Date powder was produced by foam-mat freeze-dried method without and with carrier agents (maltodextrin and gum arabic) at two concentration levels (40 and 50%). The effect of carrier agents on the physicochemical properties of powder, i.e., hygroscopicity, degree of caking, wettability, dispersibility, solubility, water absorption index, color, and total phenolic content was investigated. Carrier agent-added date powders had lower hygroscopicity that offers good storage stability. Maltodextrin-added powders had lower wettability and higher solubility which indicate good reconstitution properties. The hygroscopicity, degree of caking, wettability, and total phenolic content significantly decreased with an increase in carrier concentration level.

Controlled environmental agriculture (CEA), integrated with internet of things and wireless sensor network (WSN) technologies, offers advanced tools for real-time monitoring and assessment of microclimate and plant health/stress. Drone applications have emerged as transformative technology with significant potential for CEA. However, adoption and practical implementation of such technologies remain limited, particularly in arid regions. Despite their advantages in agriculture, drones have yet to gain widespread utilization in CEA systems. This study investigates the effectiveness of drone-based thermal imaging (DBTI) in optimizing CEA performance and monitoring plant health under arid conditions. Several WSN sensors were deployed to track microclimatic variations within the CEA environment. A novel method was developed for assessing canopy temperature (Tc) using thermocouples and DBTI. The crop water stress index (CWSI) was computed based on Tc extracted from DBTI. Findings revealed that DBTI effectively distinguished between all treatments, with Tc detection exhibiting a strong correlation (R² = 0.959) with sensor-based measurements. Results confirmed a direct relationship between CWSI and Tc, as well as a significant association between soil moisture content and CWSI. This research demonstrates that DBTI can enhance irrigation scheduling accuracy and provide precise evapotranspiration (ETc) estimates at specific spatiotemporal scales, contributing to improved water and food security.

Machine Learning is a subset of Artificial Intelligence, which is a process of learning from different types of data to make accurate predictions. Data in tourism is various such as Statistics, Photos, Maps, and Texts. Also, each tourism cycle has different stages: Pre, During, and After Trip. In this paper application of machine learning in tourism related data and trip stages are introduced in detailed.

: The microclimate and cucumber crop response in a screenhouse and in an evaporatively cooled greenhouse were studied in Oman during winter/spring and spring/summer cultivation periods. Measurements were carried out in two similarly shaped structures: (1) a greenhouse equipped with a pad-and-fan system for evaporative cooling of the greenhouse environment and (2) a screenhouse with no cooling system. Analysis of the spring/summer period climate data showed that the evaporative cooling in the greenhouse reduced the mean air temperature by about 4.5°C compared to outside and maintained the leaf temperature close to the greenhouse air temperature. The 24 h mean leaf and air temperatures in the greenhouse reached 25.8°C ±1.3°C and 25.9°C ±0.8°C, respectively. On the other hand, the 24 h mean leaf and air temperatures in the screenhouse were higher by 1.0°C and 1.3°C, respectively, compared to outside. The 24 h mean leaf and air temperatures in the screenhouse reached 32.8°C ±1.2°C and 31.8°C ±1.5°C, respectively. Furthermore, the evaporative cooling in the greenhouse maintained the 24 h mean air vapor pressure deficit (VPD) values at levels lower than 1.1 kPa, while the 24 h mean air VPD in the screenhouse reached values up to 4.5 kPa. These differences resulted in a 50% decrease in crop fruit yield during the spring/summer period. The radiation and water use efficiency (WUE) values observed in the two structures were similar during the winter/spring period but were higher in the greenhouse during the spring/summer period. However, for the greenhouse, when the water evaporated in the wet pad was also considered, the overall WUE was at the same level in both structures during summer. Furthermore, the evaporative cooling applied in the greenhouse enhanced the mean values of fruit quality characteristics measured during the spring/summer, such as fruit dry matter content (5.6%), fruit firmness (5.0 kg cm^-2), and chroma (18.6), compared to that of the screenhouse (5.0%, 4.9 kg cm^-2 and 16.3, respectively), but did not significantly affect other fruit quality characteristics, such as mean fruit weight (128 g for greenhouse and 123 g for screenhouse), total soluble solids content (3.9 °Brix for both structures), and juice pH (5.7 for greenhouse and 5.6 for screenhouse). Overall, it can be concluded that under the weather conditions of Oman, although greenhouses are still needed during spring/summer, screenhouses can be used during winter without jeopardizing crop production quantity and quality. Keywords: Evaporative cooling, Evapotranspiration, Radiation use efficiency, Water user efficiency. Total water use efficiency, Climate.