## 1 Introduction  
The increase in global population has contributed to the constantly growing demand for high-quality, nutrient-rich leafy greens such as lettuce, basil, and spinach [1]. This growing demand intensifies the need for agricultural practices that align with the United Nations sustainability goals [2], ensuring an adequate supply of high-quality, nutrient-rich leafy greens. This is also driven by consumers with growing awareness of health and sustainability [1]. Advances in agriculture, such as greenhouses, vertical farming, and smart farming, have presented new opportunities in optimizing crop quality and yield. A critical tool for optimization is monitoring and measurements of the chemical compounds in leafy greens, which can be an indicator of nutritional value, marketability, and shelf life. However, traditional methods for assessing chemical compounds in plants, such as destructive sampling and laboratory analysis, are time-consuming, labor-intensive, and often impractical for real-time or large-scale applications.  
Spectral imaging techniques, such as hyperspectral imaging, multispectral imaging, and spectrometry, have emerged as powerful non-destructive tools for plant phenotyping [3, 4]. By analyzing the interaction that occurs between light and plant tissue, these techniques can enable accurate and rapid detection of crucial chemical compounds in plants [3]. By capturing spectral information, those methods can quantify and map physiological parameters of the plants [5]. The non-invasive nature of this approach makes it suitable for monitoring plant health and quality [5, 6].  
The rationale for this systematic review is to summarize recent developments in spectral imaging technologies and their applications in detecting chemical compounds in leafy greens. Although many studies have explored this area individually, there is a need to bring together existing findings on the effectiveness and accuracy of spectral imaging techniques in agricultural settings. Therefore, this paper aims explicitly to address this gap by systematically reviewing recent studies on the non-destructive detection of chemical compounds in leafy greens using spectral imaging techniques.  
This paper was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines to ensure a rigorous and transparent review process. By collecting the results of recent studies, it aims to provide a comprehensive database of the techniques and accuracies achieved in detecting chemical compounds in most common plants.  
The time frame reflects the rapid advancements in sensor technologies, machine learning algorithms, and computation methods. Key chemical compounds of interest include chlorophyll, carotenoids, anthocyanins, nitrogen, and nitrate. In this review, there are also compounds that were not directly searched but additionally added from studies included, such as vitamin C, magnesium, sulfur, phosphorus, water content, calcium, cellulose, sugar, and soluble solid content.  
This paper is structured as follows: Section 2 contains all information about data collection, search methodology for articles, and selection processes. In Section 3, technologies of hyperspectral cameras, multispectral cameras, and spectrometers are discussed. Section 4 is divided into five subsections: Sections 4.1-4.5. All subsections contain information about chemical compounds detected and tables containing data collected. Last, Section 5 talks about findings and potential future work.