AI-assisted image analysis and physiological validation for progressive drought detection in a diverse panel of L.

Introduction: Drought detection, spanning from early stress to severe conditions, plays a crucial role in maintaining productivity, facilitating recovery, and preventing plant mortality. While handheld thermal cameras have been widely employed to track changes in leaf water content and stomatal conductance, research on thermal image classification remains limited due mainly to low resolution and blurry images produced by handheld cameras.

Methods: In this study, we introduce a computer vision pipeline to enhance the significance of leaf-level thermal images across 27 distinct cotton genotypes cultivated in a greenhouse under progressive drought conditions.

Data on long-term monitoring programs to assess environmental pressures on coastal area.

The concentration of six metals/metalloids, five congeners of high molecular weight Polycyclic Aromatic Hydrocarbons (PAHs), and sum of five congeners of Polychlorinated Biphenyls (PCBs) determined within marine-coastal sediments of the Apulia Coast during a 5-year long-term monitoring program, are reported through tables and radial graphs. The data are referred to the pollutant concentration determined within 70 sites alongside two marine transects (500 m from coastline and 1750 m of coastline) representing different morphologic features of the coast and different pollution stressors loading [1]. Concentration variability during the five monitored years and data generated by the non-parametric correlation analyses among sediment physical-chemical main parameters and metal concentrations are also included.