Efficacy of Prostaglandin Analogues for Induction of Labour and Associated Complications: A Retrospective Research Study conducted in Latifa Women and Children Hospital - Juniper Publishers

 

Reproductive Medicine - Juniper Publishers

Introduction

Induction of labour has become a more common worldwide medical intervention during the last few years [1]. The ideal cervical ripening agent must be effective, safe, easy to be administered and acceptable for the pregnant woman. Utilizing prostaglandins (PG) for cervical ripening during induction of labour (IOL) was first described in the 1960s [2]. Since that time various types of prostaglandins including PGF2α, PGE2 (Dinoprostone) and PGE1 (Misoprostol) were extensively studied to elicit the best prostaglandin pharmacological agent for pre-induction cervical ripening [2]. Dinoprostone was found to be superior to the others, as it increased the rates of successful vaginal delivery within 24 h without increasing the operative delivery rates. Vaginal route was found to be a safe and effective approach of bringing on labor.2

There are different pharmacological and mechanical methods that have been approved to ripen the unfavourable cervix [3]. Prostaglandins are the most effective drugs that cause cervical ripening by increasing inflammatory mediators in the cervix and inducing cervical changes. Prostaglandin E1 (PGE1) and prostaglandin E2 (PGE2) have different effects on these processes and on myometrium contractility [4]. The PGE2 is available for cervical ripening as a 3mg Dinoprostone vaginal pessary and also as a controlled release pessary (Propess®), which releases 10 mg of Dinoprostone over 24 hours. Prostin tablet is inserted into the vagina every 6 hours, with a maximum of 3 doses, as per our hospital protocol.

The effect of PGE2 has been investigated and there are many studies in the literature comparing the efficacy of the different formulations available in the market [5]. The incidence of Induction of labour is rising in the current era, with the advancement of technology viz. increasing frequency of ultrasound studies and CTG for fetal monitoring and assessment of fetal wellbeing by fetal medicine units. An appropriate and well tolerated pharmacological method of induction of labour cannot be decided without doing a detailed and in-depth analysis of the two commonly used drugs.

This research study was conducted to compare the efficacy of Propess and Prostin for induction of labor and their complications according to national and international standards.

Therefore, it will help us to update the current hospital guideline of Induction of Labour and eventually improve patient care.

Methodology

This is a retrospective study conducted over a period of 6 months from 1/10/2019 to 31/3/2020. The data was analysed from 597 patients in Latifa Hospital, Dubai, United Arab Emirates for the period of 12 months (1/10/2019 to 30/9/2020). The efficacy of two drugs Prostin and Propess, for induction of labour were compared with respect to their progression to labour and associated complications. The data was collected from the labour room records. Inclusion criteria included pregnant females that were 18-45 years old, gestational age of 28 weeks or more, singleton pregnancy, and cephalic presentation. Exclusion criteria included previous caesarean section/uterine surgery, any contraindication to vaginal delivery, suspected cephalo-pelvicdisproportion, multiple pregnancy, and unexplained antepartum haemorrhage. The data was collected using MS excel sheet.

Demographics

A total of 622 patients were enrolled in this study. Complete data was available for 597 out of them. These were distributed into two groups according to whether Prostin (56.8%) or Propess (43.2%) was used for induction of labour (IOL). Variations between the two groups were accounted for in terms of the following demographic factors: age, gestational age, parity, and indications for IOL. The mean age of women who received Prostin in our study was 31.6 (SD 0.32) and this was higher than that of Propess which was 29.6 (SD 0.33). The gestational ages between both groups were rather similar with the mean of Prostin being 38.4 weeks (SD 0.10) and Propess was 38.6 weeks (SD 0.12). Nulliparity was the most common parity amongst our study group with a valid percent of 37.4%. This was double the incidence of the next common parity which was 1 (18.6%). A varied range of indications for IOL existed amongst these patients ranging across Medical (HTN, DM, Cholestasis of pregnancy etc) and Obstetric (post successful ECV for unstable lie, IUGR, post term, foetal demise). The common indications were Diabetes Mellitus (31.5%) followed by post term (10.2%) and followed by intrauterine growth restriction (9.7%).

Statistical Analysis

Numerical data are presented as mean ± standard deviation or median (min/max) as appropriate and categorical data are presented as a percentage. Chi squared test or Fischer’s exact test was used to compare categorical variables (viz. complications) between the two groups of the study. T-test or Mann-Whitney test was used to compare numerical variables between the two study groups as appropriate.

All the tests are 2-sided tests and P value <0.05 indicates statistically significant results. SPSS 24 was used for data analysis.

Efficacy of drugs

The efficacy of these drugs was assessed by whether labour started or not after induction of labour. It was shown that a total of 79.1% of women from both categories collectively progressed into labour while 20.9% did not. 81.4% of those who were induced with Prostin progressed to labour while 76.0% of those who were given Propess progressed into labor. Using the Pearson Chi-Squared test this had a p-value of 0.105(not significant). Reinduction, which is defined as multiple doses of prostaglandin, was required in 1.84% of our patient population.

Complications

The most common complications following IOL were nonreassuring cardiotocography (CTG) and hyperstimulation/ tachysystole. These had an overall occurrence of 90.3% and 8.2% for Prostaglandins respectively. Other complications such as abruptio placenta and postpartum haemorrhage (PPH) were not seen as frequently with a percentage of 0.5%. While comparing the drug used for IOL, non-reassuring CTG was seen in 10.9% in women who received Prostin while it occurred in 6.2% in women who received Propess. Hyperstimulation/tachysystole was seen in 1.2% of Propess patients while there were none that experienced it in Prostin patients. The Pearson chi-squared test analysing this correlation had a p-value of 0.024 (significant). The incidence of no complications was significantly common too, with 92.6% of those on Prostin having no complications and 87.2% of those with Propess similarly. Fisher’s exact test was used to analyse the above correlation resulting in a p-value of 0.011(significant). The mean age of those who had complications was found to be 28.76 years for both Prostin and Propess and mean parity for the same was 0.91 (SD 0.251). Median parity of those who had complications is nulliparity. Test used for this comparison was Mann-Whitney test with p-value <0.001 (significant).

Mode of delivery

Those that had normal vaginal delivery (NVD) were 78.9% of the study population whilst the remaining 21.1% delivered via lower segment caesarean section. Progression to normal vaginal delivery was more common in women who received Prostin (81.4%) compared to those who received Propess (75.6%). Pearson chi-squared test revealed a p-value of 0.084 (not significant).

Discussion

The present study compared the induction of labour using Prostin vs Propess through a retrospective analysis in the hospital setting. Prostin 3mg was administered 6 hourly vaginally up to 3 doses, whereas Propess (10mg) controlled release single pessary was administered vaginally and left in place for up to 24 hours. The success rates, which was defined as onset of labour, of Prostin and Propess were 81.4% and 76% respectively. This was not statistically significant (p value= 0.105). Following induction of labor with both the agents, the incidence of “reinduction” was rather insignificant and did not bear weight on the outcomes of our study. Many studies that have investigated the efficacy of prostaglandin E2 in the induction of labour showed that Propess had a higher success rate than Prostin [6,7]. A most recent study evaluated the effect of Dinoprostine vaginal insert (Propess) compared to that of the vaginal tablet (Prostin) in primigravida specifically [8], and showed that Propess was the preferred and a better tolerated Prostaglandin E2 tablet for IOL because of the reduced need for vaginal examinations. On the other hand, our analysis shows an insignificant difference in efficacy between the two Prostaglandins E2, which was the primary outcome in this study. This is supported by multiple randomised control trials that also showed no significant difference between the two groups resulting in no preference of one drug over the other in terms of better efficacy [9-11].

The secondary outcome, being the complications resulting between the two drugs, proved to be a non-reassuring CTG and hyperstimulation/tachysystole combined. In our research, nonreassuring CTG was shown in 10.9% of the Prostin cohort while 6.2% of the Propess cohort which is insignificant (p value= 0.084). A study done in 1992, supports our results by showing that non-reassuring CTG was similar in both PGE2 pessary vs placebo groups [12]. Tachysystole was defined as more than 5 contractions/10mins minutes for two consecutive 10-minute periods. Hyperstimulation is defined as either > 5 contractions in ten minutes over a 30minute period, or contractions lasting more than 2 minutes in duration, or contractions of normal duration occurring within 60 seconds of each other as written by the NHS Wales protocol. But many authors define Hyperstimulation as exaggerated uterine response with late fetal heart rate decelerations or fetal tachycardia of more than 160 beats per minute or other worrisome fetal heart rate changes [13,14]. Our results indicate that the combination group of hyperstimulation/ tachysystole was higher in the Propess cohort while none of the Prostin cohort experienced this complication (p value=0.0024). Similarly, the same comparison done in Walsall, UK resulted in more cases of tachysystole in Propess rather than Prostin [11]. Walsall also had a higher rate of uterine hyperstimulation in both prostaglandins compared with the placebo. However, in all the cases, hyperstimulation resolved within 15 min after removal of the pessary indicating that the direct cause was from the effect of the prostaglandins.

The complications in IOL among nulliparous women is greater than that in multiparous as shown in a 2020 study done in Ireland. Many nullipara (32.63%) had undergone caesarean section compared to the multipara (4.37%). Therefore, it is in conjunction with our results that nulliparous women had a greater number of complications in comparison to other parities within our study (p value<0.001). Moreover, previous studies show that induction of labour in medically uncomplicated nulliparous women at term carries higher risk of emergency Cesarean section, compared to those who underwent spontaneous labour [13]. However, our study showed an insignificant difference between the rate of NVD versus caesarean section in the IOL with Propess and Prostin collectively (p=0.084).

A limitation of our study included the lack of a control/ placebo group which could have been used for a more effective comparison.

Conclusion

The results of our study concluded that the success of inducing labour between Prostin and Propess is not statistically significant and either can be used for a favourable outcome. Complications following their viz. non-reassuring CTG, failure of induction, reinduction of labor, higher risk of emergency Cesarean section and are quite similar with both agents. However, tachysystole/ hyperstimulation was more with Propess. These complications, however, are greater seen in nulliparous women compared to multiparous.

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Simultaneous Determination and Quantitation of Artemeter and Lumefantrine in Antimalarial Tablet Formulation using High Performance Liquid Chromatography with UV Detection - Juniper Publishers

 

Pharmacology & Clinical Research - Juniper Publishers

Abstract

Artemether-lumefantrine (AL) combination therapy is now the most used anti-malarial treatment in the world. In Ghana, it has been used as a first line treatment for uncomplicated Plasmodium falciparum malaria since 2004. In this paper, a reliable High Performance Liquid Chromatography method method was developed and validated for the simultaneous determination of AL in commercial fixed-dose combination tablets in the Kintampo-North Municipality. The method employed a Jasco HPLC system equipped with C18 reverse phase column and a mobile phase of acidified methanol and triethylamine buffer (85:15) pH 2.7 as the mobile phase. The flow rate was 1ml/min and detection were by means of a UV detector set to 222nm. The isocratic mode of elution was employed. The retention time of lumefantrine was 5.22 ± 0.19 minutes and artemether, 4.19 ± 0.22 minutes. The method was validated by evaluation of different parameters such as accuracy, precision, linearity, ruggedness and robustness. The percentage recovery for artemether and lumefantrine ranged between 99.18-100.19 and 99.96-100.07 respectively. Six brands of artemether-lumefantrine fixed-dose combination tablets (two local and four foreign) from selected chemical shops and pharmaceutical shops in the Kintampo- North Municipality were analyzed. Of the six brands of artemether-lumefantrine fixed-dose combination tablets analyzed, all passed with respect to their artemether and lumefantrine content using the developed HPLC method. The percent recovery for the local brands ranges from 93.5 to 99.2% and from 91.3 to 97.2% for artemether and lumefantrine respectively. For the foreign brands, 92.05 to 105.0% and 95.8- 99.9% for artemether and lumefantrine respectively, which complies with the International Pharmacopoeia range of 90110. The optimized and validated RP-HPLC method is simple, sensitive, precise, accurate and reproducible. The developed method has been validated as per ICH guidelines and meets all the acceptance criteria given. Hence it can be used in routine analysis for the simultaneous determination of artemether and lumefantrine in bulk as well as in pharmaceutical preparations.

Keywords:Artemether; Lumefantrine; High; Performance; Liquid; Chromatography

Abbreviations:ACT: Artemisinin-based Combination Therapy; AM: Artemether; API: Active Pharmaceutical Ingredient; BP: British Pharmacopoeia; HPLC: High Performance Liquid Chromatography; ICH: International Conference on Harmonization ; IP: International Pharmacopoeia; LU: Lumefantrine; LOD: Limit of Detection; LOQ: Limit of Quantitation; ODS: Octadecylsilane; RP-HPLC: Reverse Phase HPLC; RSD: Relative Standard Deviation; SALMOUS: Standards for Articles Legally Marketed Outside the U.S; SD: Standard Deviation; USP: United States Pharmacopoeia; UV: Ultra-Violet; WHO: World Health Organizations

Introduction

Malaria continues to be one of the major public health problems in Africa, Asia and Latin America. About 219 million cases of malaria and an estimated 660 000 deaths were recorded, out of these, 90 % occurred in Africa [1]. In Ghana malaria accounts for more than 60% of under-five hospital admissions, and 8% of under-five mortality and 9.2% of maternal deaths. The use of antimalarial medications is the only practical means of preventing mortality and lowering morbidity brought on by the disease in many malaria-endemic locations, particularly in the African region [2]. Several routinely prescribed antimalarial medications, including chloroquine and sulfadoxine-pyrimethamine, no longer work on Plasmodium falciparum. The WHO has advised that all antimalarial medications should combine an artemisinin derivative with a co-drug as a result [3]. These treatments incorporate two active substances with various modes of action [1]. It is quite concerning because ACT-resistant P. falciparum has just been found along the Thailand/Cambodia border [4], There is evidence that drug-resistant falciparum malaria has migrated from Asia into Africa [5].

Artemisinin derivatives are incredibly important antimalarial medications, and because of their quick action and lack of adverse effects, they are in high demand in endemic areas, making them vulnerable to forgery and counterfeiting [6]. Checking the quality of antimalarials is necessary to prevent the emergence of resistance. For reliable and accurate results, High performance liquid chromatography is a very powerful tool for the quantification of these medications. There are currently HPLC methods for the analysis of lumefantrine and the assay of artemether in finished pharmaceutical products (FPP) [7] as well as for lumefantrine analysis [8]. However, only a few HPLC methods were reported for the quantitative determination of Artemether and lumefantrine in fixed combination anti-malarial products [8-11]. Hence this study seeks to add to the existing methods, a method that is rapid, economical, precise and accurate for the assay of artemether and lumefantrine.

Materials and Methods

Reagents and materials

Methanol, ethanol, Triflouroacetic acid, Hydrochloric acid, trielthylamine and acetonitrile used were HPLC grade from BDH, Europe. Sodium Phosphate was obtained from Sigma Aldrich, and Hydrochloric acid from Elitech Solutions, France. Artemether and lumefantrine standards were obtained as generous gift from Pharmanova, Accra, Ghana. AL tablets were sampled from selected chemical shops and three pharmaceutical shops in Kintampo.

Instrumental and analytical condition

The HPLC analyses were carried out on a JASCO HPLC system equipped with a stationary phase consist of an Ultracarb 3μ C_18, (20) 200*3.2mm and a UV detector. The injection volume was 20 μL. The separation of artemether and lumefantrine was evaluated in different proportions of different solvents or different proportions of the same solvents or same proportion with different conditions such as the flow rate and, for each condition, the retention times were noted. The optimized condition was achieved using a mobile phase comprising acidified Methanol and triethylamine buffer (85:15).

Preparation of standard solutions

Artemether–lumefantrine standard solution

4mg lumefantrine and 24mg artemether were accurately weighed and transferred into a 25mL volumetric flask, sonicated and diluted to volume with the mobile phase to give a solution of 160μg/mL of artemether and 960μg/mL of lumefantrine. The solution was then filtered using a sintered glass filter. 20μL of this solution was injected into the column and the chromatographs recorded.

Preparation and Analysis of Tablet Formulations

Twenty Tablets of artemether and lumefantrine were weighed and finely powdered. A quantity equivalent to 4mg of artemether and 24mg of lumefantrine was transferred into 25 mL volumetric flask and appropriate amount of diluent was added. The contents were sonicated to dissolve completely and the volume was made up to the mark with diluent and filtered through sintered glass filter. 1 mL of stock solution was transferred to a 10 Ml volumetric flask and made volume up to the mark with diluents to get final concentration of 16μg/mL and 96μg/mL for artemether and lumefantrine, respectively. 20μL of this solution was injected into the column and the chromatograph recorded. To calculate the artemether and lumefantrine content in the tablets, their respective peak areas were inserted into the linearity equation from the linearity graph to determine their contents.

Validation Linearity

Aliquot portions of standard stock solution 0.2, 0.4, 0.6, 0.8 and 1.0 mL were taken in separate 10 mL volumetric flasks. The volume was adjusted to the mark with diluent to obtain concentrations of 3.2, 6.4, 9.6, 12.8, 16.0μg/mL and 19.2, 38.4, 57.6, 76.8, 96.0, 115.2 μg/mL for Artemether and Lumefantrine, respectively. Calibration curve was plotted over a concentration range of 3.2-16μg/mL for artemether and 19.2-115.2μg/mL for lumefantrine. Calibration curve was constructed by plotting peak area v/s concentration, the graph must be linear and the regression equation was calculated.

Precision

One set of three different concentrations of mixed standard solutions of artemether and lumefantrine were prepared. All the solutions were analyzed in triplicates, in order to record any intraday variations in the results. For inter-day variations study, three different concentrations of the mixed standard solutions in linearity range were analyzed on three consecutive days. The peak areas were recorded and the Relative Standard deviation (RSD) was calculated for both series of analyses.

Robustness

In the robustness study, the influence of small, deliberate variations of the analytical parameters on retention time of the drugs was examined. The following factors were selected:
1. Flow rate of the mobile phase (2.7±0.02ml/min)
2. Wavelength at which the drugs were recorded (222±1nm).

Accuracy

The accuracy of the method was determined by calculating the recovery of the analyte of interest by the standard addition method: Known amounts of working standard of artemether (1.6μg) and lumefantrine (9.6μg) were added to solutions of various concentrations like: 3.2μg, 6.4μg and 9.6μg of artemether and 19.2μg, 38.4μg and 57.6μg of lumefantrine. Each sample was prepared in triplicate and injected. Sensitivity

Limit of detection (LOD) and quantification (LOQ) were estimated from the signal-to-noise ratio. The LOD and LOQ were calculated by the use of the equations:
LOD = 3 σ/s.
LOQ = 10 σ/s

Where σ is the standard deviation of intercept of calibration plot and sis the average of the slope of the corresponding calibration plot.

Ruggedness

Sample solutions of artemether (16μg/mL) and lumefantrine (96μg/mL) were prepared and analyzed using slightly different operational and environmental conditions.

Results and Discussion

Chromatographic method development

Optimization of chromatographic mode, wavelength: Artemether and lumefantrine, both the API’s are non-polar in nature, hence either reversed phase or ion-pair or non-aqueous chromatography was used. Detection wavelength was selected by scanning reference standards over a wide range of wavelength from 200nm to 400nm. A fixed concentration of AM-LU (10μg/ml) was analyzed at different wavelengths. From the responses, the λ max value was found to be 209nm and 335nm for artemether and lumefantrine, respectively. Using this data 222nm was selected as a detection wavelength at which the components showed well resolved peaks. The standard solution of artemether and lumefantrine was prepared and run through the system and different combinations of mobile phase and column were tried for isocratic mode to get well resolved, symmetric peaks. The method employed acidified methanol/triethylamine phosphate buffer (85:15), which is economical but have well resolved peaks.

Validation

Linearity: The method demonstrates linearity over a concentration range of 10-100ug/ml for lumefantrine and 3-20ug/ml for artemether. From the calibration curve the R2 value over this range was found to be 0.9991 and 0.9995 for artemether and lumefantrine respectively. This indicates a linear relationship between the concentrations of the two analytes.

Robustness: When some conditions of the mobile phase such as pH and column used for the method were varied, there was no statistically significant difference between results of the varied and old conditions of mobile and stationary phases using the student t-test. The flow rate and wavelength of detection were varied, with no significant difference in the peak areas. This indicates that the method is robust under varying condition of both flow rate and wavelength of detection (USP SALMOUS edition 2008).

Precision: Precision is reflected by percentage RSD values less than 2. These low values suggest high sensitivity of the developed method (USP SALMOUS edition 2008).

Sensitivity: With the developed method, the LOD and LOQ for LU were 338 and 1129ug/ml (0.333 and 1.129mg/L) respectively. AM had 90 and 300ug/ml (0.090 and 0.033mg/L as LOD and LOQ respectively.

Accuracy: The percent recoveries for Accuracy was within range of (97.6 -99.86) % for AM and 97.5-99.2 for LU which indicates that the method was accurate.

Assay of Tablets

According to the USP SALMOUS standard, lumefantrine tablets should contain not less than 90.0 percent and not more than 110.0% of the labeled amounts of artemether and of Lumefantrine. (USP SALMOUS edition 2009). Six commercial brands of (two local and four foreign) AM-LU tablets were analyzed for active substances using the developed method. Triplicate determinations were carried out. The respective contents of AM and LU were 93.5/91.3, 99.2/97.2, 96.62/97.0, 95.82/98.5, 105.2/99.9, and 92.05/95.8 percentage of the declared contents for Malar2DS, DANMETHER, Malafantrine, Coartem, Lonart and Artemos plus. The formulations complied with the (90-110%) of the label claim for the IP.

Application

Analysis of Marketed formulation

The validated method was used for the simultaneous estimation of artemether and lumefantrine in fixed dose combination tablets. Six brands were procured and analyzed with the proposed method and the results are presented in Table 5. The content (mg) and percentages of each of artemether and lumefantrine in the tablet sample was computed using peak areas and the regression equations from the calibration curves. The mean contents obtained for artemether and lumefantrine in the formulated products were very close to the labeled amount. The results show that the method is accurate in determining the content of the two active ingredients in fixed dose combination tablets.

Conclusion

Considering the increasing use of ACT to treat malaria in endemic areas, the availability of simple and rapid analytical method is essential to evaluate the quality of formulations being used currently. From the present study it can be concluded that the optimized and validated RP-HPLC method is simple, sensitive, precise, accurate and reproducible. The developed method has been validated as per the ICH guidelines and it meets all the acceptance criteria given in ICH guidelines. Hence the method can be used in routine analysis for the simultaneous determination of Artemether and Lumefantrine in bulk as well as in pharmaceutical preparations.

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Sensor Data Acquisition and Analysis in Indoor Farming Using Azure IoT Hub - Juniper Publishers

 

Material Science - Juniper Publishers

Abstract

Indoor farming is one of the agricultural innovations, providing essential support in controlled environments that reduced many traditional farming challenges. However, they face unique issues in managing their crops effectively, from optimizing limited space to controlling environmental conditions within their facilities. While traditional indoor farming methods provide a foundation, they often lack the precision and real-time insights needed for optimal plant health and yield. This paper explores the application of Azure IoT Hub in indoor farming, focusing on how this technology facilitates enhanced data analysis to improve agricultural efficiency and productivity. In the realm of precision farming, Azure IoT Hub plays an important role by enabling seamless integration and management of IoT-enabled sensors. By integrating sensors into the green house, users can access real-time data on crucial parameters such as temperature, moisture levels, and potential nutrient issues. This data is then processed through Azure Stream Analytics, enabling users to make data-driven decisions with precision. These sensors are critical for monitoring variables such as water level, temperature, nutrient levels, and pH, allowing for precise control over irrigation and fertilization schedules. By implementing Azure IoT Hub, the paper demonstrates how real-time data acquisition and analysis can be optimized, ensuring that optimal growth conditions are consistently maintained. This system not only supports the immediate detection and response to environmental changes but also provides a framework for predictive analytics, which is crucial for handling issues that could adversely affect plant health and crop yield.

Keywords:Indoor farming; Precision agriculture; IoT in agriculture; Azure IoT hub; Real-time data analysis.

Abbreviations:SCK: Serial Clock; GND: Ground; VIN: Voltage Input; SDI: Serial Data Input; AI: Artificial Intelligence

Introduction

Agriculture origins date back to 12,000 years, it is a particularly unpredictable and imprecise sector. The primary goal of agriculture is to produce a sustainable food supply to meet the nutritional needs of people. Agriculture is experiencing a significant transformation that implies incorporating new technologies to meet yield requirements, comply with environmental regulations, address labor intensity challenges, and solve workforce issues. To meet these challenges, agriculture has evolved and diversified with advancements in technology, scientific research, and sustainable practices [1].

With farming serving as a crucial subset of agriculture, different indoor farming systems have emerged, including conventional agriculture, organic farming, hydroponics, aquaculture, and agroforestry, among others. These systems aim to maximize productivity while minimizing ecological harm, promoting biodiversity, and securing sustained agricultural viability.

The importance of real-time data in modern farming has revolutionized the way farmers manage their crops and livestock. Access to up-to-the-minute information enables them to make precise decision-making. For instance, real-time data helps farmers anticipate, enables them to optimize irrigation schedules and protect their crops. Sensors provide instant feedback on moisture and nutrient levels, facilitating timely interventions. Additionally, real-time tracking of livestock health indicators can prevent disease outbreaks and improve overall output. By integrating real-time data, farmers maximize efficiency, reduce waste, and increase profitability, ultimately leading to more sustainable agricultural practices as mentioned before.

In the realm of data analysis, Artificial Intelligence (AI) has added value to agriculture, broadening perspectives. CNIL characterizes AI as “the grand illusion of our era,” while experts view it as a segment of software capable of processing complex data. Interestingly, the advent of AI dates to the 1950s as shown in Figure1, contrary to the assumption of its novelty. AI encompasses a broad spectrum, including Machine Learning techniques, with Deep Learning as its subset.

Yann Le Cun, a researcher in Artificial Intelligence, considered as one of the inventors of deep learning, defines AI as a set of techniques allowing machines to accomplish tasks and solve problems normally reserved for humans and some animals [2].

Artificial intelligence requires access to big data, which is best managed through cloud storage rather than traditional data centers [3]. Azure offers a solution with its Azure IoT hub, its service supports the storage and analysis of extensive data streams from IoT devices such as Raspberry Pi, making it readily accessible for AI applications. This approach allows agriculture to overcome the limitations of local data centers and enhance decision-making efficiency [4].

Materials and Methods

Materials Temperature and humidity

I. Description: The Si7sub>021 I2C Humidity and Temperature Sensor shown in Figure 2 is an integrating humidity and temperature sensor element, an analog-to-digital converter, signal processing, calibration data, and an I2C Interface. The patented use of industry-standard, low-K polymeric dielectrics for sensing humidity enables the construction of low-power, monolithic CMOS Sensor ICs with low drift and hysteresis, and excellent long-term stability. The humidity and temperature sensors are calibrated, and the calibration data is stored in the on-chip non-volatile memory. This ensures that the sensors are fully interchangeable, with no recalibration or software changes required. (Silicon labs, 2017).

II. Power pins

a) Vin - this is the power pin. Since the chip uses 3VDC, we have included a voltage regulator on board that will take 3-5VDC and safely convert it down. To power the board, give it the same power as the logic level of our microcontroller - e.g. for a 5V micro like Arduino, use 5V.
b) 3v3 - this is the 3.3V output from the voltage regulator, we can grab up to 100mA.
c) GND - common ground for power and logic.
III. I2C Logic pins
a) SCL - I2C clock pin, connect to our microcontrollers I2C clock line.
b) SDA - I2C data pin, connect to our microcontrollers I2C data line.

Ultrasonic distance sensor HCSR04

The HCSR04 Ultrasonic (US) sensor shown in Figure 3 is a 4-pin module, whose pin names are Vcc, Trigger, Echo and Ground respectively. This sensor is a very popular sensor used in many applications where measuring distance or sensing objects are required. The module has two eyes like projects in the front which forms the Ultrasonic transmitter and Receiver. The sensor works with the simple high school formula: Distance = Speed × Time.

The Ultrasonic transmitter shown in Figure 4 transmits an ultrasonic wave, this wave travels in air and when it gets objected by any material it gets reflected toward the sensor this reflected wave is observed by the Ultrasonic receiver module as shown in the picture below

I. Power pins

a) Vcc. The Vcc pin powers the sensor, typically with +5V.
b) Trigger. This pin must be kept high for 10us to initialize measurement by sending US wave. Trigger pin is an Input pin.
c) Echo. This pin goes high for a period which will be equal to the time taken for the US wave to return to the sensor. Echo pin is an Output pin.
d) Ground. This pin is connected to the Ground of the system.

Characteristics of electronic components

Table 1 defines the characteristics of the electronic components. A breadboard is used to facilitate the connections between the Raspberry Pi and the Adafruit humidity-temperature sensor. The Ground (GND) pin of the Raspberry Pi is connected to the sensor’s GND pin. The Serial Clock (SCK) pin of the sensor is connected to the third pin of the Raspberry Pi, serving as the data input. The Voltage Input (VIN) pin is connected to the 3v3 pin on the Raspberry Pi to power the sensor with a low energy draw and a maximum current of about 50MA. The Serial Data Input (SDI) pin of the sensor is linked to the second pin of the Raspberry Pi, facilitating data transmission from the processor to the sensor.

Azure IoT hub

Azure IoT hub, as shown in Figure 5, is a managed cloud platform provided by Microsoft as part of its Azure services. Azure IoT Hub allows for bidirectional communication between IoT applications and the devices they manage [5]. It is integral for real-time data processing, device-to-cloud and cloud-to-device communication, secure messaging, and device management [6]. This platform supports various protocols, including MQTT, HTTPS, and AMQP, making it versatile for different IoT scenarios. In our study, Azure IoT Hub was used to securely collect, store, and analyze telemetry data from sensors connected to the Raspberry Pi, facilitating sophisticated data management and analysis capabilities crucial for precision agriculture [7].

Methods Sensor integration with raspberry Pi

The Raspberry Pi is connected to various sensors through the breadboard as shown in Figure 6. This setup is critical for realtime data acquisition from the physical environment. Through the breadboard, we connect the sensor Adafruit humiditytemperature: to the raspberry PI, the sensor has four pins. We connect the ground GND pin of raspberry PI and sensor, we connect the SCK pin of the sensor to the third pin of the raspberry PI, this connection serves as an input of data. We connect the VIN pin to the 3v3 in raspberry PI, this connection serves as a power low energy and a maximum available current of about 50mA. The last connection is between the SDI pin of the sensor and the second pin of the raspberry PI, this is for the data sent from the processor to the sensor.

Data management via azure IoT hub

The integration of the Raspberry Pi with Azure IoT Hub is essential for the transmission of data, ensuring the efficient flow of data from the Raspberry Pi to the cloud platform. The process involves transmitting data collected from environmental sensors to Azure IoT Hub, which supports big data storage, analysis, and accessibility. The management of data infrastructure not only captures real-time environmental variables but also secures and processes the data effectively. To illustrate the practical application, a Raspberry Pi simulator is used within a preprovisioned Azure IoT environment to demonstrate device registration, configuration, and data transmission.

Initial steps include setting up the Azure IoT environment: using Azure Cloud Shell, the Azure IoT extension is installed, and a device identity is created for the IoT hub. A connection string, crucial for linking the Raspberry Pi to the IoT hub, is generated and implemented in the Raspberry Pi Azure IoT Online Simulator. Figure 6 shows the Resource Groups section in Microsoft Azure portal’s interface. It illustrates how Raspberry Pi is registered. The presence of the Bash CLI in the portal confirms the flexibility Azure offers to users for interacting with their cloud resources using command-line operations Figure 7, which simplifies the process of deploying and managing IoT solutions.

Figure 8 shows how the Smart Phone communicates with Azure IoT Hub, which acts as the central node orchestrating data transmission and device management. This method highlights the critical role of Azure IoT Hub in not only facilitating seamless data transfer but also in securing the data sent and readily available for further analysis, thereby enhancing the capabilities of IoT solutions in environmental monitoring.

The integration of raspberry Pi with Azure IoT Hub facilitated real-time data acquisition and management, which is illustrated through the transmission of data in form of instant messages as shown in Figure 9. The console outputs display messages sent from the Raspberry Pi to the Azure IoT Hub, confirming successful communication and data relay involving environmental parameters such as humidity.

Figure 10 shows a multi-line graph representing different temperature readings- ambient, box, top, and reservoir temperatures. This graph is critical for understanding the thermal dynamics within the controlled environment, where significant fluctuations occur, potentially correlating with external environmental changes or internal system adjustments such opening the door of the growing chamber.

Figure 11 shows a line graph of relative humidity over time, indicating a peak and subsequent decline, which could suggest an interaction or response within the ecosystem as of controlled adjustment in humidity levels managed by the IoT system. These visualizations not only confirm the functionality of the sensor setup but also provide insights into the environmental conditions, demonstrating the system’s ability to monitor and react to the microclimate effectively.

The comparison in Table 2 highlights the significant improvements achieved by integrating Azure IoT Hub into the indoor farming system. Notably, real-time alert accuracy increased from 91% to 96%, while data latency was reduced by approximately 60%, ensuring faster and more reliable environmental monitoring. Additionally, the system’s enhanced scalability and security features make it suitable for deployment in larger agricultural setups. These improvements contribute to more informed decision-making, efficient resource management, and overall better performance in maintaining optimal growing conditions.

Discussion

This study demonstrated the effectiveness of integrating Azure IoT Hub with real-time sensor data acquisition and MATLAB’s predictive modeling capabilities to optimize indoor farming operations. The system successfully collected environmental data, such as temperature, humidity, and nutrient levels.

By applying this architecture, the following performance improvements were observed:
a) Water usage was reduced by 29%, from 5.2L to 3.7L per day per plant.
b) Nutrient solution use decreased by 36%, from 100mL to 64mL per cycle.
c) Average crop yield increased by 9.5%, reaching up to 301g of biomass per lettuce plant.
d) Real-time alert response time improved by 66%, from over 15 minutes to under 5 minutes.
e) Predictive modeling accuracy ranged between 93% and 96%, enabling highly reliable planning.
f) Irrigation scheduling became 50% more efficient with adaptive timing based on real-time feedback.

These outcomes demonstrate the potential of cloud-enabled IoT infrastructure in transforming data into actionable insights. The integration of Azure IoT Hub with MATLAB not only supports real-time monitoring but also enables predictive and prescriptive analytics essential for next-generation smart agriculture.

By enabling faster responses to environmental changes and reducing resource waste, the system aligns with sustainability goals and precision agriculture principles. Future enhancements could explore integration with Azure ML services for automated actuation and machine learning at the edge using Azure IoT Edge.

This predictive capability supports strategic agricultural planning, optimizing resource use, and improving yield outcomes by anticipating future environmental impacts on plant growth [8]. Such models are pivotal in transforming data into actionable insights, enhancing decision-making in precision farming [9].

Conclusion

The integration of cloud-based IoT infrastructure with advanced data analysis tools offers a significant step forward in the development of intelligent agricultural systems. This study highlights how Azure IoT Hub, when combined with real-time sensor networks and predictive modeling platforms like MATLAB, can enhance the management of indoor farming environments.

By enabling continuous environmental monitoring and datadriven decision-making, the system supports more precise control over critical variables such as temperature, humidity, and nutrient delivery. This approach not only improves operational efficiency but also contributes to more sustainable and resilient agricultural practices.

Overall, the proposed framework demonstrates the potential of combining IoT and analytics to support the transition from reactive to proactive farming strategies. It lays the groundwork for future innovations in autonomous crop management and reinforces the role of digital technologies in shaping the future of agriculture.

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