@article{oai:nodai.repo.nii.ac.jp:00000291,
 author = {佐々木, 豊 and 田島 淳 and Sasaki, Yutaka and Tajima Kiyoshi},
 issue = {2},
 month = {2016-04-20},
 note = {日本の主要作物は米であり,その早期の病害検知は重要といえる。しかしその自動診断において,イネは葉が細いため病斑を検知することは非常に難しい。本研究では,米にとって甚大な被害をもたらすいもち病を対象に,その検知手法を検討した。具体的には,先ず仮想水田空間を構築して画像を取得した。次に認識された背景といもち病を識別するパラメータの自動生成手法を開発した。この手法では形状特徴量を組み合わせる遺伝的プログラミング(GP)を導入し,そのアルゴリズムの改良,性能評価を行った。, The principal crop in Japan is rice and the early detection of disease is very important. However, because the leaves of the rice plant are very thin, it is extremely difficult to detect symptoms in an automatic diagnosis for plant disease. This study examined techniques for detecting rice blast, which causes serious damage to rice. Specifically, a virtual rice field was constructed and images were acquired first. Next an automatic generation technique for parameters that distinguish between recognized background and rice blast was developed. Genetic programming (GP) that combines shape features was introduced into this technique, and improvement and performance evaluation of the algorithm were carried out., E, 4, KJ00004669913, 論文, Article},
 pages = {102--108},
 title = {遺伝的プログラミングを応用したいもち病認識パラメータの自動生成},
 volume = {52},
 year = {},
 yomi = {ササキ, ユタカ and タジマ, キヨシ}
}