Food security has become an increasingly important challenge for all countries globally, particularly as the world population continues to grow and arable lands are diminishing due to urbanization. ...Water scarcity and lack of labor add extra negative influence on traditional agriculture and food production. The problem is getting worse in countries with arid lands and harsh climate, which exacerbates the food gap in these countries. Therefore, smart and practical solutions to promote cultivation and combat food production challenges are highly needed. As a controllable environment, greenhouses are the perfect environment to improve crops' production and quality in harsh climate regions. Monitoring and controlling greenhouse microclimate is a real problem where growers have to deal with various parameters to ensure the optimal growth of crops. This paper shows our research in which we established a multi-tier cloud-based Internet of things (IoT) platform to enhance the greenhouse microclimate. As a case study, we applied the IoT platform on cucumber cultivation in a soilless medium inside a commercial-sized greenhouse. The established platform connected all sensors, controllers, and actuators placed in the greenhouse to provide long-distance communication to monitor, control, and manage the greenhouse. The implemented platform increased the cucumber yield and enhanced its quality. Moreover, the platform improved water use efficiency and decreased consumption of electrical energy. Based on the positive impact on water use efficiency and enhancement on cucumber fruit yield and quality, the established platform seems quite suitable for the soilless greenhouse cultivation in arid regions.
Drought is the most severe problem for agricultural production, and the intensity of this problem is increasing in most cultivated areas around the world. Hence improving water productivity is the ...primary purpose of sustainable agriculture. This study aimed to use cloud IoT solutions to control a modern subsurface irrigation system for improving irrigation management of date palms in arid regions. To achieve this goal, we designed, constructed, and validated the performance of a fully automated controlled subsurface irrigation system (CSIS) to monitor and control the irrigation water amount remotely. The CSIS is based on an autonomous sensors network to instantly collect the climatic parameters and volumetric soil water content in the study area. Therefore, we employed the ThingSpeak cloud platform to host sensor readings, perform algorithmic analysis, instant visualize the live data, create event-based alerts to the user, and send instructions to the IoT devices. The validation of the CSIS proved that automatically irrigating date palm trees controlled by the sensor-based irrigation scheduling (S-BIS) is more efficient than the time-based irrigation scheduling (T-BIS). The S-BIS provided the date palm with the optimum irrigation water amount at the opportune time directly in the functional root zone. Generally, the S-BIS and T-BIS of CSIS reduced the applied irrigation water amount by 64.1% and 61.2%, respectively, compared with traditional surface irrigation (TSI). The total annual amount of applied irrigation water for CSIS with S-BIS method, CSIS with T-BIS method, and TSI was 21.04, 22.76, and 58.71 m3 palm−1, respectively. The water productivity at the CSIS with S-BIS (1.783 kg m−3) and T-BIS (1.44 kg m−3) methods was significantly higher compared to the TSI (0.531 kg m−3). The CSIS with the S-BIS method kept the volumetric water content in the functional root zone next to the field capacity compared to the T-BIS method. The deigned CSIS with the S-BIS method characterized by the positive impact on the irrigation water management and enhancement on fruit yield of the date palm is quite proper for date palm irrigation in the arid regions.
Cold storage is deemed one of the main elements in food safety management to maintain food quality. The temperature, relative humidity (RH), and air quality in cold storage rooms (CSRs) should be ...carefully controlled to ensure food quality and safety during cold storage. In addition, the components of CSR are exposed to risks caused by the electric current, high temperature surrounding the compressor of the condensing unit, snow and ice accumulation on the evaporator coils, and refrigerant gas leakage. These parameters affect the stored product quality, and the real-time sending of warnings is very important for early preemptive actionability against the risks that may cause damage to the components of the cold storage rooms. The IoT-based control (IoT-BC) with multipurpose sensors in food technologies presents solutions for postharvest quality management of fruits during cold storage. Therefore, this study aimed to design and evaluate a IoT-BC system to remotely control, risk alert, and monitor the microclimate parameters, i.e., RH, temperature, CO2, C2H4, and light and some operating parameters, i.e., the temperature of the refrigeration compressor, the electrical current, and the energy consumption for a modified CSR (MCSR). In addition, the impacts of the designed IoT-BC system on date fruit quality during cold storage were investigated compared with a traditional CSR (TCSR) as a case study. The results showed that the designed IoT-BC system precisely controlled the MCSR, provided reliable data about the interior microclimate atmosphere, applied electrical current and energy consumption of the MCSR, and sent the necessary alerts in case of an emergency based on real-time data analytics. There was no significant effect of the storage time on the most important quality attributes for stored date fruit in the MCSR compared with the TCSR. As a result, the MCSR maintained high-quality attributes of date fruits during cold storage. Based on the positive impact of the designed IoT-BC system on the MCSR and stored fruit quality, this modification seems quite suitable for remotely managing cold storage facilities.
Fresh dates have a limited shelf life and are susceptible to spoilage, which can lead to economic losses for producers and suppliers. The problem of accurate shelf life estimation for fresh dates is ...essential for various stakeholders involved in the production, supply, and consumption of dates. Modified atmosphere packaging (MAP) is one of the essential methods that improves the quality and increases the shelf life of fresh dates by reducing the rate of ripening. Therefore, this study aims to apply fast and cost-effective non-destructive techniques based on machine learning (ML) to predict and estimate the shelf life of stored fresh date fruits under different conditions. Predicting and estimating the shelf life of stored date fruits is essential for scheduling them for consumption at the right time in the supply chain to benefit from the nutritional advantages of fresh dates. The study observed the physicochemical attributes of fresh date fruits, including moisture content, total soluble solids, sugar content, tannin content, pH, and firmness, during storage in a vacuum and MAP at 5 and 24 ∘C every 7 days to determine the shelf life using a non-destructive approach. TinyML-compatible regression models were employed to predict the stages of fruit development during the storage period. The decrease in the shelf life of the fruits begins when they transition from the Khalal stage to the Rutab stage, and the shelf life ends when they start to spoil or ripen to the Tamr stage. Low-cost Visible–Near–Infrared (VisNIR) spectral sensors (AS7265x—multi-spectral) were used to capture the internal physicochemical attributes of the fresh fruit. Regression models were employed for shelf life estimation. The findings indicated that vacuum and modified atmosphere packaging with 20% CO2 and N balance efficiently increased the shelf life of the stored fresh fruit to 53 days and 44 days, respectively, when maintained at 5 ∘C. However, the shelf life decreased to 44 and 23 days when the vacuum and modified atmosphere packaging with 20% CO2 and N balance were maintained at room temperature (24 ∘C). Edge Impulse supports the training and deployment of models on low-cost microcontrollers, which can be used to predict real-time estimations of the shelf life of fresh dates using TinyML sensors.
•The almond moth, Cadra cautella is a major postharvest pest of dates worldwide.•Solar energy is applicable as non-chemical alternative in pest management.•Heat treatment has no negative effect on ...physicochemical properties of dates.
The almond moth, Cadra cautella is a worldwide pest that inflicts serious economic damage on dates while on the bunch and during storage. After phasing out of the potent fumigant methyl bromide, extreme temperatures remain an alternative option for the management of this insect pest. A solar-powered heat system was tested for the control of eggs and larvae of C. cautella on fruit of four cultivars, namely Khalas, Shishi, Shibibi, and Khashram. The heating system consisted of an insect treatment chamber, two similar solar collectors, horizontal and vertical air ducts, precision integrated-circuit temperature sensors (lm 35), axial suction fans, and automatic temperature control unit. The delay time required for heating to attain the target temperature of 55 °C in fruit of all cultivars was 120 min. The average lethal time (LT95) at 55 °C which 95% of the eggs and larvae of C. cautella has died was 52.1 and 52.3 min, respectively. Exposing dates to 55 °C for 60 min resulted in total mortality for both eggs and larvae without significant effect on total soluble solids, color, and sugars of all cultivars. Heat treatment of 55 °C for 60 min could be applied to dates immediately after harvest as a safeguard against C. cautella infestation.
Multiple sclerosis (MS) is a widespread neurological autoimmune disease that includes episodes of demyelination in the central nervous system (CNS). The accumulated evidence has suggested that aryl ...hydrocarbon receptor (Ahr), a ligand-activated transcription factor, is a promising treatment target for MS. Thus, the current study aimed to identify a novel Ahr ligand with anti-inflammatory potential in experimental autoimmune encephalomyelitis (EAE).
An in silico analysis was carried out to predict interactions between Ahr and potential natural ligands. The effects of a predicted interaction were examined in vitro using CD4+ T cells under T helper17 (Th17) cell-polarizing conditions and lipopolysaccharide (LPS)-stimulated macrophages. Silencing Ahr and microRNA (miR)-132 was achieved by electroporation. Myelin oligodendrocyte glycoprotein (MOG)35-55 and the adoptive transfer of encephalitogenic CD4+ T cells were used to induce EAE.
Molecular docking analysis and in vitro data identified gallic acid (GA) as a novel Ahr ligand with potent activation potential. GA induced the expression of Ahr downstream genes, including cytochrome P450 family 1 subfamily A member 1 (Cyp1a1) and the miR-212/132 cluster, and promoted the formation of the Ahr/Ahr nuclear translocator (Arnt) complex. In vivo, GA-treated mice were resistant to EAE and exhibited reduced levels of proinflammatory cytokines and increased levels of transforming growth factor-β (TGF-β). Furthermore, GA reduced infiltration of CD4+CD45+ T cells and monocytes into the CNS. The anti-inflammatory effects of GA were concomitant with miR-132-potentiated cholinergic anti-inflammation and the regulation of the pathogenic potential of astrocytes and microglia. Inducing EAE by adoptive transfer revealed that CD4+ T cells were not entirely responsible for the ameliorative effects of GA.
Our findings identify GA as a novel Ahr ligand and provide molecular mechanisms elucidating the ameliorative effects of GA on EAE, suggesting that GA is a potential therapeutic agent to control inflammation in autoimmune diseases such as MS.
Understanding the flight characteristics of insect pests is essential for designing effective strategies and programs for their management. In this study, we designed, constructed, and validated the ...performance of modern flight-testing systems (flight mill and flight tunnel) for studying the flight behavior of red palm weevil (RPW)
(Olivier) under a controlled atmosphere. The flight-testing mill consisted of a flight mill, a testing chamber with an automatically controlled microclimate, and a data logging and processing unit. The data logging and processing unit consisted of a USB digital oscilloscope connected with a laptop. We used MATLAB 2020A to implement a graphical user interface (GUI) for real-time sampling and data processing. The flight-testing tunnel was fitted with a horizontal video camera to photograph the insects during flight. The program of Image-Pro plus V 10.0.8 was used for image processing and numerical data analysis to determine weevil tracking. The mean flight speed of RPW was 82.12 ± 8.5 m/min, and the RPW stopped flying at the temperature of 20 °C. The RPW flight speed in the flight tunnel was slightly higher than that on the flight mill. The angular deceleration was 0.797 rad/s
, and the centripetal force was 0.0203 N when a RPW tethered to the end of the rotating arm. The calculated moment of inertia of the RPW mass and the flight mill's rotating components was 9.521 × 10
N m
. The minimum thrust force needed to rotate the flight mill was 1.98 × 10
N. Therefore, the minimum power required to rotate the flight mill with the mean revolution per min of 58.02 rpm was approximately 2.589 × 10
W. The designed flight-testing systems and their applied software proved productive and useful tools in unveiling essential flight characteristics of test insects in the laboratory.
The use of high-pressure carbon dioxide and heating applications has been more popular against stored product insect pests recently. We designed and constructed a modern automated controlled ...atmosphere system based on carbon dioxide under different pressures and temperature regimes for the management of the red flour beetle (Tribolium castaneum) (RFB) and saw-toothed grain beetle (Oryzaephilus surinamensis) (SGB) infesting stored dates (cv. Sagai). The findings of this study showed that the minimum exposure time that killed 100% of all developmental stages of the two beetles was 30 min at a pressure of 300 kPa and a temperature of 45 °C. This combination was optimal in the consumption of electrical energy and carbon dioxide. Based on the lethal time that killed 50% (LT50) and 95% (LT95) of the test insect population, RFB showed more susceptibility to the treatment than SGB. The optimal combinations of carbon dioxide concentration, pressure, and temperature at minimum exposure time that killed 100% of the test insects have no significant detrimental effects on the essential characteristics related to date quality. The designed prototype of the controlled atmosphere system, based on the pressure of carbon dioxide and heat, proved to be effective against the two target insects in stored dates. The findings encourage further studies for testing and evaluating the system under field conditions at a larger scale.
•We designed and validated a controlled atmosphere system against stored date pests.•A pressure of 300 kPa and temperature of 45 °C for 30 min, caused 100% beetle mortality.•Combination of CO2 partial pressure and temperature proved effective against insects.•The delay time to raise the internal temperature of date fruit from 22 °C to 45 °C was 75 min.•The designed controlled atmosphere system has no negative effect on fruit quality.
Evaluating and predicting date fruit quality during cold storage is critical for ensuring a steady supply of high-quality fruits to meet market demands. The traditional destructive methods take time ...in the laboratory, and the results are based on one specific parameter being tested. Modern modeling techniques, such as Machine Learning (ML) algorithms, offer unique benefits in nondestructive methods for food safety detection and predicting quality attributes. In addition, the electrical properties of agricultural products provide crucial information about the interior structures of biological tissues and their physicochemical status. Therefore, this study aimed to use an alternative approach to predict physicochemical properties, i.e., the pH, total soluble solids (TSS), water activity (a
), and moisture content (MC) of date fruits (Tamar), during cold storage based on their electrical properties using Artificial Neural Networks (ANNs), which is the most popular ML technique. Ten date fruit cultivars were studied to collect data for the targeted parameters at different cold storage times (0, 2, 4, and 6 months) to train and test the ANNs models. The electrical properties of the date fruits were measured using a high-precision LCR (inductance, capacitance, and resistance) meter from 10 Hz to 100 kHz. The ANNs models were compared with a Multiple Linear Regression (MLR) at all testing frequencies of the electrical properties. The MLR models were less accurate than ANNs models in predicting fruit pH and had low performance and weak predictive ability for the TSS, a
, and MC at all testing frequencies. The optimal ANNs prediction model consisted of the input layer with 14 neurons, one hidden layer with 15 neurons, and the output layer with 4 neurons, which was determined depending on the measurements of the electrical properties at a 10 kHz testing frequency. This optimal ANNs model was able to predict the pH with R
= 0.938 and RMSE = 0.121, TSS with R
= 0.954 and RMSE = 2.946, a
with R
= 0.876 and RMSE = 0.020, and MC with R
= 0.855 and RMSE = 0.803 b by using the measured electrical properties. The developed ANNs model is a powerful tool for predicting fruit quality attributes after learning from the experimental measurement parameters. It can be suggested to efficiently predict the pH, total soluble solids, water activity, and moisture content of date fruits based on their electrical properties at 10 kHz.
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