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Feasibility study on a portable field pest classification system design based on DSP and 3G wireless communication technology.

Han R, He Y, Liu F - Sensors (Basel) (2012)

Bottom Line: Our system transmits the data via a commercial base station.The system can work properly based on the effective coverage of base stations, no matter the distance from the ROCP to the HCP.The authentication test showed that the image data were transmitted correctly.

View Article: PubMed Central - PubMed

Affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. rzhan8403@163.com

ABSTRACT
This paper presents a feasibility study on a real-time in field pest classification system design based on Blackfin DSP and 3G wireless communication technology. This prototype system is composed of remote on-line classification platform (ROCP), which uses a digital signal processor (DSP) as a core CPU, and a host control platform (HCP). The ROCP is in charge of acquiring the pest image, extracting image features and detecting the class of pest using an Artificial Neural Network (ANN) classifier. It sends the image data, which is encoded using JPEG 2000 in DSP, to the HCP through the 3G network at the same time for further identification. The image transmission and communication are accomplished using 3G technology. Our system transmits the data via a commercial base station. The system can work properly based on the effective coverage of base stations, no matter the distance from the ROCP to the HCP. In the HCP, the image data is decoded and the pest image displayed in real-time for further identification. Authentication and performance tests of the prototype system were conducted. The authentication test showed that the image data were transmitted correctly. Based on the performance test results on six classes of pests, the average accuracy is 82%. Considering the different live pests' pose and different field lighting conditions, the result is satisfactory. The proposed technique is well suited for implementation in field pest classification on-line for precision agriculture.

No MeSH data available.


Related in: MedlinePlus

Image of Cnaphalocrocis medinalis Guenee.
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f7-sensors-12-03118: Image of Cnaphalocrocis medinalis Guenee.

Mentions: The dataset used in this study is a library of live pest images created by the first author over a period of nearly a year. A pest trap was set up in the Fuyang Plant Protection Station (Zhejiang Province, China) and cleared every morning. Captured live pests were photographed and then released. Cnaphalocrocis medinalis Guenee is taken as an example and the image is shown in Figure 7.


Feasibility study on a portable field pest classification system design based on DSP and 3G wireless communication technology.

Han R, He Y, Liu F - Sensors (Basel) (2012)

Image of Cnaphalocrocis medinalis Guenee.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376617&req=5

f7-sensors-12-03118: Image of Cnaphalocrocis medinalis Guenee.
Mentions: The dataset used in this study is a library of live pest images created by the first author over a period of nearly a year. A pest trap was set up in the Fuyang Plant Protection Station (Zhejiang Province, China) and cleared every morning. Captured live pests were photographed and then released. Cnaphalocrocis medinalis Guenee is taken as an example and the image is shown in Figure 7.

Bottom Line: Our system transmits the data via a commercial base station.The system can work properly based on the effective coverage of base stations, no matter the distance from the ROCP to the HCP.The authentication test showed that the image data were transmitted correctly.

View Article: PubMed Central - PubMed

Affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. rzhan8403@163.com

ABSTRACT
This paper presents a feasibility study on a real-time in field pest classification system design based on Blackfin DSP and 3G wireless communication technology. This prototype system is composed of remote on-line classification platform (ROCP), which uses a digital signal processor (DSP) as a core CPU, and a host control platform (HCP). The ROCP is in charge of acquiring the pest image, extracting image features and detecting the class of pest using an Artificial Neural Network (ANN) classifier. It sends the image data, which is encoded using JPEG 2000 in DSP, to the HCP through the 3G network at the same time for further identification. The image transmission and communication are accomplished using 3G technology. Our system transmits the data via a commercial base station. The system can work properly based on the effective coverage of base stations, no matter the distance from the ROCP to the HCP. In the HCP, the image data is decoded and the pest image displayed in real-time for further identification. Authentication and performance tests of the prototype system were conducted. The authentication test showed that the image data were transmitted correctly. Based on the performance test results on six classes of pests, the average accuracy is 82%. Considering the different live pests' pose and different field lighting conditions, the result is satisfactory. The proposed technique is well suited for implementation in field pest classification on-line for precision agriculture.

No MeSH data available.


Related in: MedlinePlus