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.

The use of automated machines and its manipulation using artificial intelligence is increasingly common to perform routine tasks within the industrial field. The present work aims to show the automation of a robotic arm, its monitoring and control using a web server and a Human Machine Interface (HMI) screen. For this work a robotic arm MITSUBISHI was programmed for the classification of pieces based on their color. Subsequently, this system is monitored and controlled employing the programming of a web page and the design of an HMI created using the TIA-Portal software. As a result of this methodology, a complete system of industry 4.0 will be obtained, which can be implemented to control and monitor a robotic arm using a HMI screen and Web Server in the current industry. The systems used to carry out the control were a PLC S300 (cpu313C 2 DP) with ASI CP 343 2 DP network card, with 5 slaves, Keypad (Slave 1) Module 2DI (Slave 2), Optoreflexive Sensor (Slave 3) Set of valves FESTO (Slave 4) Modules2DI 2DO (Slave 5) 2DO an S1200 PLC (CPU 1214 C DC / DC / DC) an HMI screen (KTP600 Basic Mono DP) as color sort.

This data article presents a multimodal, non-invasive dataset documenting the physiology and growth stages of Stenocereus queretaroensis (pitayo), a native species from the arid and semi-arid regions of Southern Zacatecas, Mexico. In particular, Stenocereus spp. are important cacti in the region due to its nutritional properties, role as an economic resource, and cultural significance.It is worth emphasising that these cacti traditionally grow wild (i.e., without deliberate cultivation); accordingly, controlled cultivation is uncommon and remains understudied. With the aim of producing a formal, comprehensive analysis and compendium, the data were collected across multiple phenological stages to provide a complete representation of the plant development cycle, from vegetative growth through to fruiting. To achieve this, the collection process combined high-resolution multispectral imaging with field spectrometry in the 400–700 nm range. Standardized acquisition protocols were applied in field conditions to capture consistent reflectance data, and environmental variables such as illumination, temperature, and geographic coordinates were recorded for each session to ensure reproducibility.