本研究に, ミヤコワネレ‘高性濃紫色種’の異なった花芽発達段階において, 高温処理 (10時から15時の5時間を, 30～35℃の温度で保った.) を5, 10, 20日行い, 頂生花に発生したアボーション発生率と高温処理期間との関係を検討した.
処理を行った花芽発達段階は次の8段階とした.1) 小花形成前期, 2) 小花形成中期, 3) 花弁形成前期, 4) 花弁形成中期, 5) 花弁形成後期, 6) 花弁完成期, 7) 出らい期, 8) 出らい7～10日後.
ブラインド型発生率は, 小花形成前期から花弁形成中期の20日間処理で高く, その発生率は70%から100%であった.また, 1小花形成前期から花弁形成前期の発達段階では, 5日間処理でも60%から85%の発生がみられた.なお, ブラインド型は花弁完成期以後の段階ではいずれの処理期間でも発生がみられなかった.
管状花ネクロシス型は, 花弁形成後期の10日と20日間処理および花弁完成期の20日間処理で10%発生した.
管状花一部ネクロシス型は, 花弁形成後期から出らい期にかけての発達段階で発生し, とくに花弁完成期の10日および20日間処理で発生率が高かった., なお, これらの処理期間においても, 管状花一部ネクロシス型は出らい7日から10日後の発達段階では発生しなかった.
本研究の遂行にあたり, ご指導とこ校閲を賜った東京農業大学農学部教授樋口春三博士に厚く御礼を申し上げます.また, 論文のとりまとめにあたり, ご助言とこ校閲をいただいた静岡大学農学部教授石田明博士ならびに同助教授糠谷明博士に深謝の意を表します.

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本研究は, ミヤコワスレ‘高性濃紫色種’の種々の花芽発達段階における高温 (10時から15時までの5時間を30～35℃) と遮光 (黒色寒冷しゃ2枚被覆) との組み合せ処理期間がアボーション発生に及ぼす影響を調査した.処理期間は, 高温, 遮光とも0, 5, 10, 15日間の4段階で, 8期の各花芽発達段階にそれぞれ16処理区を設けた.
アボーションは, 小花形成前期から出らい期にかけての発達段階で発生し, 出らい7～10日後の花らいには発生しなかった.アボーション発生率は, 高温と遮光との処理期間が長くなるにつれて高くなった.また, 発生率は花芽発達段階が進むにつれて, 低下する傾向にあった.ブラインド型は, 小花形成前期から花弁形成中期の発達段階で多発し, その発生率は, 高温と遮光の処理期間が長くなるにつれて高くなった.管状花ネクロシス型は, 花弁形成後期と花弁完成期の発達段階で発生し, 発生率は高温と遮光の15日間の処理区で最も高かった.管状花一部ネクロシス型は, 花弁形成後期から出らい期にかけての発達段階で発生し, とくに花弁完成期で発生が多かった.その発生率は, 高温と遮光の処理期間が長くなるにつれて高くなったが, 高温遭遇のない遮光処理ではまったく発生しなかった.
本研究の遂行にあたり, ご指導および論文のこ校閲を賜った東京農業大学農学部教授樋口春三博士に厚く御礼を申し上げます.また, 論文のとりまとめにあたり, ご助言とこ校閲をいただいた静岡大学農学部教授石田明博士, 同助教授糠谷明博士, 同助教授高木敏彦博士ならびに図の作成にあたりご指導をいただいた, 静岡大学教育学部教授望月雄蔵博士に厚く謝意を表します.

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Plant control systems are characterized by complexity and fuzziness. Genetic algorithm is one of the combinational optimization techniques for complex systems, which utilized genetic operations such as crossover and mutation.
The present work is attempted to apply genetic algorithm and artificial neural network to the optimal control problem for intermittent solution supply in deep hydroponic system. The neural network is used for constructing dynamic model of net photosynthetic rate to drainage and supply in the intermittent solution supply, and the genetic algorithm is used for searching optimal value from numerous responses simulated by the model. The optimal control problem in the present study is to decide 4-step combinational times of the drainage and supply (t₁, t₂, t₃ and t₄), which maximize the net photosynthetic rate of the plant.
By applying genetic operations, we could obtain optimal value easily. In this case, the degree of reaching optimal value (evolution speed) is closely related to crossover rate and mutation rate. Higher the both values increased the evolution speed. For example, when the crossover rate (P_c) and mutation rate (P_m) are respectively equal to 0.8 and 0.8, the optimal value can be obtained within 5-generation. However, lowered the both values caused significant delay of evolution speed. In this case, however, the optimal value can be successfully obtained.
Thus, it was found that the genetic algorithm is very powerful tool for finding the optimal value of objective function contains numerous variables. It seemed that the combination of genetic algorithm and neural network allows available control for growth optimization.

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In the intentional production of crops grown in a plant factory, it is necessary to measure the progress of growth of an individual plant. The image processing of plant growth is an effective tool for intact and non-destructive measurement, so as to be able to digitize continuously the images of plant or leaves with the computer system. There is a significant positive correlation between several growth indices obtained by image processing and the top fresh weight of lettuce plants (Lactuca sativa L. non-heading type) grown in a plant factory. The correlation coefficient between the horizontal projected leaf area and the top fresh weight was highest among the image features of lettuce, i.e. 0.947 or higher. During the early growth period from two-to eight-leaf stage, the correlation coefficient between the horizontal projected leaf area and the top fresh weight was higher than that of the whole span of lettuce's life, i.e. 0.95 or higher. The horizontal projected leaf area for several days after sowing made it possible to estimate the top fresh weight. And so, we have developed an automatic growth measurement system by image processing. To measure individual plants continuously and automatically, we have developed the algorithms for image processing and measurement. The algorithms were composed of the labelling for recognizing individual plant, calculating of argument suited to the growth process and automatic calibration for eliminating the error caused by plant age. From these, the top fresh weight of individual plants was obtained continuously by use of a low-cost automatic plant growth measuring system with a personal computer.

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In order to demonstrate the effect of removal of manure on environment in poultry house, the authors performed measurements in an open type poultry house in Ehime Prefecture from December of 1991 to April of 1992.
Cross section and ground plan of the poultry house are shown in Figs. 1 and 2. The numbers in Fig. 2 indicate measurement points. NH³ and CO² gas generation rate from manure with relation of temperature and weight water content are shown in Figs. 3 and 4.
During the experimental period, 21, 500 White Leghorns were raised in the poultry house. Weight water content of the manure on shelf was about 68% and that at 1st floor was about 38%. The authors removed the accumulating manure on shelf using a trial machine for removal of manure on March 15. Temperature, NH³ and CO² gas concentration, humidity and heat flux inside and outside the poultry house were measured.
Figure 5 shows the diurnal changes in temperature and NH³ gas concentration before the removal of the manure. Figure 6 shows the diurnal changes in temperature and NH³ gas concentration after the removal of the manure. Vertical distribution of NH³ gas concentration at point 5 is shown in Fig. 7.
NH³ gas concentration after the removal of the manure was lower than that before the removal of the manure, especially at 2 cm above the manure on shelf. It is considered that NH³ gas generation from the manure on shelf causes higher NH³ gas concentration in the cages and that decrease of about 10 ppm of NH³ gas concentration will be expected by removal of manure on shelf.
Furthermore, horizontal distribution of NH³ gas concentration shown in Fig. 8 was observed under condition of weak breeze. The highest concentration was 93 ppm at point 6, indicating necessity of improvement of environmental control.

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