Abstract
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.
Highlights
Introduction e Food and AgriculturalOrganization (FAO) predicts that the world population will reach 8 billion people by 2025 and 9.6 billion people by 2050 [1]
Due to an increase in population, the need of foods is increasing day by day. e basic needs of humans cannot be met by using old traditional farming methods. e old farming methods consume more manpower and are less efficient. e risk of less productivity is still there by using old traditional farming methods. e crop yield can be increased by using new farming methods with the usage of IoT technology. e “Internet of ings” (IoT) is a creative idea integration through which any object can transfer data through the network [2,3]. e “Internet of ings” (IoT) is an exceptionally encouraging group of innovations that are capable of offering numerous solutions towards the modernization of agriculture [4]
We will discuss the smart agriculture system especially focusing on different insect pest predictions and their solutions
Summary
We will discuss the smart agriculture system especially focusing on different insect pest predictions and their solutions. Mekala et al [19] presented different techniques to boost up the agriculture market by using CLAY-MIST measurement techniques which were based on the sensed temperature and humidity to assess the comfort level of the crop. It presents the IoT cloud model which shows 5-layer architecture. Sense the data such as moisture level, temperature, and humidity and apply FPGA to monitor the environmental and soil condition required to know the timings of water supply to the fields for better growth of plants.
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