Detection of Growth Stages of Chilli Plants in a Hydroponic Grower Using Machine Vision and YOLOv8 Deep Learning Algorithms

Florian Schneider, Jonas Swiatek and Mohieddine Jelali ()
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Florian Schneider: Cologne Laboratory of Artificial Intelligence and Smart Automation (CAISA), Institute of Product Development and Engineering Design (IPK), Technische Hochschule Köln—University of Applied Sciences, 50679 Cologne, Germany
Jonas Swiatek: Cologne Laboratory of Artificial Intelligence and Smart Automation (CAISA), Institute of Product Development and Engineering Design (IPK), Technische Hochschule Köln—University of Applied Sciences, 50679 Cologne, Germany
Mohieddine Jelali: Cologne Laboratory of Artificial Intelligence and Smart Automation (CAISA), Institute of Product Development and Engineering Design (IPK), Technische Hochschule Köln—University of Applied Sciences, 50679 Cologne, Germany

Sustainability, 2024, vol. 16, issue 15, 1-29

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 view 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 the 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.

Keywords: artificial intelligence; deep learning; YOLOv8; machine vision; image processing; indoor farming; hydroponics; chilli plants; Capsicum annuum (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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