - when aphids first colonize the plants, when the aphid population attains the peak and the information about the peak intensity of the aphid population. For predicting the crop’s age at which population peaked at Ludhiana, the weighted interaction of the relative humidity (RH) in the evening and the number of hours of sunshine (NHS) along with the weighted interaction of minimum temperature and RH (morning) were important parameters while, at Niphad, the weighted NHS and the interaction of RH (morning and evening) were important. Likewise, for predicting the maximum aphid population at Ludhiana, the weighted interaction of minimum temperature and RH (morning) were important, while at Niphad, the key parameters were the weighted interaction of RH (evening) with the NHS. Interpretation: A prototype system developed to forecast the location-specific (Ludhiana and Niphad) infestation of wheat crops by aphids is expected to facilitate aphid management through an accurate forewarning at the locations.

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Section: Web-enabled Systemsupporting

confidence: 84%

“…inear) methods (Kumar et al, 2012(Kumar et al, , 2016Sharma et al, 2015;Singh et al, 2016). While the major purpose of forewarning is to trigger pest management measures in a timely fashion, it also facilitates communication between researchers and farmers.…”

Section: Predicting the Incidence And Severity Of Wheat Aphids And De...mentioning

confidence: 99%

Predicting the incidence and severity of wheat aphids and development of a web-enabled forewarning system in India

Kumar¹,

Sharma²,

Singh³

et al. 2023

JEB

View full text Add to dashboard Cite

show abstract

Mobile Application for Dissemination of Advisories on Pest and Diseases of Rice & Mustard

Rakhee

Kumar

Thakur

et al. 2018

Communications in Computer and Information Science

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AI based hybrid CNN-LSTM model for crop disease prediction: An ML advent for rice crop

Jain

Ramesh

2021

2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Web enabled and weather based forewarning of yellow stem borer [Scirpophaga incertulas (Walker)] and leaf folder [Cnaphalocrcis medinalis (Guenee)] for different rice growing locations of India

Cited by 4 publications

References 3 publications

Predicting the incidence and severity of wheat aphids and development of a web-enabled forewarning system in India

Predicting the incidence and severity of wheat aphids and development of a web-enabled forewarning system in India

Mobile Application for Dissemination of Advisories on Pest and Diseases of Rice & Mustard

AI based hybrid CNN-LSTM model for crop disease prediction: An ML advent for rice crop

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