Machinery Laboratory of China Agriculture Citrus Research System

Guangzhou, China

Machinery Laboratory of China Agriculture Citrus Research System

Guangzhou, China

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Wen T.,Key Laboratory of Key Technology for South Agricultural Machinery and Equipment | Wen T.,Machinery Laboratory of China Agriculture Citrus Research System | Wen T.,South China Agricultural University | Hong T.,Key Laboratory of Key Technology for South Agricultural Machinery and Equipment | And 8 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2011

The number of Bactrocera Dorsalis in occurring period is the important parameter which threats the growth situation for fruit trees and is the basis of implementing variable rate technology. In order to realize detecting the occurrence of Bactrocera Dorsalis real-time and fasting in large-scale orchard, machine vision technologies based on moving object trace tracking were employed to trace Bactrocera Dorsalis behavies around traps real-time, so as to achieve statistics of their number into the hole precisely. The fore 50000 video image were selected as evaluation samples which collected in feed room of Resource and Environment College in South China Agricultural University using vision monitoring platform for Bactrocera Dorsalis. Through comparison of results with methods of artificial and machine vision detecting, the experiment indicated that the number of Bactrocera Dorsalis detected by artificial and machine vision were 85 heads and 78 heads, respectively. The loss rate of detecting using machine vision was 9.4%, which can meet the demands of pests' monitoring.


Wen T.,Key Laboratory of Key Technology for South Agricultural Machinery and Equipment | Wen T.,Machinery Laboratory of China Agriculture Citrus Research System | Wen T.,South China Agricultural University | Hong T.,Key Laboratory of Key Technology for South Agricultural Machinery and Equipment | And 10 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2012

China Nowadays, solar photovoltaic energy applications has been penetrated into all areas of industrial and agricultural manufacturing in China. Due to fluctuation and randomness of solar radiation, solar efficiency was not fully utilized and operation devices were vulnerable to damages because of unstable power supplies. In this paper, regular silicon solar batteries were studied and the intelligent detection technology based on perturbation and observation was employed to track maximum power point of silicon solar battery and applied to the solar power equipment. By comparing the traditional method and the (maximum power point tracking) MPPT method, the experiment indicated that power outputs using MPPT method improved 11% compared with that by traditional method and suppressed power supply fluctuation effectively.

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