Version 1
: Received: 17 April 2024 / Approved: 18 April 2024 / Online: 18 April 2024 (10:32:10 CEST)
How to cite:
Schneider, F.; Swiatek, J.; Jelali, M. Detection of Growth Stages of Chilli Plants in a Hydroponic Grower Using Machine Vision and YOLOv8 Deep Learning Algorithms. Preprints2024, 2024041243. https://doi.org/10.20944/preprints202404.1243.v1
Schneider, F.; Swiatek, J.; Jelali, M. Detection of Growth Stages of Chilli Plants in a Hydroponic Grower Using Machine Vision and YOLOv8 Deep Learning Algorithms. Preprints 2024, 2024041243. https://doi.org/10.20944/preprints202404.1243.v1
Schneider, F.; Swiatek, J.; Jelali, M. Detection of Growth Stages of Chilli Plants in a Hydroponic Grower Using Machine Vision and YOLOv8 Deep Learning Algorithms. Preprints2024, 2024041243. https://doi.org/10.20944/preprints202404.1243.v1
APA Style
Schneider, F., Swiatek, J., & Jelali, M. (2024). Detection of Growth Stages of Chilli Plants in a Hydroponic Grower Using Machine Vision and YOLOv8 Deep Learning Algorithms. Preprints. https://doi.org/10.20944/preprints202404.1243.v1
Chicago/Turabian Style
Schneider, F., Jonas Swiatek and Mohieddine Jelali. 2024 "Detection of Growth Stages of Chilli Plants in a Hydroponic Grower Using Machine Vision and YOLOv8 Deep Learning Algorithms" Preprints. https://doi.org/10.20944/preprints202404.1243.v1
Abstract
Vertical Indoor Farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use Deep Learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s eye and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering and fruiting classes. All trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
