## 1 Introduction
[12], chlorophyll meters provide a convenient tool for plant health assessment, but cost, availability, and device accuracy are important considerations that may limit their widespread use and accessibility. The proposed low-cost device shows promise as an alternative, but further development and validation are necessary to establish its reliability and suitability in various agricultural contexts. [PERSON] et al. [11] present a promising and accessible approach to estimating chlorophyll content in lettuce leaves using a smartphone-based image acquisition technique. While the method demonstrates significant correlations with chlorophyll content, it is important to consider the limitations and potential variability associated with this approach. Further research and validation are necessary to assess the technique's performance across different plant species and lighting conditions and enhance its accuracy and consistency. [PERSON] et al. [13] present a promising application of machine learning, specifically, the random forest algorithm, for predicting chlorophyll content based on the reflection spectra of plant leaves. The study benefits from a diverse sample set and comparative analysis of different models. However, the limited wavelength range, focus on specific plant species, performance variability, and variations in comparison with other models should be taken into account when interpreting and applying the findings. [PERSON] et al. [14] present a cost-effective solution for predicting chlorophyll content in citrus leaves using smartphone images and predictive models. However, the limited scope, lack of comparison with existing methods, and absence of a detailed evaluation of prediction errors warrant further investigation and validation to assess the method's robustness and generalizability. The presented data from the available literature provide a summary of the advantages and limitations of the measurement methods discussed. These methods offer several benefits, including the ability to collect data in situ (on-site) at a lower cost compared with traditional methods, in real time, without causing harm to the plant, potentially with higher accuracy. They offer advantages such as accessibility, profitability, real-time data acquisition, and non-destructive measurements. However, it is important to acknowledge the limitations associated with these methods. They may exhibit lower precision or accuracy compared with laboratory-based techniques. There could also be limitations in terms of sensitivity or dynamic ranges, which may affect the range of measurable values. Environmental factors, such as changes in lighting conditions or temperature, can influence the measurements obtained. Furthermore, there might be restrictions on the types of pigments that can be effectively measured using these methods. Additionally, the successful utilization of these methods may require specialized training or technical expertise to ensure accurate and reliable results. While these measurement methods offer several advantages over traditional approaches, it is crucial to consider their limitations and make informed decisions based on the specific requirements and constraints of the research or application at hand. The aim of this work is to develop and test a measuring device that can be sufficiently accurately and reliably used in determining the pigment content of plant leaves. This will improve the quality of data collected on plant leaf pigment content, which could have important implications for research in a number of fields, including plant biology, agriculture, and the environment. To achieve the formulated goal, it is necessary to perform the following tasks:  
* Design a new measuring device that can accurately determine the pigment content of plant leaves. * Implement the measuring device and test its performance against existing methods of measuring pigment content in plant leaves. * Evaluate the reliability and accuracy of the measuring device in comparison with existing methods.