Red Palm Weevil (<i>Rynchophorus Ferrugineous, Olivier</i>) Recognition by Image Processing Techniques

Abstract

Problem statement: Red palm weevil is the most destructive insect for palm trees all over the world. This research is part of developing an automated wireless red palm weevil detection and control system. The focus for this study was to develop red palm Weevil recognition system which can detect RPW in an image and can be used in wireless image sensor network which will be part of entire proposed system. Approach: Template based recognition techniques were used. Two general recognition methods i.e., Zernike and Regional Properties and an algorithm combining them were used. Besides that, a novel technique for detecting Rostrum of RPW named as 'Rostrum Analysis' was proposed and used for recognition, a conclusive algorithm based on all three techniques was also proposed, 319 test images of RPW and 93 images of other insects which found in RPW habitat were used. Results: It was found that both general techniques i.e., Regional Properties and Zernike Moments methods perform reasonably in recognizing RPW. The algorithm based on both these methods performs better than individual methods. The Rostrum Analysis outperforms better than both the earlier methods and proposed algorithm using all three analytical techniques gives best results among all discussed techniques in recognizing RPW as well as other insects. Conclusion: The most balanced and efficient recognition technique is to use the proposed conclusive algorithm which is combination of Regional Properties, Zernike Moments and Rostrum Analysis techniques. The maximum time for processing an image is 0.47 sec and the results obtained in recognizing the RPW and other insects are 97 and 88% respectively.

Monitoring Pest Insect Traps by Means of Low-Power Image Sensor Technologies

“…At the highest data rate (250 Kbps), with the standard antenna and the maximum available transmission power (10 dBm) we have established point-to-point communications over distances of up to 500 m. We have fixed the maximum point-to-point communications distance in 300 m, where the received signal strength is about −81 dBm, just 10 dB above CC1101 radio receiver sensitivity threshold [12] and the observed average packet error rate was around 1%. Reducing the transmission power to the default value (0 dBm), the one used in all of our experiments, the maximum distance is reduced to 140 m.…”

“…In Figure 1, control station is represented by a red triangle. Control station deals with (1) the storage of the captured images delivered by the image sensors, and (2) the processing of the captured images in order to determine the number of individuals found at each trap by means of image recognition algorithms like the one proposed in [12], that it is able to identify RPW individuals with a success rate higher that 95%. All the captured images with the corresponding metadata (timestamp, GPS location, processing results, etc. )…”

Pecan Weevil Recognition Using Support Vector Machine Method

“…Few approaches have already been developed such as Surveillance of Fruit Flies System for identification of Fruit Flies (Liu et al, 2009), Automated Bee Identification System for identification of Bees (Arbuckle et al, 2001), Species Identification Automated and Web Accessible System for identification of Spiders (Do et al, 1999), Red Palm Weevil Recognition System for identification of Red Palm Weevil (Al-Saqer and Hassan, 2011a;2011b).…”

A Robust Recognition System for Pecan Weevil using Artificial Neural Networks

“…For example, Liu et al (2009) developed an identification system for fruit flies, Arbuckle et al (2001) developed a system for identifying bees and Do et al (1999) developed an identification system for spiders. Recently, a recognition system has also been developed for red palm weevil (Al-Saqer and Hassan, 2011a;2011b).…”