This article is an integral proposal of remote laboratories that complements the classes and the traditional laboratories of the subjects of Automation at Universidad de Piura. Four control loops have been implemented in the FESTO Compact Workstation pilot plant to remotely manipulate them: level control, flow control, pressure control and temperature control. In each one of them, the students must identify the open-loop process, understand the nature of the process and finally control it in a closed – loop, all this in an automated and remote way through the Internet. EasyPort and DirectLogic06 PLC have been used for module control, DirectSOFT software for PLC programming, FESTO Didactic EzOPC and NI OPC Servers for communication, and mainly LabVIEW for graphic interface and data processing.

The agribusiness in Peru has grown significantly in the last decade and has become one of the most important producers of fine and flavor cocoa. The European Union has established quality parameters for the presence of heavy metals, one of which is cadmium due to its harmful effects on human health. To improve the quality control of cocoa, this article proposes to estimate the percentage of cadmium in a cocoa sample using the hyperspectral imaging and Machine Learning algorithms, as a non-invasive, non-destructive method that can be implemented in real time. In addition, areas of the spectral signature that contribute the most to the cadmium estimate are identified in order to significantly reduce the number of spectral bands used in the model. This reduction of spectral bands allowed to increase the R2 with the validation data from 67.92% to 72.39%, reaching an average error of 0.18 ppm using the Partial Least Square (PLS) method.

La mancha carmelita del arroz, asociada a Bipolaris oryzae, requiere esquemas de vigilancia que permitan detección y respuesta temprana. En este trabajo se presenta un esquema metodológico de vigilancia fitosanitaria en arroz basado en la integración de teledetección de proximidad y mediciones in situ en sectores agrícolas del departamento de Lambayeque (provincias de Lambayeque, Ferreñafe y Chiclayo). Se empleó un UAV DJI Matrice 300 RTK con cámara multiespectral (B, G, R, RedEdge y NIR; azul, verde, rojo, borde rojo e infrarrojo cercano) y sensor térmico H20T, complementado con captura de esporas, inspección visual del cultivo y registro meteorológico in situ. Las adquisiciones se organizaron en jornadas de monitoreo sucesivas entre marzo y junio de 2025. Los resultados preliminares muestran la generación de productos georreferenciados multiespectrales y térmicos y la obtención de evidencia fitosanitaria de referencia (severidad visual y cuantificación de esporas) para su integración con variables espectrales y térmicas. El avance evidencia la factibilidad del enfoque y sienta las bases para etapas posteriores de modelado con inteligencia artificial, integración de variables meteorológicas y generación de mapas de riesgo con proyección a un geoportal de apoyo a la decisión fitosanitaria.

In Peru, the exportation of organic bananas has increased considerably in the last ten years, the arrival of the “boom” of organic bananas is imminent, therefore it is necessary better management of small producers and associations for this organic resource, providing them with new technologies that help them to better organize, develop and monitor production. In the present study, a segmentation algorithm based on k-means has been developed, through which it was possible to group the weekly production of an organic banana packhouse, formulated on its production ratio. This project has been carried out to facilitate the monitoring of the production of each sector of the agricultural cooperative APBOSMAM (Asociación de Productores de Banano Orgánico Sector el Monte y Anexos Mallaritos), which provided production data. In the development of this study, two production ratios have been considered for the analysis: “Plant population per hectare ratio” and “Pre-bagged bunches ratio” by each sector during a week. From formed groups, it has been possible to identify the sectors with the lowest production and the possible factors causing this low contribution at the time of exporting the fruit.