The Pistoia Mountains exhibit a great variety of flora, particularly rich in aromatic plants, such as juniper, mint, savory, helichrysum, and thyme. Thyme is especially notable for its essential oil, typically displaying high thymol and carvacrol content. While the chemotype of thyme determined by its specific terpene composition is genetically controlled, environmental factors, plant age, and seasonality can influence terpene production. This article investigates the morpho-chemical variability of wild thyme plants collected from two different regions of the Pistoia Mountains, identifying five distinct chemotypes. The gas chromatography–mass spectrometry (GC-MS) technique was used to characterize the chemical profiles and determine the seasonal variation in terpene production, identifying spring and summer as the balsamic period, the optimal time for essential oil collection. Furthermore, high-value thyme clones were preserved through in vitro micropropagation, ensuring chemotype stability. These findings contribute to a deeper understanding of wild thyme biodiversity and provide a foundation for practical applications, including the development of value-added products like herb-infused cheeses, plant and animal disease treatments, and integrated pest management strategies in agricultural systems.

In recent times, salinity has emerged as a significant abiotic stress affecting plant survival in an ecosystem. In the experimental setup, response to salinity was investigated in Populus alba L. ‘Villafranca’ clone transgenic plants (line 16, 22 and 24) over-expressing an aquaporin – aqua1 – and compared to P. alba wild-type (Wt). Plants were grown in vivo under both control conditions (0 mM NaCl) and salinity (100 mM NaCl) for the duration of 28 days. This work presents a pilot study to investigate the performance of machine learning based algorithms to classify poplar leaves exposed to salt stress treatment, with the aim of performing the task in automated manner. The data set used in this work contains images acquired through a normal RGB camera. Segmentation and normalization based preprocessing were applied on the complete data set. Convolutional Neural Networks (CNNs) based architectures were employed for the extraction of prominent features from the images. Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) classified the extracted feature into the group of plants treated with 100 mM NaCl or otherwise. The primary evaluation index, accuracy indicates that the classification with SVM and RF achieved highest accuracy 75% and 71% to detect leaves of 100 mM NaCl treated plants.

Salinity is one of the main abiotic stresses for plant survival. In this work, response to salinity was investigated in Populus alba L. clone ‘Villafranca’ transgenic plants over-expressing aqua1, an aquaporin of Populus × canadensis Moench clone ‘I-214’ (GeneBank: GQ918138) and compared to wild-type plants (WT). Two lines with a different level of over-expression were selected. Plants were grown under control conditions (0 mM NaCl) and salinity (100 mM NaCl) for 27 days. At the end of the experiment, transgenic and WT plants were phenotypically indistinguishable. Shoot elongation and total leaf area were reduced equally in all treated plants, even if the relative reduction was lower in transgenic Line 16. The other morphological and physiological parameters measured did not evidence a response correlated to aqua1 over-expression. Interestingly, we observed a decrease in Na concentration in the treated roots of Line 16 compared to WT. Higher polyphenols content in roots and leaves of transgenic Line 16 under control condition was observed. suggesting that aqua1 over-expression has contributed to improve ion regulation and oxidative stress mitigation. These findings revealed that the aqua1 over-expression helps maintain osmotic balance and efficient water transport, contributing to the stability of polyphenol profiles under salinity stress, thereby enhancing tolerance of transgenic lines. Also, our results suggest aqua1 involvement in cell and plant growth and a possible buffering effect of aqua1 over-expression by transcriptional and post-translational modifications that must be further investigated.