Development of an automated detection and counting system for the bagworms, Metisa plana Walker (: Psychidae)

Abdul Rashid Mohamed Shariff, Mohd Najib Ahmad, Ishak Aris, Izhal Abdul Halin and Ramle Moslim Introduction Serious damage caused by bagworm outbreak s

The economic impact from a moderate bagworm attack of 10%- 50% leaf damage may cause 43% yield loss (Wood et al., 1972; Basri and Kevan, 1995). History and past records

• The bagworm outbreaks in oil palm plantations were documented by Wood (1968) and Basri et al. (1988). • There are three major species of bagworms of oil palm that have been reported, namely Metisa plana Walker, Pteroma pendula Joannis and Mahasena corbetti Tams. • In fact, the bagworms are native species that eaten indigenous plants, nevertheless, they have changed and adapted to the hosted African oil palm, guineensis Jacquin (Wood et al., 1976; Cheong and Tey, 2012). • Based on bygone records of bagworm infestations (1986 – 2000), it is confirmed that over 63 955 ha of oil palm planted in 69 estates in Peninsular are being attacked by M. plana and P. pendula, which are known as the primary pests. • It was recorded that the infestations are of single and mixed species, which ranged from zero to 7811 ha per year (Ho et al., 2011). • Besides that, according to Chung and Sim (1993) and Chung and Narendran (1996), by assuming a mean treatment cost of RM 14 – RM 62 per ha., it is revealed that the treatment cost per conservative one round has reached amount of RM 529 311 – RM 2 344 090 over the past 15 year period. Problem Statement

• Any report on an oil palm bagworm outbreak from smallholders or estates has to be solved and overcome with proper procedures. • As a first step, inspection of infested area and investigation of bagworm population through census activity will be carried out thoroughly. • In normal practice, census is conducted manually by counting a number of bagworm population per frond through naked eyes. • Accuracy and precise data collection are doubtful, it may due to human error such as miscounting, cheating and creating data. • Data accuracy is vital prior to planning and conducting any control actions at the reported and infested area. Census for monitoring bagworm population

1. Census is conducted to count number of insect pest directly and further assessment can be made. 2. Census procedure involves superficial inspection for sign of pest incidence and a more detailed assessment or ‘enumeration’ (Corley et al., 2003). 3. Average data of census will determine the seriousness of insect pest outbreaks. This data can be used for accurate timing of pest control.

Step 1 Step 2 Step 3

Observation of infestation Cutting selected frond Counting bagworms per frond Objectives

1) To initiate automated oil palm bagworm detection and counting system for bagworm census.

2) To study the effectiveness of the automated field oil palm bagworm detector and counter versus manual census technique. Materials & Methods

• To localize/detect region of interest (RoI) in image based on color processing Image • To detect object (bagworms) and to remove unnecessary background in images. Segmentation

• To extract object/bagworms based on shape Morphological image • To remove non-targeted regions in the image processing

• To apply supervised classification method. • The pattern recognition method uses supervised learning algorithms that create classifiers based on trained data from different object classes, in this case, input data on specific sizes or wavelength and reflectance of live and dead Classification bagworms corresponding to three groups; Group 1 : Larval stage 1-3, Group 2 : Larval stage 4-7 & Group 3 : Pupal stage at the bottom part of oil palm frond.

• To count bagworm according to specific group, assigns to appropriate objects or class label. Counting • Able to differentiate between live and dead bagworms, through trained data. Continued… Experimental set up • The position of the camera from the targeted objects was set at 30 cm and 50 cm distance. • To consider changes in light condition, shadow, vibration and sudden object captured during recording.

Image processing/color processing • Color space approach based on previous report was applied to dataset from Banting site for testing. This approach consisted of series of image processing to extract bagworm and separate them from background. Refer to flow chart. • HSV threshold is set at:  Lower = [0,0, 204]  Upper = [36,255,255] Continued…

. Faster Regional-Convolutional Neural Networks (R-CNN) The algorithm was introduced by (Ren et al., 2015). It solved the problem of slow processing, experienced by the previous CNN based algorithm such as R-CNN and Fast R-CNN.

. Setup and procedure Training process: a) The training samples collected from MPOB and Banting site. b) A number frame is taken from video file c) Image samples are labeled and manually determine its bounding box around bagworm image d) The labeled image is trained using pre-train models e) Models are taken from a pre-trained CNN

Model testing: a) The video file was loaded and a frame was captured. b) An input frame image was pre-processed by resizing them to 960 x 540 pixel. c) The it processed at CNN processing to identify the object (bagworm) d) The output of the process is set of data information bounding box of the object in the current frame. e) The information from CNN processing is visualized by overlaying bounding box around the object that has confidents level above 90% and display them on a window. f) This frame is then saved in a video file Results & Discussion

■ The color space processing technique showed that the accuracy of detection was low, average of 40% and 34%, for 30 cm and 50 cm camera distance, respectively. The parameters have been set to its optimum value, but the results are still low on detection of the object and also produced a lot of wrong recognition. This was due to color similarity between damage leaflet and the bagworms.

■ The deep learning with Faster R-CNN performed and potent to be more practical and reliable for object/bagworm detection, with high accuracy, approximately at 85-95% accurate detection.

10 Color processing

30 cm Camera distances 50 cm

• Detection was low, average of 40% and 34%, for 30 cm and 50 cm camera distance, respectively • Through color space methods, it seems to be difficult to focus on targeted bagworms, although the image was filtered through Gaussian smoothing operator to remove noises and improved quality (Nameirakpam et al., 2015). • The RoI of the targeted object was wrongly detected due to color similarity between damage leaflet and the bagworms. Deep Conv. Network

30 cm Camera distances 50 cm

• By applying deep convolutional neural network, percent of detection increased tremendously, up to 88-100% accuracy • The camera distance, 30cm resulted in higher percent detection due to closer and more focus detection, which gave better object recognition effect. Thus, camera distance plays an important role to maximize details of the recognized object during snapshot (Simone et al., 2013). Continued…

• It was concluded that different image analysis approaches led to different level of detection accuracy and was proven by one-way ANOVA analysis with Least Significant Difference (LSD) test at P < 0.05. • Percent detection of the bagworms was low, ranging from 30-40%.

Wrong detection of targeted objects through color processing technique

• 88-100% accuracy using deep learning neural network. Conclusion

• Developed a system for detecting and counting bagworms based on deep learning neural network approach, with high detection accuracy, approximately 88-100%. • Able to detect and count bagworms in fast processing technique. • The camera distance, 30 cm resulted in higher per cent detection due to closer and more focus detection, which gave better object recognition effect. - Malaysia – Patent Application No. PI 2017702195. 14 June 2017.

- Pending Patent filing on method and device. GRACIAS TERIMA KASIH THANK YOU

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