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.

中文翻译：

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使用深度学习的基于物联网的棉粉虱预测

农业正遭受低生育率和气候危害的问题，例如虫害和疾病增加。病虫害侵袭的早期预测对于提高农业生产力非常有帮助。害虫（粉虱）侵袭对棉花作物产量有很大影响。提出物联网解决方案来预测粉虱攻击采取预防措施。借助物联网和基于温度、湿度、降雨和风速等环境参数的 RBFN 算法，开发了害虫预测系统 (IPPS)。农药的害虫预警和质量控制提出了预测粉虱攻击的经济阈值水平。经济阈值水平和RBFN算法用于利用温度、湿度、降雨量和风速预测粉虱攻击。f -measures、precision、recall、Cohen's kappa、ROC AUC和混淆矩阵用于确定RBFN算法的性能。拟建的害虫预测系统部署在害虫高发区，为农民提供害虫预测信息以采取控制措施。