This study focused on the formulation and characterization of silver nanoparticles (AgNP) functionalized with d-limonene. The nanoparticles were functionalized by phase inversion and the synthesis of the nanoparticles was performed in situ; particle size was determined by laser diffraction, zeta potential and optical colloidal stability using Multiscan 20 for a period of 24 hours at 37 °C; the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the formulated material on Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 29213, Klebsiella oxytoca ATCC 700324, Enterococcus casseliflavus ATCC 700327, Escherichia coli BLEE, carbapenem-resistant Pseudomona aeruginosa were determined. The nanoparticles showed colloidal stability at a d-limonene concentration of 3.93%, silver ions at 1.61 × 10⁻³%, non-ionic adjuvant at 24% and ascorbic acid at 5.88%; citric acid/citrate (1:1) 0.48M for a pH of 4.5 was used as a buffer system. The formulation was classified as a polydisperse system (PD = 0.0851), with a zeta potential of −11.6 mV and average particle size of 81.5 ± 0.9 nm. A particle migration velocity of −0.199 ± 0.006 mm∙h⁻¹, a constant transmission profile and backscattering profile with variations of 10% were evidenced, which represents a stable formulation. The nanoparticles presented an MIC and an MBC of 28 μg∙mL⁻¹ (5.6 × 10⁻²% d-limonene and 4.7 × 10⁻⁵% AgNP) against all tested bacteria.

In Côte d’Ivoire, the government and its development partners have implemented a national strategy to promote agroforestry and reforestation systems as a means to combat deforestation, primarily driven by agricultural expansion, and to increase national forest cover to 20% by 2045. However, the assessment of these systems through traditional field-based methods remains labor-intensive and time-consuming, particularly for the measurement of dendrometric parameters such as tree height. This study introduces a remote sensing approach combining drone-based Airborne Laser Scanning (ALS) with ground-based measurements to enhance the efficiency and accuracy of tree height estimation in agroforestry and reforestation contexts. The methodology involved two main stages: first, the collection of floristic and dendrometric data, including tree height measured with a laser rangefinder, across eight (8) agroforestry and reforestation plots; second, the acquisition of ALS data using Mavic 3E and Matrice 300 drones equipped with LiDAR sensors to generate digital canopy models for tree height estimation and associated error analysis. Floristic analysis identified 506 individual trees belonging to 27 genera and 18 families. Tree height measurements indicated that reforestation plots hosted the tallest trees (ranging from 8 to 16 m on average), while cocoa-based agroforestry plots featured shorter trees, with average heights between 4 and 7 m. A comparative analysis between ground-based and LiDAR-derived tree heights showed a strong correlation (R² = 0.71; r = 0.84; RMSE = 2.24 m; MAE = 1.67 m; RMSE = 2.2430 m and MAE = 1.6722 m). However, a stratified analysis revealed substantial variation in estimation accuracy, with higher performance observed in agroforestry plots (R² = 0.82; RMSE = 2.21 m and MAE = 1.43 m). These findings underscore the potential of Airborne Laser Scanning as an effective tool for the rapid and reliable estimation of tree height in heterogeneous agroforestry and reforestation systems.