Prediction of cotton leaf curl virus disease and its management through resistant germplasm and bio-products

Saeed, Humera; Ehetisham-ul-Haq, Muhammad; Atiq, Muhammad; Kamran, Muhammad; Idrees, Muhammad; Ali, Shaukat; Burhan, Muhammad; Mohsan, Muhammad; Iqbal, Muhammad; Nazir, Shahid; Yasin, Saleem Il

doi:10.1080/03235408.2018.1443602

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…Whitefly has positive relation with temperature and wind speed, while negative relation with humidity and rainfall. e whitefly population increases with the increase of temperature and wind speed, while the whitefly population decreases with the increase of relative humidity and rainfall [11].…”

Section: Prototype Model and Deploymentmentioning

confidence: 91%

“…Optimum values Temperature [11,32] 35-51°C Humidity [11,32] Below 65% Rainfall [11] 1-2 mm Wind speed [11] 5.50-5.75 km/h e average 73.5% hotspots of whitefly above ETL have been observed during field evaluation in the experimental area in 2018 and 2019, while on the other, the deep neural network prediction of whitefly accuracy of 82.88% has been observed. Efficient monitoring of environmental parameters is important for effective prediction processes to achieve the desired results.…”

Section: Abiotic Factorsmentioning

confidence: 97%

“…(i) Whitefly's attack time/month is an important factor because in a specific time/month, the growth of whitefly is probably more (ii) Environmental factors such as (temperature, humidity, rainfall, and wind speed) are major factors for the growth of whitefly insect pests [11] (iii) Whitefly insect pest growth is more in a dry environment (iv) High wind speed can transfer the larva and eggs from one place/area to another e study aims is to seek protection from insect pest attacks with the early prediction using environmental factors to produce high crop yield and to alert the farmer of taking prevention measures. e remaining paper is structured into "related work," "methodology," "implementation," and "results and discussion.…”

Section: Introductionmentioning

confidence: 99%

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IOT-Based Cotton Whitefly Prediction Using Deep Learning

Saleem

Kazmi

Bajwa

et al. 2021

Scientific Programming

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Agriculture is suffering from the problem of low fertility and climate hazards such as increased pest attacks and diseases. Early prediction of pest attacks can be very helpful in improving productivity in agriculture. Insect pest (whitefly) attack has a high influence on cotton crop yield. Internet of Things solution is proposed to predict the whitefly attack to take prevention measures. An insect pest prediction system (IPPS) was developed with the help of the Internet of Things and a RBFN algorithm based on environmental parameters such as temperature, humidity, rainfall, and wind speed. Pest Warning and Quality Control of Pesticides proposed an economic threshold level for prediction of whitefly attack. The economic threshold level and RBFN algorithm are used to predict the whitefly attack using temperature, humidity, rainfall, and wind speed. The seven evaluation metrics accuracy, f-measures, precision, recall, Cohen’s kappa, ROC AUC, and confusion matrix are used to determine the performance of the RBFN algorithm. The proposed insect pest prediction system is deployed in the high influenced region of pest that provides pest prediction information to the farmer to take control measures.

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Section: Prototype Model and Deploymentmentioning

confidence: 91%