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Management of Bemisia whitefly and its virus transmission in Japan
Ken-Ichiro Honda 한국응용곤충학회 2008 한국응용곤충학회 학술대회논문집 Vol.2008 No.10
The whitefly Bemisia tabaci (Gennadius) (Homoptera: Aleyrodidae) is one of the most important agricultural pests in Japan, that causes retard of plant growth and sooty moulds through excreted honeydew by direct sucking of pholoem sap, and additionally transmits several kinds of plant virus. B. tabaci consists of more than 20 biotypes which possess different ecological or physiological characters but cannot be distinguished from others morphologically. In Japan, exotic B and Q biotypes are the common pests of vegetables, flowers and ornamental plants. B biotype, the silver-leaf whitefly, was first recorded in Aichi Prefecture, Tokai region, in 1989 and expanded its distribution to almost all part of Japan, except for the northern area, within several years. Q biotype was recently found in Hiroshima Prefecture, Chugoku region, in 2004 and is still expanding the distribution in our country. Indigenous B. tabaci biotypes also exist in the southwestern part of Japan: JpL biotype was recorded in Honshu, Shikoku, Kyushu Islands and Nauru biotype was found in Amami and Ryukyu Islands. Although the host plants of these indigenous biotypes include some agricultural crops, these insects are not important as agricultural pests. The most serious problem in vegetable cultivation caused by B. tabaci is an intensive epidemic of the tomato yellow leaf curl disease (TYLCD) which leads to a large yield loss of tomato production in green houses. TYLCD distributes worldwide and it was found in Aich and Sizuoka Prefectures, Tokai region, and Nagasaki Prefecture, Kyushu region, simultaneously in 1996. The distribution of TYLCD expanded mainly in the western part of Japan for several years after its first finding, but recently TYLCD started to occur also in the eastern part of Japan, Kanto and Tohoku regions. Tomato yellow leaf curl virus (TYLCV), a pathogen of TYLCD, is transmitted by B or Q biotype of B. tabaci in a persistent manner. Although an effective control of B. tabaci is essential for decreasing of TYLCD outbreaks in tomato green houses, it is quite difficult to control these whiteflies only by the spraying of chemically synthesized insecticides due to their insecticide resistance. Especially, Q biotype shows a high level of resistance to pyriproxyfen and neonicotinoid insecticides. To avoid the development of insecticide resistance in B. tabaci, we are trying to combine some different control methods, for example, use of a fine mesh screen to prevent the invasion of vector insects, use of the physical-coating or microbial insecticides with the chemically synthesized insecticides to prevent the reproduction of vector insects, closing and steaming of a green house at the end of tomato cultivation to kill vector insects and prevent their escape from there, as an integrated pest management (IPM) system for B. tabaci and TYLCD control. We are also breeding TYLCV resistant varieties of tomato and considering how to use these varieties effectively.
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Imanishi, Toshiaki,Hanai, Taizo,Aoyagi, Ichiro,Uemura, Jun,Araki, Katsuhiro,Yoshimoto, Hiroshi,Harima, Takeshi,Honda , Hiroyuki,Kobayashi, Takeshi The Korean Society for Biotechnology and Bioengine 2002 Biotechnology and Bioprocess Engineering Vol.7 No.5
In order to control glucose concentration during fed-batch culture for antibiotic production, we applied so called “software sensor” which estimates unmeasured variable of interest from measured process variables using software. All data for analysis were collected from industrial scale cultures in a pharmaceutical company. First, we constructed an estimation model for glucose feed rate to keep glucose concentration at target value. In actual fed-batch culture, glucose concentration was kept at relatively high and measured once a day, and the glucose feed rate until the next measurement time was determined by an expert worker based on the actual consumption rate. Fuzzy neural network (FNN) was applied to construct the estimation model. From the simulation results using this model, the average error for glucose concentration was 0.88 g/L. The FNN model was also applied for a special culture to keep glucose concentration at low level. Selecting the optimal input variables, it was possible to simulate the culture with a low glucose concentration from the data sets of relatively high glucose concentration. Next, a simulation model to estimate time course of glucose concentration during one day was constructed using the on-line measurable process variables, since glucose concentration was only measured off-line once a day. Here, the recursive fuzzy neural network (RFNN) was applied for the simulation model. As the result of the simulation, average error of RFNN model was 0.91 g/L and this model was found to be useful to supervise the fed-batch culture.
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Takeshi Kobayashi,Toshiaki Imanishi,Taizo Hanai,Ichiro Aoyagi,Jun Uemura,Katsuhiro Araki,Hiroshi Yoshimoto,Takeshi Harima,Hiroyuki Honda 한국생물공학회 2002 Biotechnology and Bioprocess Engineering Vol.7 No.5
In order to control glucose concentration during fed-batch culture for antibiotic production, we applied so called “software sensor” which estimates unmeasured variable of interest from measured process variables using software. All data for analysis were collected from industrial scale cultures in a pharmaceutical company. First, we constructed an estimation model for glucose feed rate to keep glucose concentration at target value. In actual fed-batch culture, glucose concentration was kept at relatively high and measured once a day, and the glucose feed rate until the next measurement time was determined by an expert worker based on the actual consumption rate. Fuzzy neural network (FNN) was applied to construct the estimation model. From the simulation results using this model, the average error for glucose concentration was 0.88 g/L. The FNN model was also applied for a special culture to keep glucose concentration at low level. Selecting the optimal input variables, it was possible to simulate the culture with a low glucose concentration from the data sets of relatively high glucose concentration. Next, a simulation model to estimate time course of glucose concentration during one day was constructed using the on-line measurable process variables, since glucose concentration was only measured off-line once a day. Here, the recursive fuzzy neural network (RFNN) was applied for the simulation model. As the result of the simulation, average error of RFNN model was 0.91 g/L and this model was found to be useful to supervise the fed-batch culture.
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