ORAL - International Conference of Agricultural Engineering

the pheromone trap to attract target insects specifically and cropping a low-noise part of theimage area to allow easy analysis, we can estimate the number of target insects in imagedata on the proposed system. The number of target insects can be estimated with highaccuracy, and this simple image analysis worked well to reduce the labor needed forcounting and to easily handle a large mass of image data (Fukatsu et al., 2012).4. DiscussionWe have proposed a practical image data monitoring system for agricultural use with FieldServers, and we tested the monitoring system with several agricultural applications toevaluate the effectiveness and potential of the system. As a practical image analysis methodfor image data collected at a field site, we proposed a simple image analysis method thatuses a combination of basic image processing techniques (cropping, subtracting andbinarizing functions). Although it is generally difficult for simple image analyses to producedetailed and accurate results, our proposed image analysis method can provide usefulinformation, and it has the potential to be effective for various applications.In the application for detecting farm operation, we used our image analysis method to filterextraneous image data with the browsing Web application. It has been important but difficultto manually check collected image data for detecting targets, because most of the imagedata were extraneous. The filtering method is effective and practical for manual checking,and it reduces user effort. In the application for counting target insects, we estimated thenumber of targets by simply applying the results of the background-difference technique tothe part of the image data that excluded the area most susceptible to disturbance. Thecounting method is also flexible and applicable because it doesn’t require special individualsettings and parameters.In conclusion, we constructed a system that can be easily used by non-expert users. Themonitoring system facilitates the use of varied applications for many users, and the simpleimage analysis method facilitates the practical use of image monitoring for various targets.The more that image monitoring comes into wide use, the more that researchers will be ableto easily apply advanced image analyses for practical use, based on our system.Reference listAkyildiz, I.F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensornetworks: a survey. Computer Networks, 38, 393-422.Bauchi, C., & Rath, T. (2005). Prototype of a vision based system for measurements of whitefly infestation. Acta Horticulturae (ISHS), 691, 773-780.Boissard, P., Martin, V., & Moisan, S. (2008). A cognitive vision approach to early pestdetection in greenhouse crops. Computers and Electronics in Agriculture, 62, 81-93.Choi, K., Lee, G., Han, Y.J., & Bun, J.M. (1995). Tomato Maturity Evaluation Using ColorImage Analysis. Transactions of the ASAE, 38, 171-176.Fukatsu, T., & Hirafuji, M. (2005). Field Monitoring Using Sensor-Nodes with a Web Server.Journal of Robotics and Mechatronics, 17, 164-172.Fukatsu, T., Watanabe, T., Hu, H., Yoichi, H., & Hirafuji, M. (2012) Field monitoring supportsystem for the occurrence of Leptocorisa chinensis Dallas (Hemiptera: Alydidae) usingsynthetic attractants, Field Servers, and image analysis. Computers and Electronics inAgriculture, 80, 8-16.Fukatsu, T., Hirafuji, M., & Kiura, T. (2006). An Agent System for Operating Web-basedSensor Nodes via the Internet. Journal of Robotics and Mechatronics, 18, 186-194.Iwaya, K., & Yamamoto, H. (2005). Diagnosis of Optimal Harvesting Time of Rice UsingDigital Imaging, Journal of Agricultural Meteorology, 60(2005) 981-984.Tillett, R.D. (1991). Image Analysis for Agricultural Processes: a Review of PotentialOpportunities. Journal of Agricultural Engineering Research, 50, 247-258.