Maged Mohammed

Drought stress poses a considerable challenge to agriculture sustainability in arid regions. Water scarcity severely affects date palm growth and productivity in these regions. However, as water resources become increasingly scarce in arid regions, understanding the drought tolerance of date palm cultivars becomes imperative for developing drought-resistant cultivars and optimizing irrigation water usage for sustainable agriculture. This research examines the impact of different drought stress levels based on evapotranspiration (ETc), i.e., 40%, 60%, 80%, and 100% ETc, and time intervals (0, 6, 12, 18, and 24 months) on leaf growth, net photosynthesis, chlorophyll a and b content, and leaf relative water content (LRWC) of four prominent date palm cultivars, Khalas, Barhee, Hilali, and Ashrasee. In addition, the study also examines the effects of drought stress on dry weight, potassium and calcium content of leaf, stem, and root, and proline content in fresh leaves of these four cultivars. A solar-powered drip irrigation system with automated time-based irrigation scheduling was used to accurately control the irrigation water amount. To real-time estimate ETc in the study area, meteorological data were collected using a cloud-based IoT system. The findings of this study revealed that severe drought conditions (40 and 60 % ETc) significantly reduced leaf growth, plant dry biomass, and physiological and biochemical attributes; however, date palm cultivars can be grown under moderate drought conditions (80% ETc) with minimal effect on phenotypic, physiological, and biochemical traits to conserve water. The study also revealed that the drought-related characteristics decreased gradually with an increase in water stress time over 24 months. Comparing the date palm cultivars revealed that the Khalas and Barhee cultivars are more drought-tolerant, followed by Hilali, while Ashrasee is susceptible. The study elucidated a water conservation strategy employed in response to drought-induced stress based on the physiological and morphological parameters of date palm cultivars. It provides valuable insights into irrigation practices in arid regions. However, future studies can be focused on other nondestructive innovative techniques such as pulse-amplitude-modulation (PAM) fluorimetry, infrared radiation (IR), and video imaging system (VIS) methods to identify drought stress in date palms.

The safety and quality of fresh fruits deserve the greatest attention, and are a priority for producers and consumers alike. Modern technologies are crucial to accurately estimating and predicting fresh fruits’ quality and shelf life, to optimize supply chain management. Modified atmosphere packaging (MAP) is an essential method that maintains quality parameters and increases the shelf life of fresh fruits by reducing their ripening rates. This study aimed to develop a cost-effective, non-destructive technique using tiny machine learning (TinyML) and a multispectral sensor to predict/estimate the quality parameters and shelf life of packaged fresh dates under the natural atmosphere (Control), vacuum-sealed bags (VSBs), and MAP with different gas combinations: 20% CO2 + N balance (MAP1), and 20% CO2 + 10% O2 + N balance (MAP2). The shelf life and quality parameters of the packaged fresh dates (pH, total soluble solids (TSSs), sugar content (SC), moisture content (MC), and tannin content (TC)) were evaluated under different storage temperatures and times. A multispectral sensor (AS7265x) was utilized to correlate the fruit quality parameters with spectrum analysis under the same storage conditions, to prepare the dataset to train the prediction models. The prediction models were trained in the Edge Impulse Platform, and deployed to an Arduino Nano 33 BLE sense microcontroller unit (MCU) for inference. The findings indicated that the vacuum and MAP1 efficiently increased the shelf life and maintained the quality parameters of the packaged fresh fruit to 43 ± 2.39 and 39 ± 3.34 days, respectively, at 5 °C. The optimal neural network consisted of the input layer with 20 nodes (the packaging type, storage temperature, and 18 channels of the spectral sensor data at 410 to 940 nm wavelengths), two hidden layers with 20 and 12 nodes, and an output layer with one node for the target quality parameter or shelf life. These optimal prediction models efficiently predicted the shelf life with R2 = 0.951, pH with R2 = 0.854, TSSs with R2 = 0.893, SC with R2 = 0.881, MC with R2 = 0.941, and TC with R2 = 0.909. The evaluation of the developed prediction models under each packaging condition indicated that these models serve as powerful tools for accurately predicting fruit quality parameters, and estimating the shelf life of fresh dates.