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Photosynthetically active radiation (PAR) is crucial for plant growth, influencing photosynthesis efficiency and crop yield. The increasing adoption of controlled-environment agriculture (CEA) necessitates precise PAR monitoring. The high cost of commercial PAR sensors, however, limits their accessibility and widespread use, creating a growing need for a low-cost alternative capable of reliable deployment in diverse agricultural environments. Building on recent advancements in PAR sensing using multi-channel spectral sensors such as the AS7341 and AS7265, this study develops the electronics for an AS7341-based, open-source, cost-effective (~USD 50) PAR sensor validated across a broad PPFD range and conditions, ensuring reliability and ease of replication. It uses a relatively simple multi-linear regression that offers real-time applications without energy intensive machine learning. The developed sensor is calibrated against the industry-standard Apogee SQ-500SS PAR sensor in four distinct farming environments: (i) horizontal grow lights, (ii) vertical agrotunnel lighting, (iii) agrivoltaics, and (iv) in greenhouses. A mean error ranging from 1 to 5% indicates its suitability for controlled environment farming and continuous data logging. The open-source hardware design and systematic installation guidelines enable users to replicate, calibrate, and integrate the sensor with minimal background in electronics and optics.

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1. Introduction

Photosynthetically active radiation (PAR) refers to the spectral range of solar radiation between 400 and 700 nanometers (nm) that is utilized by plants for photosynthesis [1]. Unlike general sunlight, which encompasses a broader range of wavelengths, PAR specifically denotes the portion of the electromagnetic spectrum that excites chlorophyll molecules, driving the photochemical reactions essential for the plant [2]. The rate of photosynthesis and the production of starch and other carbohydrates are directly correlated with the quantity of incident PAR [3,4]. This is quantified in terms of photosynthetic photon flux density (PPFD), which measures the number of photons (µmol·m−2·s−1) reaching a given surface per unit time [5].

The increasing adoption of controlled-environment agriculture (CEA), including greenhouse cultivation, hydroponics, vertical farming, and agrivoltaics, has necessitated precise PAR monitoring to optimize plant growth and productivity [6]. Agrivoltaics systems, which integrate photovoltaic (PV) modules with agricultural land, introduce an additional layer of complexity due to dynamic shading, variable light transmission [7,8], and potential spectrum modification by partially transparent solar panels [9]. An agrivoltaic system typically consists of three primary components: (i) PV modules, which are often mounted above agricultural land (outdoors) or integrated into greenhouse structures (indoors) to generate electricity; (ii) agricultural crops, which continue to grow beneath or around the PV installations; and (iii) support structures and electrical infrastructure, including mounting racks, inverters, and wiring for power conversion and distribution. Furthermore, outdoor agrivoltaic systems can be broadly categorized into three types: (1) uniformly illuminated semi-transparent thin-film based PV modules, (2) non-uniform semi-transparent crystalline silicon (c-Si) modules, and (3) opaque PV modules mounted on open-air racking structures [10]. The spatial configuration and optical properties of PV modules—such as their height, tilt angle, row spacing, and transparency—directly influence the quantity and quality of light that reaches the crops below. This creates a unique light environment that varies dynamically throughout the day and across seasons. The interaction between plant canopy architecture, PV module configurations, and light availability requires robust, real-time PAR measurement to ensure optimal plant development while maximizing energy yield [11]. PAR measurements are thus useful for a wide range of farming techniques summarized in Figure 1.

Moreover, in indoor farming systems that rely on artificial lighting such as light-emitting diode (LED) grow lights, the spectral composition and intensity must be carefully controlled to match plant-specific PPFD requirements. Figure 2 illustrates the PPFD ranges and photoperiod optimal for various crops grown under CEA conditions. The selection of appropriate crop varieties [12], adjustment of light spectra, and development of smart, adaptive lighting environments depend on precise PAR quantification [13].

Commercial PAR sensors are typically expensive, proprietary, and often lack seamless compatibility with open-source data monitoring and logging systems (see Table 1). Most commercially available models are full-spectrum quantum PAR sensors, designed to provide high-precision measurements with an accuracy of within 5%. This level of accuracy comes at a significant cost, with standalone sensors priced above CAD 600 and complete monitoring systems reaching around CAD 1000. The high cost of these sensors poses a challenge for researchers and agricultural practitioners seeking cost-effective solutions for large-scale deployment. As the demand for precision agriculture and controlled-environment farming increases, the development of affordable, reliable, and easily integrable PAR measurement systems are essential to enable broader adoption and optimization of sustainable agricultural practices.

To develop low-cost PAR sensors, alternatives to quantum sensors and expensive spectrometers have been explored. The availability of cost-effective microcontrollers, amplifiers, and IoT devices has facilitated the development of PAR sensors using silicon (Si) photodiodes such as the TSL250, VTB8440BH [23], BPW34 [24] and gallium arsenide (GaAs)-based photodiodes, such as the G2711-01 and G1118 [25], have been widely used in combination with optical filters that selectively pass 400–700 nm wavelengths to enhance PAR measurement accuracy. Relying on a single photodiode for PPFD estimation under varying lighting conditions poses challenges, however. Furthermore, system performance is heavily dependent on the quality of the optical filter employed with these types of PAR sensors, which further increases the overall cost.

To address these limitations, multi-channel light sensors have been introduced for PPFD estimation, leveraging multiple spectral channels to improve accuracy while eliminating the need for external optical filters. A commonly used sensor in this category is the TCS34715FN [26,27], a four-channel RGBW (red, green, blue, and white) sensor that enables PPFD prediction across different lighting conditions at a lower cost and with improved reliability.

With advances in optical sensing technology, new multi-channel spectral sensors have emerged, significantly enhancing PAR measurement capabilities. Sensors such as the AS7341 (11-channel), featuring 4 × 4 photodiode arrays, cover a broad spectral range from 350 nm to 1000 nm [28], while the AS7265 (18-channel) consists of three sensor chips (AS72651, AS72652, and AS72653) that collectively provide 18 spectral channels, spanning from 410 nm to 940 nm [29]. These sensors have been integrated into recent research efforts, employing advanced calibration techniques such as vector quantization [30], machine learning algorithms [31,32], and multilinear regression for PPFD estimation [31,33,34,35]. Comparative analyses of these approaches, including their accuracy, cost, and calibration complexity, are summarized in Table 2.

While machine learning-based models offer high accuracy, multilinear regression provides a more practical solution for real-time monitoring applications due to its ease of calibration and implementation [32]. Therefore, in this study, a multilinear regression-based approach is adopted to develop a cost-effective and reliable PAR sensor for real-time agricultural monitoring.

While previous studies using multi-channel optical sensors have explored cost-effective techniques for developing lab-scale PAR sensors, these methods often involve complex computational models or extensive calibration procedures, limiting their practicality for widespread adoption. Furthermore, the reliability of many of these sensors remains limited, as their accuracy is often validated using small datasets and within a restricted range of PPFD. Additionally, only a few of these sensors have been developed as fully integrated devices with standardized guidelines for replication, calibration, and deployment. The lack of well-documented methodologies and open-source implementation frameworks [36,37] further hinders their widespread adoption and practical usability in real-world agricultural applications. To address these limitations, it is crucial to develop an open-source PAR sensor that is not only easy to construct, but also highly reliable, with validation across the full PPFD range (0–2000 µmol/m2/s). Additionally, an integrated data logging system should be capable of continuously recording PAR values over extended periods to support long-term monitoring and analysis.

Despite growing interest in low-cost PAR sensors, the current body of literature lacks a fully open-source, cost-effective, and well-documented solution that is validated across multiple lighting environments and over a wide PPFD range (0–2000 µmol·m−2·s−1). Existing sensors often involve trade-offs in cost, calibration complexity, or deployment feasibility. To address this gap, this study develops a plug-and-play PAR sensor that (i) costs much less in parts, (ii) includes standardized open-source hardware, firmware, and calibration protocols, (iii) is validated against a commercial quantum sensor across real-world conditions, and (iv) includes an integrated data-logging system for continuous monitoring.

Hence, an open-source PAR sensor system using AS7341 is designed, developed, and rigorously tested under four distinct lighting environments: a greenhouse, with grow lights (Mars Hydro TS-1000), in an agrotunnel with high efficiency LEDs (Better Grow Lights), and outdoor agrivoltaics systems. The sensor is calibrated and validated using a commercial Apogee SQ-500SS Quantum PAR sensor. A comparative analysis is conducted to evaluate sensor performance, highlighting key trade-offs between cost, accuracy, and application feasibility.

The remainder of this paper is structured as follows: Section 2 outlines the materials and methods used for sensor development, calibration, and testing under multiple lighting environments. Section 3 presents the results of the sensor’s performance compared to a commercial PAR sensor, including regression analysis and validation metrics. Section 4 discusses the implications of the findings, limitations, and potential improvements for broader deployment. Finally, Section 5 concludes the study with the key takeaways and directions for future research.

2. Materials and Methods

2.1. AS7341 Sensor Description and Parameters Extraction

The AS7341 sensor [28] is an 11-channel optical sensor with a measuring light intensity of eight optical channels within visible range (415 nm; 445 nm; 480 nm; 515 nm; 555 nm; 590 nm; 630 nm; and 680 nm, which is the PAR range as well) and three extra channels, namely one near-infrared (NIR) (910 nm), one for white light measurement, and one for flicker. The sensor operates around 1.8V and it can communicate with any microcontroller using I2C protocol, but the I2C voltage level is limited to 1.7–1.9 V, so a level shifter is required between the I2C of AS7341 and the microcontroller (3.3V for ESP32). To utilize the AS7341 sensor for PAR estimation, raw sensor values from eight optical channels with-in the 415–685 nm range will be monitored. The sensor is set to operate with a gain setting of 1 and a total integration time of 100 ms, achieved using ATIME = 35 and ASTEP = 999. The spectral response of the sensor under a grow light is illustrated in Figure 3b, while Figure 3a presents a re-constructed visualization of the spectral distribution of the grow light source (Mars Hydro TS-1000) [38].

2.2. Features and Components of the Sensor

The ESP32-based PAR sensor integrates the AS7341 optical sensor for accurate PAR measurement across various agricultural environments. It employs I2C communication for spectral data acquisition and an onboard multi-linear regression (MLR) model for real-time PAR estimation. The system supports SPI-based SD card logging for long-term data storage and features a web-based dashboard for remote monitoring via Wi-Fi. Sensor data are collected and updated every minute by default, with logging intervals configurable according to user preference and requirements. At this logging frequency, a 16 GB SD card can store up to 215 years of PAR data. The web server continuously handles client requests and serves the latest sensor readings, refreshing the dashboard approximately once per second. For power efficiency, the sensor operates on a rechargeable battery with optimized consumption in data logging mode. The electrical design of the PAR sensor is shown in Figure 4a for the ESP32 data logger and (b) for the AS7341.

The custom-designed sensor PCB integrates an ESP32-based data logger on one side and an AS7341 spectral sensor module on the other. The ESP32 [39] data logger includes essential circuit components such as a lithium battery charging module (supporting a single-cell 3.7 V battery), a MAX17048 fuel gauge IC for real-time battery voltage monitoring and state-of-charge (SOC) estimation, a microSD card slot for data storage, and a USB-to-serial converter for boot loading shown in Figure 5a,b. The AS7341 sensor module shown in Figure 5c,d is equipped with a dual-voltage regulator (3.3 V and 1.8 V), an I2C level shifter, and the AS7341 IC for spectral data acquisition. A detailed bill of materials is available in the Appendix A where (Table A1) lists all required components and Table A2 provides the PCB Gerber files and open-source design files created using KiCad (V8.1) [40]. Additionally, 3-D-printable enclosure STL files are available in the Open Science Framework (OSF) repository [41]. These files are all open-source and licensed under GNU General Public License (GPL) 3.0 [42], and the hardware is released under CERN OHLv2S [43]. The printing parameters are summarized in Table A3 and can be printed on any RepRap class [44,45] fused filament fabrication-based 3-D printer [46]. Commercial filament was used here; however, costs could be further reduced with distributed recycling and additive manufacturing (DRAM)-based feedstock [47,48,49].

2.3. Assembly of PAR Sensor

The assembly process of the device is shown in Figure 5e. The final assembled device and its feature are shown in Figure 6. The sensor’s front case features an opening to allow light to reach the AS7341, covered with a circular acrylic sheet to permit full-spectrum transmission while protecting against dust and water. For direct sunlight deployment where light intensity exceeds 1000 µmol/m2/s, a diffuser is recommended instead of acrylic to prevent sensor saturation. The back case houses a battery compartment with a secure battery holder and a power switch for on/off operation. The sensor also includes an SD card slot, a USB Type-C port for boot loading and charging, and an I2C port for display connectivity or calibration with the SQ-500SS reference sensor.

2.4. Calculation of PAR Using Multilinear Regression

The AS7341 optical sensor comprises 11 spectral channels, 8 of which fall within the visible light spectrum (415–685 nm), coinciding with the PAR range. The raw sensor data from these 8 channels (S1 to S8) are recorded continuously under a predefined gain setting (G = 1) and a fixed integration time of 100 ms. To estimate the PAR value, an MLR model is employed, which establishes a linear relationship between the spectral sensor readings and the actual PAR values obtained from a reference Apogee SQ-500SS sensor [50]. In the MLR model, the predicted PAR value y^ is expressed as [51,52]

(1)y^=b0+∑i=1nbixi

(2)y^=b0+b1x1+b2x2+b3x3+…+b8x8,

where

y^ is the estimated PAR value;

x1, x2, …, x8 represent the recorded raw sensor values from channels within the PAR range;

b0 is the intercept term;

b1, b2, …, b8 are the regression coefficients corresponding to each spectral channel.

The regression coefficients (bi) are computed using the least squares method, which minimizes the sum of squared errors (SSE) between the predicted y^ and actual PAR values (y) obtained from the reference sensor. The regression coefficients and model evaluation metrics can be easily computed using tools like a spreadsheet program in Libre Office 25.2.3 [53], Python 3.13.0 (NumPy [54], SciPy [55]), or MATLAB R2024b [56]. In this research, the raw data are stored in the SD card in a .txt file and later for calibration they will be analyzed using excel where the regression tool is used to find the co-efficient. In Excel, the ToolPak add-in allows users to perform multiple regression analysis (Uses the worksheet function LINEST) without requiring programming expertise [57].

2.5. Modes of Operation and Corresponding Core and Setup Instruction

2.5.1. Calibration Mode

The calibration process involves simultaneously collecting spectral data from the AS7341 sensor and reference PAR measurements from the Apogee SQ-500SS sensor under varying lighting conditions. To achieve this, the developed device incorporates an I2C communication port, which serves dual purposes. In deployment mode, this port is used to connect an OLED display for real-time monitoring. In calibration mode, however, the same I2C port is repurposed to interface with the SQ-500SS sensor, enabling simultaneous data acquisition which is shown in Figure 7a. To accurately measure the low voltage output (0–40 mV) of the SQ-500SS sensor, an ADS1115 16-bit analog-to-digital converter (ADC) is integrated into the system. This high-resolution ADC, which operates via I2C protocol, ensures precise voltage measurements, allowing for reliable sensor data logging through the device’s I2C interface.

The calibration and deployment procedures are used across different farming environments, including (i) horizontal grow lights, (ii) vertical Better Grow Lights [58] in an agrotunnel for CEA [59], (iii) agrivoltaics greenhouses [60], and outdoor crop-based agrivoltaics systems [61], are shown in Figure 7b–e. The collected dataset from these calibration experiments is subsequently used to train the MLR model, where the optimal regression coefficients are determined through statistical analysis. This process enhances the sensor’s ability to predict PAR values with high accuracy. By leveraging an open-source hardware platform and a systematic calibration methodology, this approach ensures easy replication and integration, even for users with minimal expertise in electronics and optical sensing.

2.5.2. Deployment of Sensor

Once the MLR model is trained and the regression coefficients are determined, the derived equation can be integrated into the ESP32 firmware to enable real-time estimation of PAR values from AS7341 spectral readings. The ESP32 continuously acquires raw data from the sensor, applies the regression model, and stores the computed PAR values along with spectral readings onto an SD card for offline analysis in .txt file.

For real-time monitoring of PAR and spectral data, a web-based dashboard can be integrated into the ESP32 firmware. When the Web dashboard feature is enabled (Webdashboard = 1), the ESP32 connects to a designated Wi-Fi network. A built-in HTTP server runs on the ESP32, providing a real-time dashboard that displays PAR and spectral data, which can be accessed from any device on the same network by entering the ESP32’s assigned local IP address in a web browser. The dashboard is shown in Figure 7f. This functionality enables wireless monitoring of environmental conditions, making it particularly useful for applications such as precision agriculture and controlled-environment farming. Continuous Wi-Fi transmission in this mode, however, increases power consumption, which may result in faster battery depletion, making it less suitable for long-term field deployments without an external power source.

3. Results

3.1. Calibration and Results with Grow Lights and Agrotunnel

Both sensors were positioned under the grow light, as illustrated in Figure 7b, and placed vertically in front of vertical farming wall in an agrotunnel as illustrated in Figure 7c. The PAR values were recorded from both the Apogee SQ-500SS and the AS7341 sensors over a period of 84 min across various PAR levels, which were adjusted using the grow light’s intensity control knob and for 158 min in the agrotunnel. Following data collection, a multilinear regression (MLR) model was applied to establish a calibration relationship between the sensors. The regression analysis demonstrated excellent performance, with both the correlation coefficient (R) and the coefficient of determination (R2) approaching 1, indicating a strong linear relationship. The calibration results are shown in Table 3.

To further validate sensor performance, the derived MLR coefficients were used to predict PAR values for an additional 75 min test under the same grow light conditions. The results, presented in Figure 8a,b, confirm that the PAR values predicted by the AS7341 sensor closely align with the actual measurements from the Apogee quantum sensor. The mean error between the two sensors was found to be less than 1%, demonstrating the accuracy and reliability of the developed calibration model under grow light exhibiting a consistent spectral distribution at different intensity levels, as shown in Figure 8a. In the agrotunnel, which uses better grow light (360A), the error found is around 1.11%.

3.2. Calibration and Results in Greenhouse

For outdoor calibration, both sensors were deployed in a greenhouse and an agrivoltaics site, as illustrated in Figure 7d,e. PAR values were recorded simultaneously using the Apogee SQ-500SS and the AS7341 sensor over a continuous period of 1390 min across both locations. Following data acquisition, a multilinear regression (MLR) model was applied to establish a calibration relationship between the AS7341 sensor outputs and reference measurements. The corresponding MLR coefficients and performance parameters are presented in Table 4. To further validate the sensor’s performance, the derived MLR coefficients were used to predict PAR values. The comparison results for the greenhouse and agrivoltaics site are shown in Figure 9a–f, respectively. The mean absolute error between the two sensors was found to be within the range of 2–5%.

3.3. Battery Charging Duration and Impact of WiFi Dashboard on Backup Duration

The performance of 1300 mAh battery backup for the PAR sensor is illustrated in Figure 10. The battery management IC, MP73831, charges the battery with a maximum current of 500 mA, enabling a full charge within approximately 150 min, as shown in Figure 10a. The sensor’s battery performance was evaluated under two scenarios: with the Wi-Fi-based web dashboard enabled and disabled. During both test conditions, the sensor recorded PAR values at one-minute intervals and logged the data to an SD card. Figure 10b,c indicate that the sensor operated for approximately 20 h without the web dashboard, which is 5 h longer than the 15 h runtime observed when the dashboard was active. Battery life can be further extended by reducing the data logging frequency and utilizing the ESP32’s internal RTC to place the system in deep sleep mode between logging intervals. These optimizations can be implemented through the device firmware.

4. Discussion

This article presents the development of a low-cost, handheld PAR measurement device featuring a web-based dashboard, SD card data logging, calibration against an analog quantum sensor, and communication capability with smart greenhouse lighting control systems to enable optimized and cost-effective lighting management. The sensor supports continuous monitoring in outdoor environments and records PAR values at user-defined intervals. The total cost of the device is approximately one-tenth that of commercially available PAR sensors, while offering additional functionalities not typically found in commercial quantum sensors. These results are thus in line with other applications of open hardware that are economically beneficial [62,63].

Compared to previously published solutions summarized in Table 5, where the costs were estimated from the bill of materials, this device offers a compact, low-cost, and open-source alternative that integrates all essential features while remaining accessible to users with limited expertise in optics or electronics. The complete hardware and firmware are available in the Open Science Framework (OSF) repository [41], enabling further customization and seamless integration into existing smart greenhouse or horticultural systems. Other open hardware is already available for farms [64,65], which is particularly mature for farm robotics [66,67].

During validation, the sensor demonstrated a mean error of 2–5% under outdoor lighting conditions, and an even lower error—approximately 1%—under indoor artificial lighting. This error, however, is in addition to the intrinsic error of the reference quantum sensor. As such, while the device may not be suitable for highly precision-dependent applications, it is well-suited for use cases such as smart greenhouse lighting control and continuous, low-cost PAR monitoring in agrivoltaics environments.

Beyond PPFD monitoring, the sensor also enables real-time assessment of spectral intensity distribution. This functionality is particularly valuable in controlled environment agriculture, where different crops respond to specific wavelengths at different times in the lifecycle. The sensor can help detect spectral shifts caused by, for example, dynamic greenhouse glazing or photovoltaic panels (e.g., trackers) and support adaptive lighting strategies to maintain optimal growing conditions. Therefore, in integrated agrivoltaic systems, this PAR sensor can play a critical role in optimizing crop yield beneath solar installations.

Looking ahead, the convergence of several technological trends promises to further enhance PAR-and-spectral monitoring in agriculture. First, embedding self-powered sensors—harvesting their own energy from small PV strips—will reduce wiring complexity and extend field deployability. Second, integrating optical sensors directly into semi-transparent PV laminates could yield co-located light measurements, unlocking closed-loop control of both crop lighting and tracker positioning to maximize agrivoltaic system performance. Moreover, fully integrated environmental sensing platforms—combining PAR with temperature, humidity, CO2, and soil-moisture measurements on a single board—will enable holistic crop-health monitoring and more precise environmental models. Together, these advances will push optical sensing from single-point, lab-bench prototypes toward scalable, self-calibrating networks.

5. Conclusions

In recent years, research on PAR sensors has gained significant momentum, particularly with the advent of low-cost, multi-channel light sensors becoming commercially available. Various methodologies have been proposed for PAR estimation, ranging from advanced artificial intelligence and machine learning models to simpler approaches like linear regression. Among these, the use of multi-channel sensors, such as the AS7341, has demonstrated strong potential to serve as a cost-effective alternative to traditional quantum PAR sensors. There remained a gap in the availability of a comprehensive, easy-to-calibrate, and ready-to-deploy device that combines hardware, firmware, and a practical calibration approach. This study addresses that gap by introducing a compact, open-source PAR sensor system that not only rivals the performance of high-cost commercial sensors and dedicated data loggers but does so at a significantly reduced cost (~CAD 70 or USD 50). This represents a 95% reduction in cost for an equivalent commercial scientific PAR system (not the sensor alone). Thus, users in many jurisdictions can have a PAR system for less than the cost of sales taxes on commercial systems, which makes them much more accessible in low-resource settings. This makes the device an attractive solution for widespread adoption in smart lighting and spectral control applications within agriculture and horticulture. Validation results show a mean error of 2–5% under outdoor lighting and approximately 1% under indoor artificial lighting. With a battery backup of 15–20 h per charge, the device supports remote, untethered deployment. Local SD card-based logging enables its use in locations without Wi-Fi connectivity, while the inclusion of I2C and USB-C interfaces ensures seamless integration with existing smart farming and environmental control systems.

While the system shows robust performance across multiple lighting environments, certain limitations should be acknowledged. Sensor accuracy and stability may be affected under extreme environmental conditions, such as high humidity, rapid temperature fluctuations, or prolonged exposure to direct sunlight without adequate housing. Future improvements could involve the integration of environmental shielding, adaptive calibration algorithms, and wireless connectivity modules to enhance robustness, scalability, and ease of deployment in diverse agricultural scenarios. These enhancements would further increase the sensor’s utility for long-term, autonomous field applications, supporting precision agriculture and climate-resilient farming practices.

Author Contributions

Conceptualization, M.M.R. and J.M.P.; methodology, M.M.R., U.J. and J.M.P.; software, M.M.R.; validation, M.M.R. and U.J.; formal analysis, M.M.R., U.J. and J.M.P.; investigation, M.M.R. and U.J.; resources, J.M.P.; data curation, M.M.R., U.J. and J.M.P.; writing—original draft preparation, M.M.R., U.J. and J.M.P.; writing—review and editing, M.M.R., U.J. and J.M.P.; visualization, M.M.R.; supervision, J.M.P.; funding acquisition, J.M.P. All authors have read and agreed to the published version of the manuscript.

 Data Availability Statement

All source code for this project is available at https://osf.io/vxarp/ (accessed on 29 May 2025).

 Conflicts of Interest

The authors declare no conflicts of interest.

 Abbreviations

The following abbreviations are used in this manuscript:

PARPhotosynthetically active radiation;
PPFDPhotosynthetic Photon Flux Density;
MLRMultiple linear regression;
I2CInter Integrated Circuit.

Footnotes

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Figures and Tables

Figure 1 Application of PAR sensor.

Figure 2 Typical PPFD range and photoperiod requirements for specific types of crops [14,15,16,17].

Figure 3 (a) The nominal spectral intensity plot of grow light Mars Hydro TS-1000 (recreated from TS-1000 data sheet) and (b) measured spectral light distribution of LED grow light using AS7341 sensor.

Figure 4 Electrical design of the PAR sensor (a) ESP32 data logger, (b) AS7341 diagram.

Figure 5 (a–d) PCB layout and 3D visualization of the PCB, (e) encloser design and assembly, and (f) an assembled 3D model of the sensor.

Figure 6 Hardware assembly and calibration, (a) interior hardware, (b) assembled sensor with display, and (c) sensor overview.

Figure 7 Calibration process and data collection, (a) calibration setup, (b) calibration under grow light, (c) deployment in agrotunnel, (d) deployment in agrivoltaics, (e) deployment in greenhouse, and (f) web dashboard.

Figure 8 Validation results of the calibrated sensor under grow light and agrotunnel conditions: (a) Spectral reading range measured by the AS7341 under grow light, (b) comparison of predicted PAR values and actual PAR readings under grow light, (c) correlation and mean error analysis between predicted and actual PAR values under grow light, (d) recreated figure of spectral distribution of the Better grow light (360A) used in the agrotunnel, (e) comparison of predicted and actual PAR values in the agrotunnel, (f) spectral reading range measured by the AS7341 in the agrotunnel, and (g) correlation and mean error analysis between predicted and actual PAR values in the agrotunnel.

Figure 9 Validation results of the calibrated sensor in a greenhouse and an agrivoltaics site: (a) Spectral reading range measured by the AS7341 in the greenhouse, (b) comparison of predicted PAR values and actual PAR readings in the greenhouse, (c) correlation and mean error analysis between predicted and actual PAR values in the greenhouse, (d) spectral reading range measured by the AS7341 in the agrivoltaics site, (e) comparison of predicted and actual PAR values in the agrivoltaics site, and (f) correlation and mean error analysis between the predicted and actual PAR values in the agrivoltaics site.

Figure 10 Battery backup: (a) Complete charge cycle (1300 Ah, 4.2 V Li-ion battery), (b) complete discharge cycle of battery with Wi-Fi dashboard on, and (c) complete discharge cycle without a Wi-Fi dashboard.

A cost comparison between some commercial PAR sensors and their features and level of accuracy.

Manufacturer Model Cost (in CAD) Spectral Range PAR Rangeµmol m−2 s−1 Sensitivity Calibration Uncertainty Reference
Only Sensor Including Monitoring Device
Apogee MQ-500 663 900 389 to 692 nm 0 to 4000 0.01 mV per μmol s−1 m−2 ±5% [18]
MQ-510 - 917 389 to 692 nm 0 to 4000 ±5% [19]
SQ-520 769 - 389 to 692 nm 0 to 4000 ±5% [20]
LI-COR LI-190R 673 - 400–700 0 to 10,000 5 μA to 10 μAper 1000 μmol/s/m2 ±5% [21]
Seeed studio S-PAR-02 336 - 400–700 0–2500 1 mV per μmol/s/m2 N/A [22]

A comparison between recently developed multi-channel spectral sensor-based PAR sensors in different literature, methods, complexity of their implementation and accuracy, and cost.

Calculation Method Measurement Environment Sensor/Device Used Calibrated with Microcontroller Used Spectral Range Cost (If Mentioned) Data Acquisition Performance Ref
Multilinear regression Indoor smart hydroponic system AS7265x Apogee SQ-520 Quantum Sensor Arduino UNO, Raspberry Pi 410–940 nm Not mentioned Data logging InfluxDB server and Raspberry Pi Correlation factor R2 = 88.7% for ambient light and 99.8% under LED. [33]
Multiple linear regression Outdoor PAR measurement AS-7341 LI-190 with Li-250A light LoRa-WAN 360 nm to 760 nm Not mentioned Wireless R2 of 0.991 obtained. [34]
Multi-linearregression Greenhouse and field monitoring AS-7341 SS-110 spectroradiometer Raspberry Pi 3 B+ 400–700 nm Not mentioned Google cloud storage PPFD is tracked with 0.3% error. [31]
Machine learning method (Decision tree and Random Forestmodels) Greenhouse and field monitoring AS-7341 SS-110 spectroradiometer Raspberry Pi 3 B+ 400–700 nm Not mentioned Google cloud storage Mean absolute percentage errors (MAPEs)of 0.01–0.88% [32]
Vector quantization Indoor controlled lighting system and outdoor AS7265x Black comet spectroradiometer Windows 10 laptop with an i7 processor 410–940 nm Not mentioned Serial data transmission to laptop A 12.51% average error wasobtained. [30]
Linear regression Indoor greenhouse setup AS7341 Solar Electric Quantum Meter #3415FSE ESP32 S2 TFT Feather 400–700 nm USD 51 N/A (LCD display) [35]

Multiple linear regression analysis results and calibrated co-efficient.

Regression Statistics Multiple Linear Regression Calibration Co-Efficient
Coefficients Standard Error t Stat p-Value
Multiple R 0.999891 Intercept (b0) −1.83008 0.898409 −2.03702 0.042778
R Square (R2) 0.999782 415 nm (b1) −0.10893 0.185726 −0.58649 0.558117
Adjusted R Square 0.999774 445 nm (b2) −0.19323 0.152327 −1.26855 0.205867
Standard Error 1.97794 480 nm (b3) 0.149401 0.099145 1.506898 0.133191
Observations 242 515 nm (b4) 0.234282 0.11797 1.985939 0.048212
555 nm (b5) 0.019283 0.084076 0.229355 0.818794
590 nm (b6) −0.1623 0.058166 −2.79027 0.005702
630 nm (b7) 0.133297 0.033919 3.92987 0.000112
690 nm (b8) 0.087622 0.029122 3.00877 0.002911

Table of correction factors and regression factors and Linear regression analysis for outdoor lighting.

Regression Statistics Multiple Linear Regression Calibration Co-Efficient
Coefficients Standard Error t Stat p-Value
Multiple R 0.99659 Intercept (b0) −1.9196374 0.36951 −5.19507 0.00000
R Square (R2) 0.99319 415 nm (b1) 4.7280568 0.08699 54.35164 0.00000
Adjusted R Square 0.99315 445 nm (b2) −0.6033910 0.13967 −4.32009 0.00002
Standard Error 8.88614 480 nm (b3) −1.5187001 0.10148 −14.96531 0.00000
Observations 1390 515 nm (b4) 0.4630669 0.13534 3.42156 0.00064
555 nm (b5) 0.6610031 0.09463 6.98546 0.00000
590 nm (b6) −1.6748574 0.07769 −21.55799 0.00000
630 nm (b7) 0.7903176 0.05275 14.98284 0.00000
690 nm (b8) −0.2266746 0.04864 −4.65997 0.00000

A comparison can be drawn between the existing literature and this research.

Publication Easy to Replicate Complete Device Low Cost Remote Monitoring Data Acquisition Open Source
Stevens et al. [33] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.]
Bäumker et al. [34] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.]
Mohagheghi et al. [31] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.]
Leon-Salas et al. [30] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.]
Kurasaki et al. [35] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.]
This work [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.] [Image omitted. Please see PDF.]

Appendix A

Table of bill of materials.

No. Ref Name Product Detail (Model) Package Vendor Number Price (CAD)/Parts Price (CAD) Links (All Visited on 17 April 2025)
1 C1, C4, C8, C9, C11 10uF 10 μF 0805 Digikey 5 0.06 0.292 https://www.digikey.ca/en/products/detail/samsung-electro-mechanics/CL21A106KOQNNNE/3886754
2 C2, C5, C12, C15 0.1uF 0.1 μF 0805 Digikey 4 0.04 0.1584 https://www.digikey.ca/en/products/detail/samsung-electro-mechanics/CL21B104KCFNNNE/5961324?
3 C3, C7, C10 1.0uF 2.2 μF 0805 Digikey 3 0.19 0.576 https://www.digikey.ca/en/products/detail/samsung-electro-mechanics/CL21B225KAFNFNE/3888611
4 C6, C19 4.7uF 4.7 μF 0805 Digikey 2 0.06 0.1168 https://www.digikey.ca/en/products/detail/samsung-electro-mechanics/CL21A475KAQNNNE/3886902
5 R2, R7, R8, R17, R18, R5, R11, R12, R13, R14 10k 10 kΩ 0805 Digikey 10 0.02 0.192 https://www.digikey.ca/en/products/detail/stackpole-electronics-inc/RMCF0805FT10K0/1760676
6 R3, R4, R34 1k 1 kΩ 0805 Digikey 3 0.013 0.039 https://www.digikey.com/en/products/detail/stackpole-electronics-inc/RNCP0805FTD1K00/2240229
8 R6 2.0k 2 kΩ 0805 Digikey 1 0.012 0.012 https://www.digikey.com/en/products/detail/stackpole-electronics-inc/RMCF0805FT2K00/1760249
9 JP1, JP2 0 k 0 Ω jumper 0805 Digikey 2 0.01 0.0296 https://www.digikey.ca/en/products/detail/stackpole-electronics-inc/RMCF0805ZT0R00/1756901
10 R9, R10 5.1k 5 kΩ 0805 Digikey 2 0.02 0.0384 https://www.digikey.ca/en/products/detail/stackpole-electronics-inc/RMCF0805FT5K10/1760394
12 Memory Card Slot MEM2061-01-188-00-A 10 (8 + 2) Position microSD™ 10 (8 + 2) position Digikey 1 1.69 1.69 https://www.digikey.ca/en/products/detail/gct/MEM2061-01-188-00-A/9859612
13 U7 CH340C USB to Serial Adapter Chip SOP-16 Amazon 1 3.768 3.768 https://www.amazon.ca/JESSINIE-CH340C-SOP-16-Adapter-Oscillator/dp/B0BK991VVV/
14 U6 Voltage regilator for AS7341 AP7312-1833W6-7 SOT-26 Digikey 1 1.78 1.78 https://www.digikey.ca/en/products/detail/diodes-incorporated/AP7312-1833W6-7/2901062
15 Q1, Q4 N-MOS BSS138 SOT-23-3 Digikey 2 0.25 0.502 https://www.digikey.ca/en/products/detail/onsemi/BSS138/244210
16 X1 AS7341 AS7341-DLGM 8-TFLGA Digikey 1 12.37 12.37 https://www.digikey.ca/en/products/detail/ams-osram-usa-inc/AS7341-DLGM/9996230
17 U1 battery charger MCP73831T-2ACI/OT SOT-23-5 Digikey 1 1.23 1.23 https://www.digikey.ca/en/products/detail/microchip-technology/MCP73831T-2ACI-OT/964301
18 U2 ESP32 WROOM 32E ESP32-WROOM-32E-H4 38-SMD Module Digikey 1 4.34 4.34 https://www.digikey.ca/en/products/detail/espressif-systems/ESP32-WROOM-32E-H4/12696413
19 U3, U5 3.3V regulator XC6222B331MR-G SOT25 Digikey 2 1.28 2.55 https://www.digikey.ca/en/products/detail/torex-semiconductor-ltd/XC6222B331MR-G/2138187
20 U4 Battery monitoring MAX17048G+T10 8-TDFN-EP Digikey 1 8.13 8.13 https://www.digikey.ca/en/products/detail/analog-devices-inc-maxim-integrated/MAX17048G-T10/3758921
21 USB1 USB Type C USB4105-GF-A-120 SMD Digikey 1 1.19 1.19 https://www.digikey.ca/en/products/detail/gct/USB4105-GF-A-120/14559037
22 Q2 nMOS MBT3904DW1T1G SOT-363 Digikey 1 0.29 0.29 https://www.digikey.ca/en/products/detail/onsemi/MBT3904DW1T1G/918648
23 Q3 P-MOS DMG2305UX-7 SOT-23-3 Digikey 1 0.44 0.44 https://www.digikey.ca/en/products/detail/diodes-incorporated/DMG2305UX-7/4340667
24 D5 RGB LED COM-16347 5.00 mm L × 5.00 mm W Digikey 1 0.83 0.83 https://www.digikey.ca/en/products/detail/sparkfun-electronics/COM-16347/11630204
25 - Lithium Battery HXJNLDC 3.7V 503759 1300mAh 5 × 37 × 59mm Amazon 1 22.00 22.00 https://www.amazon.ca/3000mAh-103665-Lithium-Replacement-Bluetooth/dp/B08MPLHH32/?th=1
26 SW1, SW2 Button KMR231NG ULC LFS 4.60 mm × 2.80 mm Digikey 2 0.89 1.78 https://www.digikey.ca/en/products/detail/c-k/KMR231NG-ULC-LFS/2176541
27 LED2, LED3 BLUE LED 150080BS75000 0805 Digikey 2 0.28 0.56 https://www.digikey.ca/en/products/detail/w%C3%BCrth-elektronik/150080BS75000/4489912
28 LED4 RED LED 150060RS75000 0603 (1608 Metric) Digikey 1 0.23 0.23 https://www.digikey.ca/en/products/detail/w%C3%BCrth-elektronik/150060RS75000/4489901
29 D4 Diode BAT60AE6327HTSA1 SOD323-3D Digikey 1 0.62 0.62 https://www.digikey.ca/en/products/detail/infineon-technologies/BAT60AE6327HTSA1/1280934
30 - PCB - - JLCPCB 1 5.00 5.00 https://jlcpcb.com/
Total = CAD 70.7

ESP32 codes, PCB Gerbers, and 3D printed parts repository.

Parts Name Quantity File Type License Location of File (All Visited on 17 April 2025)
PCB_gerbers 1 STEP/stl CERN OHL-S 2.0. https://osf.io/vxarp/
PCB_KiCad 1 STEP/stl CERN OHL-S 2.0. https://osf.io/vxarp/
3D_printed_parts_Onshape 5 STEP/stl CERN OHL-S 2.0. https://osf.io/vxarp/
ESP32_calibration_firmware 1 .ino GNU GPL v3 https://osf.io/vxarp/
ESP32_deployment_firmware 1 .ino GNU GPL v3 https://osf.io/vxarp/

3D printing parameters.

Parameter Value
Filament PLA
Layer Height 0.3 mm
Initial Layer Height 0.2 mm
Infill Density 15%
Printing Temperature 210 °C
Build Plate Temperature 60 °C
Print Speed 60 mm/s
Travel Speed 175 mm/s

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