Objectives: Multispectral imaging and spectrophotometry are widely used to estimate plant physiological characteristics, yet the literature remains fragmented across sensors, indices, and analytical approaches. Methods: This systematic review followed PRISMA 2020 and was preregistered in OSF (Open Science Framework). Web of Science, Scopus, Google Scholar, and Consensus were searched up to January 2025 for peer-reviewed studies and selected gray literature studies focused on plant physiological trait estimation using multispectral or spectrophotometric methods. From 256 identified records, 96 studies met the eligibility criteria. Methodological quality was assessed across five domains, and results were synthesized narratively owing to high heterogeneity. Results: A total of 96 studies met the eligibility criteria. Among these, multispectral sensors were the most commonly used (40.7%), followed by UAV-mounted platforms (25.9%), while hyperspectral sensors accounted for 18.5% of the studies. The most frequently used vegetation index was NDVI, reported in 87% of the studies, mainly for estimating vigor, biomass, and canopy structure. Discussion: Although multispectral indices reliably capture key agronomic traits, cross-study comparability is currently hampered by significant methodological variability and a lack of consistent validation protocols. Conclusions: Multispectral imaging and spectrophotometry are effective tools for estimating plant physiological traits, but greater standardization is needed across studies. Owing to the limited number of studies on Stenocereus spp., the review was expanded to plants in general; the shortage of reports addressing Stenocereus spp. highlights the need for future research in these species.

This paper presents the design, implementation, and evaluation of a fuzzy logic-based adaptive lighting strategy for Phaseolus vulgaris cultivation. The objective of this study was to integrate an image-derived plant growth indicator obtained through projected leaf-area estimation into an adaptive pulse-width modulation framework. The proposed system used two input variables: the error between the desired and measured Photosynthetic Photon Flux Density (PPFD) and the estimated projected leaf area. PPFD was indirectly estimated from illuminance measurements acquired using a BH1750 sensor (ROHM Semiconductor, Kyoto, Japan) Mostrar más líneas connected to an Arduino Uno microcontroller, while projected leaf area was obtained from RGB images captured with an Intel RealSense D435 camera and processed in MATLAB R2021a. The fuzzy inference system was implemented using the Mamdani approach and the Fuzzy Logic Designer application. The experimental results showed that the system regulated the PWM signal within a range of 17.00% to 87.85%, corresponding to a power range of 13.65 W to 34.58 W. The projected leaf-area indicator increased from 284.94 cm² to 392.57 cm² during the 21-day evaluation period, representing a relative increase of 37.77%. These findings provide preliminary evidence supporting the integration of image-derived projected leaf-area information, illuminance sensing, and fuzzy logic for adaptive lighting regulation in controlled-environment cultivation systems.

Heart rate variability (HRV) is a useful non-invasive tool for studying autonomic nervous system modulation under emotional stimulation; however, accurate estimation of dominant frequencies in HRV signals remains challenging due to their non-stationary nature and the sensitivity of some spectral methods to configuration parameters. This work presents a methodology for the spectral characterization of HRV signals derived from ECG recordings from the DREAMER database, with emphasis on optimizing the model order of the MUSIC algorithm to improve dominant frequency localization within the physiological low-frequency (LF) and high-frequency (HF) bands. The proposed methodology included ECG signal preprocessing, R-peak detection, RR interval extraction, HRV interpolation, and spectral analysis using MUSIC, while evaluating different model orders through a signal-driven composite criterion based on AIC, MDL, ESTER, eigengap, and model complexity. The criteria were normalized using min–max normalization and combined using equal predefined weights. The results showed that the signal-driven selection of the parameter p produced recording-dependent model order configurations and different dominant frequency estimates across the analyzed stimuli. The resulting LF/HF agreement was evaluated independently after model order selection and showed non-uniform correspondence across stimuli and spectral estimators. Overall, these findings indicate that model order selection can substantially influence the spectral characterization obtained with MUSIC and provide a signal-driven framework for examining this dependence in HRV recordings.