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Soybean (Glycine max L. Merr.) cultivars Akisirome and Peking were inoculated with Bradyrhizobium japonicum Is-21 and Rhizobium fredii USDA 194, respectively, and were grown in cylindrical pots containing sterilized vermiculite which were aerated with CO 2 -free air or ambient air to study the effects of CO 2 deficiency in the rhizosphere on plant growth, nodulation, and nitrogen fixation. The repressive effects of CO 2 -free air treatment were more conspicuous in Peking than Akisirome, and nodule number, nodule mass, amount of biologically fixed N and plant growth of Peking were reduced remarkably by the CO 2 -free air treatment.Acetylene reduction activity (ARA) of Peking inoculated with USDA 194 and Akisirome inoculated Is-21 was assayed in the absence and presence of CO 2 , ARA of Akisirome was not affected by the absence of CO 2 , while that of Peking decreased drastically.Based on these results, it was concluded that nitrogen fixation by Akisirome inoculated with B. japonicum Is-21 was not appreciably influenced by CO 2 deficiency in the rhizosphere, while that of Peking inoculated with R. fredii USDA 194 was severely repressed, and the decrease was estimated to be due to both the reduction of the nodule mass and specific nitrogen-fixing activity.

The evaluation of material's characteristics from the impact perforation images has been studied in the material engineering fields. In this method, the steel ball is shot into the material specimen, and the characteristic ofthe material is estimated from the steel ball's behavior. However, the observation of steel ball's behavior is often difficult because of the scattered fragments of the specimen. We have proposed to use the neural network to estimate the steel ball position in the impact perforation image. However, the miss-recognition of the steel ball was often seen because of the influence on the scattered fragments of the specimen. In this study, the preprocessing of the image with the high-pass filter is introduced to improve the performance of the recognition of the steel ball. We examine two types of filters using the Hanning window and the Blackman window.Keywords: impact perforation images, steel ball, neural network, high-pass filter. INTRODUCTIONIt is important to estimate the perforation limit velocity and residual velocity of the material in the design of the structure that collides with the flying object. Therefore, the characteristic evaluation from the impact perforation image by the super high-speed photograph system has been studied in the material engineering [1] [2]. In this method, a steel ball is shot into the material specimen and is localized by the sequential photograph. Then, the characteristic of the material is estimated from the steel ball location and the specimen's behavior. However, the observation of steel ball's behavior might be difficult in the specimen such as ceramics because of scattering the fragments.Neural networks are often used as a method of the image recognition. When the neural network is used for the image recognition, we expect the correct recognition from an imperfect image because of the robustness of the neural network. We have already proposed to apply the multilayer neural network for the recognition of the steel ball in the impact perforation image. We have used the three-layered feed-forward neural network which estimated the steel ball location in the impact perforation image [3]. However, the miss-recognition of the steel ball was often seen because of the influence on the scattered fragments of the specimen.On the other hands, the high-pass filters are often used for edge emphasis in the image processing. Moreover, the high-pass filters have an effect to make the background of the image uniform, since they cut the low frequency.In this study, we introduce the preprocessing of the image with the high-pass filter to the neural network to improve the performance of the recognition of the steel ball. We examine two types of filters using the Hanning window and the Blackman window. We use the impact perforation image of the polymethyl methacrylate (PMMA) specimen which contains a lot of scattered fragments.

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