A Self-Supervised Learning-Based Intelligent Greenhouse Orchid Growth Inspection System for Precision Agriculture

Abstract

Precision agriculture is a high-level agricultural production process that achieves high quality and high efficiency by combining traditional agricultural technology with the Internet of Things (IoT) technology and artificial intelligence (AI) technology. This method achieves high-quality, high-tech, and high-level agricultural production. Value-added agricultural products are the target. Intelligent greenhouse planting technology facilitates the control and management of the greenhouse environment through intelligent analysis and decision-making while preserving the traditional agricultural cultivation experience and management and expanding the operating area, reducing the burden and expenditure of farm management. In this article, an orchid growth inspection system is proposed for detecting the growth status of orchids in greenhouses. This system can be used to manage environmental factors, flower names, and the growth status of orchids in the greenhouse. In addition, an emerging self-supervised learning method is proposed for training and identifying orchid models to recognize the growth status of orchids. Many orchid species have the same appearance, color, and shape. The difficulty of identifying orchids can hinder their proper management. Therefore, if the name and growth status of the orchid can be established, the managers of the orchid greenhouse can more effectively manage orchid growth. The experimental results show that the orchid recognition accuracy reached 98.6%.