Automatic Detection of Lettuce Tipburn in Plant Factory with Artificial Light Using Convolutional Neural Networks

カテゴリ: 部門大会

論文No: SS3-1

グループ名: 【C】2022年電気学会電子・情報・システム部門大会

発行日: 2022/08/24

タイトル(英語): Automatic Detection of Lettuce Tipburn in Plant Factory with Artificial Light Using Convolutional Neural Networks

著者名: 中山 海斗（千葉大学）,中間 公啓（千葉大学）,小圷 成一（千葉大学）

著者名(英語): Kaito Nakayama (Chiba University),Kimihiro Nakama (Chiba University),Seiichi Koakutsu (Chiba University)

キーワード: 植物工場|チップバーン|機械学習|畳み込みニューラルネットワーク|ラズベリーパイグーグルドライブ|Plant Factory|Tipburn|Machine Learning|Convolutional Neural Network|Raspberry PiGoogle Drive

要約(日本語): Plant factory with artificial light (PFAL) is attracting worldwide attention as a technology for stably producing crops. One of the major problems of PFAL is tipburn which is a physiological disorder of crops. When tipburn occurs, leaf tips discolor blackly and the commercial value as vegetables is damaged. Tipburn is able to detect using convolutional neural networks (CNN). In this article, we aim to construct an observation system of lettuce grown in PFAL using a small computer such as Raspberry Pi and a small camera dedicated to Raspberry Pi. In the proposed system, the images of lettuce are captured periodically and saved in Google Drive. The proposed system makes it possible to detect tipburn using CNN at any time, as long as a network environment is available.