Identification and Counting of Two Important Greenhouse Pests by Image Processing, Case Study: Whitefly and Thrips

Abstract

Introduction Lack of water resources, increasing demands for food, the optimal use of water and land, and food security are of the most important reasons for the development of greenhouses in the country. The benefits of greenhouse cultivation consisted of the possibility to produce off-season, increase harvest period, reduce the production costs, increase economic efficiency and etc. Regarding the conditions of the greenhouse, in terms of temperature and humidity, a site is susceptible to contamination with various pests and diseases, which can cause a lot of damages to the products. So, for a high-quality product, identification and timely control of pests are necessary. The need for spraying in a timely manner, with a sufficient number of times, is to have accurate information on the population of pests in a greenhouse environment at different times. Whiteflies, thrips, and aphids are among the most commonly known harmful insects in the world, causing severe damages to greenhouse plants. Materials and Methods Twenty yellow sticky cards were randomly selected in different parts of the greenhouse of cucumbers in the Amzajerd district of Hamadan. From each card, 45 photos were taken with Canon IXUS 230HS digital camera with a resolution of 12.1 Megapixels at a distance of 20 centimeters. Before each image processing, trapped insects were initially identified and counted by three entomologists. At this stage, three types of insects (two harmful insects, whitefly and thrips and non-harmful insect) were identified. Then the images were transferred to Matlab software. The algorithm of identifying and counting the whitefly was the following six steps: Step 1: Convert the original image to the gray level image Step 2: Correcting the effects of non-uniform lighting Step 3: Determine the optimal threshold and convert the gray level image to the binary image Step 4: Remove objects smaller than Whitefly Step 5: Fill the holes in the image Step 6: Counting broken segments The algorithm of identifying and counting the thrips was the following eight steps: Step 1: Convert the original image to the gray level image Step 2: Correcting the effects of non-uniform lighting Step 3: Determine the optimal threshold and convert the gray level image to the binary image Step 4: Prepare image negatives Step 5: Remove objects smaller than the thrips Step 6: Remove the thrips and isolate the rest of the objects Step 7: Split the thrips Step 8: Count the thrips Results and Discussion Relative accuracy, root mean square error (RMSE) and Coefficient of variation of the RMSE of Whitefly counting in image processing system were 94.4%, 15.3 and 5.5% respectively. The results of the T-test between two methods indicated that there was no significant difference between them. The mean relative accuracy, root mean square error (RMSE) and Coefficient of variation of the RMSE of the thrips count in the image processing system were 87.4%, 18 and 5.9% respectively. There was no significant difference between the two methods. Conclusions The proposed image processing algorithm was able to detect whiteflies and thrips with a relative accuracy of 94.5% and 87.4%, respectively. In addition to simplicity, this method has the ability to adapt to different conditions. Also, with some changes in the proposed algorithm, the system will also be able to identify other pests. In order to design an intelligent spray system in the greenhouse, the population of pests needs to be monitored frequently, so the identification and counting of pests will be necessary for the intelligent spray system.

Keywords

• auto counting
• insect
• intelligent spraying
• yellow sticky card