A Portable Tool for Spectral Analysis of Plant Leaves That Incorporates a Multichannel Detector to Enable Faster Data Capture

Abstract

In this study, a novel system was designed to enhance the efficiency of data acquisition in a portable and compact instrument dedicated to the spectral analysis of various surfaces, including plant leaves, and materials requiring characterization within the 410 to 915 nm range. The proposed system incorporates two nine-band detectors positioned on the top and bottom of the target surface, each equipped with a digitally controllable LED. The detectors are capable of measuring both reflection and transmission properties, depending on the LED configuration. Specifically, when the upper LED is activated, the lower detector operates without its LED, enabling the precise measurement of light transmitted through the sample. The process is reversed in subsequent iterations, facilitating an accurate assessment of reflection and transmission for each side of the target surface. For reliability, the error estimation utilizes a color checker, followed by a multi-layer perceptron (MLP) implementation integrated into the microcontroller unit (MCU) using TinyML technology for real-time refined data acquisition. The system is constructed with 3D-printed components and cost-effective electronics. It also supports USB or Bluetooth communication for data transmission. This innovative detector marks a significant advancement in spectral analysis, particularly for plant research, offering the potential for disease detection and nutritional deficiency assessment.

1. Introduction

Optical spectrometers that operate in the visible and infrared regions have become indispensable tools in our daily lives, fundamentally transforming our comprehension and interaction with a wide range of materials and substances, regardless of their physical state. This technology is extensively employed in several areas, including agriculture, energy, chemistry, materials science, and food production [1,2,3,4,5,6,7,8,9]. The operating principle of visible–short-wave near-infrared (VIS-NIR-SWIR) spectroscopy is the transfer of energy between light and matter. This fundamental principle allows for the identification and study of different compounds by examining their distinct spectral properties. In fact, the spectral characteristics within the VIS-NIR-SWIR range are intricately linked to the vibrational modes of the functional groups found in the target substance [2,10]. Indeed, the vibrational modes serve as a distinctive mark, resembling a fingerprint, which enables scientists and researchers to obtain valuable information about the composition and characteristics of the substances being studied. Moreover, the non-invasive, high-resolution, and non-destructive aspects of Vis-SWNIR spectroscopy are very beneficial in plant studies [3,11,12]. It enables quick assessments without causing damage to the samples, making it ideal for the real-time monitoring of plant health and nutrient levels.

Several studies used spectral analysis to investigate plants in the past [12,13,14,15,16,17,18,19,20]. For example, Ge et al. explored the utility of VIS-NIR-SWIR as a high-throughput instrument for measuring six leaf parameters of maize plants: chlorophyll content (CHL), leaf water content (LWC), specific leaf area (SLA), nitrogen (N), phosphorus (P), and potassium (K) [12]. To estimate leaf attributes from hyperspectral data, two multivariate modeling techniques, namely, partial least-squares regression (PLSR) and support vector regression (SVR), were used to calculate several vegetation indices. The results show that the proposed methodologies can be used to predict CHL levels but not the other leaf metrics. In 2020, ** of input data, which represents the spectrum reflectance measurements, to the matching output data, which are the reflectance values at specified wavelengths. The MLP model, which is designed for feedforward operation, adeptly captures intricate connections between the input and output data, facilitating accurate spectrum analysis and predictions. On the other hand, the weights of these connections are modified during the training process. Each neuron’s output is multiplied by the connection weight, then undergoes a rectified linear unit (ReLU) activation function and is summed with the outputs of other linked neurons. In this case, a ReLU function was selected because of its reduced training time and straightforward integration into embedded devices.

Tiny machine learning (TinyML) is a revolutionary branch of artificial intelligence that enables the execution of ML models on low-power devices, like MCUs. This breakthrough technology empowers the implementation of machine learning models for sensor data analysis directly on the device, resulting in lower power consumption and feasible deployment on battery-powered devices. The benefits of TinyML are manifold: local data processing minimizes the latency, enhancing the efficiency and expediting decision-making without the need for information transfer to a server. Moreover, reduced power consumption is critical for battery-constrained devices, while local data storage heightens the security by mitigating risks associated with information transfer. The implementation process typically commences with training the model on a higher-power computer using TensorFlow, followed by optimization with TensorFlow Lite to reduce the size and complexity. The model is then adapted to the MCU’s capabilities, and necessary code is written to load and run the model on the device, undergoing tests and performance adjustments as needed.

In this work, the perceptron training process was carried out using the TensorFlow library. The main objective of this implementation was the subsequent integration of the model in an embedded system to have the data adjusted in real-time. This optimization process is achieved through the adaptation of the model to the tiny machine learning framework, which allows for its efficient implementation on an MCU with limited resources.

2.6. Measurement of Reflectance Using an Optical Spectrum Analyzer (OSA)

The experimental setup illustrated in Figure 6 was employed to obtain the reflectance spectrum of colored paper sheets with different colors. This setup employed a laser-controlled plasma-type white light source (Energetiq EQ-99-FC, Wilmington, MA, USA) that emitted between 190 and 2500 nm, and an optical spectrum analyzer (OSA) (Yokogawa, AQ6373, Tokyo, Japan). Both instruments in this scenario were connected using a fiber optic probe (Ocean Optics, QR200-7-UV-VIS), which enabled the measurement of the reflectance of the sample being analyzed. As depicted in Figure 6, the probe was strategically positioned at a 45° angle to mitigate the impact of undesirable reflections that might compromise the measurement accuracy. This orientation served the dual purpose of not only minimizing unwanted reflections but also preventing incident light from reflecting directly back toward the light source. To ensure that the measurement was always taken in the same position, i.e., at the same distance between the probe and the sample, a holder was employed. This holder was allowed for securing the position of the probe with a screw.

The OSA measurements were conducted using a linear scale with a precision of 5 nm, covering a wavelength range of 350 nm to 980 nm with increments of 0.31 nm. The initial phase of the experiment entailed capturing diffuse reflectance spectra spanning from 350 nm to 980 nm. This was achieved by utilizing a certified reflectance standard, specifically the USRS-99-010 model from Labsphere (Hewlett Packard, Palo Alto, CA, USA). This accessory helped to calibrate the white color, thereby guaranteeing the consistency and accuracy of measurements. Its function was to emulate an ideal target with nearly perfect reflectance, facilitating precise and reliable data collection throughout the experiment. Through spectral analysis of this reference sample, we established a standard against which the reflectance characteristics of other materials could be evaluated. This comparison was crucial to ensure precise and uniform measurements across all samples. By employing a reference sample, we could compensate for variations in the measurement configuration, such as fluctuations in the light intensity or sensor sensitivity, ensuring the successful normalization of the collected data. Subsequently, a series of colored paper sheet samples, with each one displaying a distinct color, were subjected to experimentation utilizing the aforementioned setup to acquire the reflectance spectrum for each colored paper sample. The selected colors were red (C01), orange (C02), yellow (C03), fuchsia (C04), violet (C05), dark blue (C06), light blue (C07), dark green (C08), and light green (C09).