Pepper Detection with YOLO11 and Depth Images
Research on improving pepper detection accuracy by combining RGB images and depth information with computer vision and AI.
Research Theme
Smart Agriculture & Computer Vision
We research and develop technologies that use imaging and AI to understand crops and agricultural work and support production and field operations.
- Computer Vision
- AI
- Sensing
- Smart Agriculture
Projects
Related Projects
Development of AI-Based Sorting Technology for Defective Summer-Autumn Peppers, Including Red-Colored Fruit
A collaborative research project developing technology to sort out defective summer-autumn peppers, including prematurely red-colored fruit, using AI-based image recognition.
Creating Innovative Deep-Learning Data Collection Technology for Crops Using Mixed Reality
Researching methods for collecting deep-learning training data for crops using mixed reality (MR) technology.
Development of a High-Precision Green Onion Peeling and Preparation Machine Using Image Analysis Technology
A research project developing a high-precision green onion peeling and preparation machine using image analysis technology, in which the lab participated as a co-investigator.
Development of a Method for Recognizing Green Onion Branch Positions from Images to Improve Preparation Work Efficiency
A commissioned research project from Oita Prefecture on developing a method to recognize green onion branch positions from images to improve the efficiency of preparation work.
Establishment of drone remote-sensing technology to support efficient large-scale farm management
A collaborative research project using image analysis of drone-captured imagery to support more efficient management of large-scale farms.
Development of labor-saving Satsuma mandarin cultivation using smart technologies and a new planting method
A collaborative research project combining image analysis technology and a new planting method to reduce labor in Satsuma mandarin cultivation.
Development of crop-growth diagnosis technology for open-field vegetables using multicopters
A collaborative research project on using multicopters (drones) to assess the growth conditions of open-field vegetables including white onions through image analysis.
Green Onion Branch Position Detection
Research on real-time green onion branch position detection using computer vision, AI, and edge devices to support preparation work.
Publications
Related Publications
2026
LIGHTWEIGHT YOLOX-BASED GREEN ONION BRANCHING POINT DETECTION FOR AUTOMATED PEELING ON EDGE DEVICE
Takuto Ando, Iori Yamaguchi, Jun Shono, Takahiro Kawabe, Koji Uchida, Kosuke Shigematsu, Yusuke Inoue, "LIGHTWEIGHT YOLOX-BASED GREEN ONION BRANCHING POINT DETECTION FOR AUTOMATED PEELING ON EDGE DEVICE," ICIC Express Letters, Vol.20, No.8, pp.811-819, Aug. 2026.
2026
セマンティックセグメンテーションに基づく小ねぎの太さ分類手法
Hiroto Shirono, Rento Kiyota, Yusuke Inoue, Kosuke Shigematsu, "Classification of Green Onion Thickness Based on Semantic Segmentation," Agricultural Information Research, Vol.35, No.2, pp.40-48, Jul. 2026.
2026
YOLO11 と深度画像を用いたピーマン検出モデルの精度向上
猪俣秀真, 井上優良, "YOLO11 と深度画像を用いたピーマン検出モデルの精度向上", 2026年 電子情報通信学会総合大会, D-12-60, Mar. 2026.
2024
小ねぎ調製位置検出のためのインスタンスセグメンテーション
安藤拓翔, 井上優良, "小ねぎ調製位置検出のためのインスタンスセグメンテーション", 2024年度（第32回）電子情報通信学会九州支部学生会講演会, D-27, Sep. 2024.
2024
MRデバイスを用いた農作業者向けインタフェースの開発
坂本勅乃, 井上優良, "MRデバイスを用いた農作業者向けインタフェースの開発", 2024年度（第32回）電子情報通信学会九州支部学生会講演会, D-53, Sep. 2024.
2024
エッジデバイス上におけるリアルタイム小ねぎ分岐部位置検出
Takuto Ando, Yusuke INOUE, "Real-time Green Onion Branch Position Detection on Edge Devices," Agricultural Information Research, Vol.33, 1, Jul, 2024.
2023
YOLOX を用いた白ねぎの葉の分岐部検出
小野竜也, 井上優良, "YOLOX を用いた白ねぎの葉の分岐部検出", 2023年度（第31回）電子情報通信学会九州支部学生会講演会, D-22, Sep. 2023.
2023
エッジ検出を用いたこねぎ分岐部の検出
安藤拓翔, 井上優良, "エッジ検出を用いたこねぎ分岐部の検出", 情報処理学会第243回 システム・アーキテクチャ研究発表会, Vol.2023-ARC-251, No.4, Jan. 2023.
2022
ほ場画像セグメンテーションのための色空間の検討
廣瀬花菜子, 井上優良, 嶋田浩和, "ほ場画像セグメンテーションのための色空間の検討", 2022年度（第30回）電子情報通信学会九州支部学生会講演会, D-39, Sep. 2022.
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