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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.

Abstract-With the emergence and popularity of web application, threats related to web applications has increased to large extent. Among many other web applications threats Structured Query Language Injection Attack (SQLIA) is the dominant in its use due to its ability to access the data. Many solutions are proposed in this regard that has success in specific conditions. The proposed model is based on the dynamic analyzer model. The proposed model also has certain advantages like wide applicability, fast response time, coverage to large number of techniques of SQL Injections (SQLI) and efficient in term of resource usage.

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Global climatic changes have severe impacts on agricultural productivity. Enhanced pest attacks on crops are one of the major impacts on sustainable developments in agriculture to come up with the needs of the ever-increasing human population. Early warning of a pest attack is important for Integrated Pest Management (IPM) activities to be effective. Early warning of pest attacks is also important for judicious use of pesticides for efficient use of resources for minimal impacts on the environment. Sugarcane is the major cash crop and is also severely affected by different types of pests. This study proposed stem borer attack prediction on sugarcane crops by directly sensed environment conditions from the crop field using the Internet of Things (IoT). Data-driven machine learning decisions are made to predict the pest population from directly sensed crop field temperature, humidity, and rainfall conditions. Directly sensed environment conditions and the data-driven decision by the Naïve Bays classification approach help to accurately predict the occurrence of a pest attack above or below the Economic Threshold Level (ETL). The performance of the proposed solution is judged in terms of the performance of the machine learning model and the accuracy of the proposed solution in the prediction of the stem borer attack on the sugarcane crop. The main objective of the proposed solution is to support sustainable developments in agriculture by using the IoT to capture the crop field context and machine learning to make data-driven decisions.