Sensing technology for greenhouse tomato production: A systematic review

Abstract

Greenhouse has become a critical solution to global climate challenges and food security . Tomatoes, one of the most critical greenhouse crops , are among the most popular vegetables worldwide. Advanced sensing technologies play a key role in monitoring plant growth and supporting management decisions to improve the efficiency and sustainability of greenhouse tomato production. This review aims to provide theoretical insights and technical guidance for monitoring and managing greenhouse tomato production by systematically examining sensing technologies in this field. The review covers four key areas: (1) a comprehensive analysis of critical environmental factors influencing tomato growth, such as temperature, humidity, light intensity, and CO 2 concentration; (2) an exploration of high-throughput, non-destructive sensing technologies, including chlorophyll fluorescence imaging, infrared CO 2 sensing, and multispectral imaging; (3) an investigation of the algorithms based on multi-sensor data fusion and data-driven diagnostic systems for disease detection and growth forecasting; and (4) a discussion on potential research topics in the future to address the limitations of the existing methods. Key findings show that deep learning-based multimodal data fusion models significantly improve accuracy in disease detection, facilitating tomato growth monitoring. The integration of the Internet of Things (IoT) and wireless sensor networks (WSN) forms a solid foundation for precise irrigation, fertilization, and automated environmental management. Future research could focus on overcoming challenges such as the real-time fusion of multi-source heterogeneous data, improving the generalization and interpretability of intelligent diagnostic models, and scaling innovative agricultural technologies for global implementation, thereby promoting the sustainable development of greenhouse horticulture .

Topics & Concepts