The Plectranthus ornatus Codd. (also known as acetaminofem and boldo) has been found to have various pharmaceutical uses, including insecticidal properties. The metabolite composition of P. ornatus varies depending on soil and climatic conditions. The objective of this study was to optimize the growth and metabolite composition of P. ornatus (N = 72) through biodynamic substrate cultivation in Machetá-Cundinamarca, Colombia, located at 1850 masl, over a period of 60 days. Four different substrates were tested: sandy, vermicompost, horse manure, and biodynamic substrate, with the aim of identifying significant differences that would indicate optimization.The study evaluated root growth, plant material growth, and relative metabolite amounts (%) present in an ethanolic extract obtained under the same conditions. Additionally, the volatile fraction was identified using HS-SPME/GC-MS, and soil analysis was performed. The results showed that the plants grown in the biodynamic substrate had the highest growth in plant material and root (p < 0.05), while the plants grown in the sandy substrate had the highest concentration of volatile fraction in the extract. Furthermore, the plants grown in the biodynamic substrate exhibited greater vitality, and the physicochemical/microbiological composition of this substrate at the end of the trial showed a higher concentration of nutrients required for aromatics cultivation and a higher concentration of nitrogen-fixing bacteria.In conclusion, this study suggests that in the long term, the biodynamic substrate may be more efficient in obtaining metabolites of pharmaceutical interest, and a combination with sandy substrate should be considered for optimal results.

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Coffee Leaf Rust (CLR) is a fungal epidemic disease that has been affecting coffee trees around the world since the 1980s. The early diagnosis of CLR would contribute strategically to minimize the impact on the crops and, therefore, protect the farmers’ profitability. In this research, a cyber-physical data-collection system was developed, by integrating Remote Sensing and Wireless Sensor Networks, to gather data, during the development of the CLR, on a test bench coffee-crop. The system is capable of automatically collecting, structuring, and locally and remotely storing reliable multi-type data from different field sensors, Red-Green-Blue (RGB) and multi-spectral cameras (RE and RGN). In addition, a data-visualization dashboard was implemented to monitor the data-collection routines in real-time. The operation of the data collection system allowed to create a three-month size dataset that can be used to train CLR diagnosis machine learning models. This result validates that the designed system can collect, store, and transfer reliable data of a test bench coffee-crop towards CLR diagnosis.