Investigating the Possibility of Using the Wireless Sensor Network (WSN) and image processing in an early detection and diagnosis of the pest of Greenhouse White fly

Managing the production of greenhouse products requires knowledge of controlling many environmental factors and plant nutrition and fight against pests and plant diseases. Recognition pests and fight against them is one of the most important activities in the process of production of greenhouse products. Pre-knowledge of the demographic density of insect pests is one of the effective methods of pesticides and reduce their levels of use, especially for insect pest control toxins. Wireless Sensor Networking Technologies (WSN) is one of the new technologies used to sense the environment and collect and transmit information to the user or the central station to view and respond appropriately to an occurrence or phenomenon. In this study, the use of WSN in monitoring, timely diagnosis of greenhouse white flies, design and mapping of greenhouse contamination was investigated. For this goal, 3750 images of 15 sticky traps with white flies that attached to Melon greenhouse in Isfahan Agricultural Jihad Research Center were provide and transmitted online using a WSN to a computer located at a distance of 900 meters from the greenhouse. The color images of the sticky traps are acquired by using 15 digital cameras were converted to gray colored images using MATLAB software, then after image classification with Support Vector Machine (SVM) classifier based on their features, are divided into two categories of images: whiteflies affected image and whiteflies unaffected image. After identification of the white flies, number of pests was counted and infection maps of Greenhouse with ArcMap10.2 software was drawn up. Assessment of the system showed that accuracy of SVM algorithm for categorizing images of sticky traps was 97.73%, and the average values of statistic parameters of the Confusion matrix for 15 traps including sensitivity, accuracy, specificity and classification accuracy were 98.46%, 83.31%, 99.08% and 97.72% respectively. The overall accuracy of the system for detection and counting Greenhouse whitefly pests is 97.71%. The average root mean square error (RMSE) in estimating of the number of white flight by image processing and direct counting was between 1 and 5.03. Therefore, the system is suitable for detecting and tracing and counting the number of trapped white flies, and it is possible to design appropriate greenhouse poisoning plans to fight this pest.