Wearable sensors are a revolutionary tool in agriculture because they collect accurate data on plant environmental conditions that affect plant growth in real-time. Moreover, this technology is crucial in increasing agricultural sustainability and productivity by improving irrigation strategies and water resource management. This review examines the role of wearable sensors in measuring plant water content, leaf and air humidity, stem flow, plant and air temperature, light, and soil moisture sensors. Wearable sensors are designed to monitor various plant physiological parameters in real-time. These data, obtained through wearable sensors, provide information on plant water use and physiology, making our agricultural choices more informed and accurate. Internet of Things (IoT) technologies can improve irrigation strategies and reduce water consumption by analyzing data from wearable sensors and adapting it to automate the irrigation system. The review also highlights the importance of using Artificial Intelligence (AI) to predict plant water needs accurately. This review concludes that wearable sensors provide accurate and real-time data on the stress state of plants and their surroundings, improving water management efficiency and agricultural production sustainability. These IOT and AI-enabled technologies are a crucial milestone toward smart and sustainable agriculture, which shows the importance of innovation in responding to enhanced climate threats.
Water samples from a variety of sources in Kelantan, Malaysia (lakes, ponds, rivers, ditches, fish farms, and sewage) were screened for the presence of bacteriophages infecting
The majority of the environmental outputs from gas refineries are oily wastewater. This research reveals a novel combination of response surface methodology and artificial neural network to optimize and model oil content concentration in the oily wastewater. Response surface methodology based on central composite design shows a highly significant linear model with P value <0.0001 and determination coefficient R2 equal to 0.747, R adjusted was 0.706, and R predicted 0.643. In addition from analysis of variance flow highly effective parameters from other and optimization results verification revealed minimum oily content with 8.5 ± 0.7 ppm when initial oil content 991 ppm, tempe
The financial analysis of the published financial statements is the means that enables businessmen, financial institutions, financial analysts and others to conduct their studies and conclusions to obtain information that helps them in the decision-making process, including decisions related to investment. National in making the decision on the investment activity, for the period from 2012 to 2018, through the information provided by the annual financial statements, by selecting a set of indicators provided by the financial statements, namely (liquidity ratio, activity percentage, profitability ratios) to measure the extent of this ability Indicators in determining their role in making an investment decision.
The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices
... Show MoreThe prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices
... Show MoreAn experiment was carried out at Al-Raed Research Station, which is located on the Baghdad-Anbar road during the winter season, in order to study the effect of the type of solar panels and irrigation system on some performance indicators of irrigation systems and germination percentage of bean crop (Vicia Faba L). A randomized complete block design (RCBD) was used with three replications. The experiment consist of two factors. 1st factor was the solar panel type with two levels : monocrystalline and polycrystalline. Second factor was the irrigation system with two levels Drip and sprinkler irrigation system the following indicators were studied : solar panel efficiency (%), Irrigation
This experiment was carried out in one of the fields (A) affiliated to the College of Agricultural Engineering Sciences / University of Baghdad, for the spring season 2021, On hybrid tomato plants (Mayai Mayai) to test flower viability, using two factors, the first was three levels of irrigation interval (2, 4, 6) days, and the second factor three concentrations of compound Nano fertilizer with concentrations (0, 1.5, 2.5) gm liter-1, so that the number of treatments is 9 treatments and three replications, the number of experimental units is 27 experimental units distributed randomly according to the random drawing method to ensure reducing experimental error and obtaining the most accurate results. A factorial experiment 3 x 3 x 3 was carr
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