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Model predictive control for precision irrigation of a Quinoa crop

  • Iván Beltrán Ccama EMAIL logo , Bruno Faccini Santoro and José Oliden Semino
Published/Copyright: December 31, 2022

Abstract

Traditional High Andean agriculture is rainfed, and irrigation is commonly carried out in an open loop, that is, without measuring variables such as soil moisture content or plant development to define water consumption. This article presents model predictive control applied to irrigation systems under real conditions, whose purpose is the efficient use of water in rainfed crops with improved yield and crop productivity at minimum water consumption. The article presents a control strategy applying a model of predictive control that calculates the optimal amount of water for daily irrigation under real conditions. The most important attraction of the model is the prediction and future behavior of the controlled variables as a function of the changes in the manipulated variables. The objective is to improve the yield of the crop at minimum water consumption, for this, it will be necessary to use models that link with the Aquacrop software and allow it to be a source of data, and for the prediction of future values. The predictive controller is evaluated in the Quinoa crop (Chenopodium Quinoa Willdenow), and the performance is compared against existing traditional irrigation data in the literature. The results indicate that the predictive controller can achieve higher crop efficiency and reduce irrigation water supplies considerably.

1 Introduction

Agriculture is the sector responsible for most of the water consumption on the planet, corresponding to approximately 70% of the total use in 2020 [1]. An alternative to achieve the best use of water resources in agriculture is to apply control strategies with proven potential in the industry. Traditional irrigation methodologies are based on defining periods of time for the use of irrigation water, which do not take into account the real-time information that can be obtained from the crop, such as soil moisture, soil salinity, ambient temperature, need for water in the crop, and evapotranspiration.

Classic control methodologies, such as On/Off control and Proportional Integral Derivative control, are easy to implement and have proven effectiveness in the industry. However, given the complexity of agricultural systems (nonlinearity and multivariables), the model predictive control (MPC) has shown superior performance in processes of this type [2,3].

MPC performance is superior to classical control. The MPC can achieve high regulation accuracy with moderate complexity. Therefore, this method is very suitable for precision agricultural production.

The MPC is a strategy based on the numerical optimization of a cost function over a finite horizon that calculates the control input using a mathematical model to predict the responses of the process [4]. An MPC refers to a class of advanced computer-controlled algorithms that use an explicit process model to predict the future response of a plant. A series of control inputs are calculated at each sampling instant, but only the first calculated input is implemented in the process [5]. The first input of the optimal sequence is sent to the process, and the entire calculation is repeated at subsequent sampling times [4]. This controller is based on three ideas: the use of a prediction model, optimization in a sliding horizon, and feedback adjustment [6], and it also allows the introduction of restrictions. The literature mentions some MPC applications in irrigation systems [3,7,8,9,10,11].

The application of MPC to agriculture can generate significant productivity and efficiency benefits. However, no review has been reported in agricultural applications with high Andean crops.

The high Andean phytogeographic domain is characterized by presenting crops adapted to climatic rigor, both due to excessive cold and lack of water, specifically the high plateau region of Puno presents a variable regime of well-differentiated rainfall: a wet season (November–February), a dry season (June–August), and transition periods (September–October and April–May). Due to this, the amount of water available is generally insufficient to cover the daily irrigation needs of high-value Andean crops on the world market, such as Quinoa (Chenopodium Quinoa Willdenow).

Due to the complexity of crop dynamics, their simulation plays a fundamental role in the evaluation of irrigation management strategies [12,13]. AquaCrop is presented as a suitable alternative for this type of crop [14] because it simulates the yield response of herbaceous crops to water and is particularly suitable for conditions in which water is a limiting factor in the production of crops [12].

In the present investigation, the predictive control based on models applied to the irrigation of the Quinoa crop will be evaluated, taking the AquaCrop-OpenSource (AquaCrop-OS) as a plant model and structure Auto Regressive with Exogenous Variables (ARX) as a prediction model. The results obtained were compared with the methods available in the AquaCrop-OS gallery. All simulations were performed in MATLAB.

The article is structured as follows: Section 2 describes the cultivation of quinoa and the characteristics of its irrigation. Section 3 describes the AquaCrop crop simulator, while Section 4 describes the implementation of the model-based predictive controller. Finally, in Sections 5 and 6, the numerical results, analysis, and conclusions are developed.

1.1 The cultivation of quinoa

Chenopodium Quinoa Willdenow, known as Quinoa, is a whole grain, native to the Andes of Bolivia, Chile, and Peru. It is a crop tolerant to abiotic and hydric stress; that is, it requires a small amount of water (200–300 mm) for its vegetative development [15], and it has extraordinary adaptability, in agroecological conditions from sea level to 4,000 m above sea level, being able to withstand temperatures from −4 to 38°C and grow with relative humidity between 40 and 70% [16]. The high Andean phytogeographic domain is characterized by presenting crops adapted to climatic rigor, both due to excessive cold and lack of water, specifically the high plateau region of Puno presents a variable regime of well-differentiated rainfall: a wet season (November–February), a dry season (June–August), and transition periods (September–October and April–May). Due to this, the amount of water available is generally insufficient to cover the daily irrigation needs of Andean crops with high value in the world market, such as Quinoa.

1.2 Irrigation

Quinoa in traditional cultivation presents critical phenological stages or phases of susceptibility and tolerance to the need for irrigation (Figure 1). According to the Puno-based National Institute of Agrarian Innovation (INIA-Puno), Quinoa must be sown in moist soil and must be kept for the first 15 days until germination, with the presence of rain or irrigation (sensitive stage). In the vegetative tolerant phenological branching stage of 15–70 days, quinoa supports the absence of water for up to 70 days. The flowering and milky grain stages are susceptible to the lack or absence of water; that is, between 70 and 120 days the water requirement is essential because they synthesize photosynthates and photoassimilates, which will be assimilated and will translate into the yield of Quinoa, and, in case of absence of rain, must be compensated with irrigation with a frequency of between 5 and 7 days. The last phenological stage of pasty grain and physiological maturation no longer requires water. Tolerant stages are marked with a red stripe and sensitive stages with a green stripe (Figure 1). The complete development of the crop takes place in 180 days.

Figure 1 
                  Critical phases tolerant to the need for irrigation in the cultivation of Quinoa taken from INIA.
Figure 1

Critical phases tolerant to the need for irrigation in the cultivation of Quinoa taken from INIA.

2 Materials and methods

2.1 AquaCrop

The AquaCrop tool is a model that simulates crop growth. It was developed by the Food and Agriculture Organization (FAO) in order to improve water productivity in rainfed and irrigated conditions. It simulates the yield response of arable crops to water and is particularly suitable for conditions where water is a limiting factor in crop production [12]. It has been validated for various crops such as wheat [17] corn [13], quinoa [14], cotton [18,19], sugar cane [11], cassava [20], and potato [21].

It was developed in 2009, and its open-access version AquaCrop-OpenSource (AquaCrop-OS) is presented by FAO [22]. The program introduces crop information according to the various characteristics of climate, type of crop, type of irrigation, soil, and others. The results obtained from crop growth, water balance, water content in the crop, and others can provide solutions for various applications such as developing irrigation programs to optimize production, supporting decision making on water policies, and comparing potential yields and real [12].

2.2 Irrigation methods in AquaCrop-OS

AquaCrop has a gallery of irrigation methods for cultivation, and in AquaCrop-OS, the operation of these methods is encoded in the AOS_Irrigation file and the choice of method is in the IrrigationManagement.txt file. The methods provided are as follows:

  1. Method 0: Rainfed: without irrigation

  2. Method 1: Soil moisture-based: soil moisture is calculated each day, if it is less than a chosen value, irrigate to reach field capacity.

  3. Method 2: Fixed interval: irrigation to saturation in a specified time interval.

  4. Method 3: Specified time series: irrigation is given by a schedule specifying the day and the amount of water.

  5. Method 4: Net calculation: it irrigates every day at field capacity, but this method takes into account the weather forecast and adapts the irrigation to this value.

AquaCrop-OS, being an open-source gray box tool, can incorporate other user-defined irrigation methods.

2.3 Model-based predictive control

The usual MPC approach is described in the following objective function:

(1a) min u ( k ) , , u ( k + N 1 ) i = 0 N 1 y ( k + i 1 ) r ( k + i 1 ) Q 2 + u ( k + i ) R 2 ,

which is subject to:

(1b) y ( k + 1 ) = f ( y ( k ) , u ( k ) , v ( k ) ) ,

(1c) u min u ( k ) u max , k = 0 , , N 1 ,

where y is the control variable, u is the manipulated variable, v is the measurable disturbances, r is the reference, and Q and R correspond to the weight of each term of the cost function.

The function f defines the prediction model of the controller. This problem is solved at each sampling instant. Figure 2 shows the control loop.

Figure 2 
                  Predictive controller loop.
Figure 2

Predictive controller loop.

2.4 Prediction model (ARX)

For the prediction of crop behavior, an ARX structure model or autoregressive model with exogenous input was used. To find the parameters, the method of least squares was used, which allows us to solve linear regression problems analytically and with a unique solution. The ARX model is represented in the form of a differential equation as follows:

(2) A ( z ) y ( k ) = B ( z ) u ( k d ) + e ( k ) ,

where y ( k ) is the system output, u ( k ) is the system input, e ( k ) is the system disturbance, and d is the system delay.

3 Results and discussion

The research was carried out simulating the conditions of Quinoa (Chenopodium Quinoa Willdenow) cultivation in the high Andean phytogeographic domain in the Puno region, Peru. It is located in the southeastern highlands of the country, on the Collao plateau at 13°0066′00″ and 17°17′30″ south latitude and 71°06′57″ and 68°48′46″ west longitude. From the Greenwich meridian, it is located on the plateau between 3,812 and 5,500 m.a.s.l. In the Juli region, agriculture is developed with greater momentum on the shores of Lake Titicaca and the Coata and Ilave hydrographic basins (Figure 3).

Figure 3 
               Location of the research site obtained from Google Earth Engine.
Figure 3

Location of the research site obtained from Google Earth Engine.

For the development of the simulations, the input data were obtained from different sources. Figure 4 shows the climate information of the precipitation between the years 1964 and 2021 is obtained from the national service of hydrology and meteorology (SENAMHI). Likewise, information on the cultivation of Quinoa was obtained from the Ministry of Agriculture (MINAGRI) and the Puno-based National Agricultural Research Institute (INIA), public institutions of the Peruvian government. All of the aforementioned data are part of the inputs to the AquaCrop-OS model in MATLAB. All the simulations implemented were developed in the 2020a version of this programming environment.

Figure 4 
               Average rainfall in the study area, between the years 1964 and 2021, obtained from SENAMHI-Puno meteorological station.
Figure 4

Average rainfall in the study area, between the years 1964 and 2021, obtained from SENAMHI-Puno meteorological station.

3.1 Identification of the model for responding to the water deficit

The data for this experiment was obtained by simulation in AquaCrop-OS before a pseudorandom binary sequence (mm) irrigation input, with the real meteorological data taken from SENAMHI, and with the crop data (obtain from MINAGRI and INIA).

The ARX structure model shown in equation (2) can be rewritten considering a system with multiple inputs and one output as represented by the following equation:

(3) [ 1 + a z 1 ] y ( k ) = [ b 1 b 2 b 3 ] u ( k 1 ) v 1 ( k 1 ) v 2 ( k 1 ) + e ( k ) ,

where y ( k ) is the system output (water deficit), u ( k ) is the system input that can be manipulated (irrigation), v 1 ( k ) ) is the measurable system input (evapotranspiration), v 2 ( k ) is the measurable system input (precipitation), and e ( k ) is the system disturbance.

Normalized mean square error (NMSE) was used as an evaluation criterion, obtaining the best performance in 1989 (NMSE = 5.7212 × 10−4) and the worst in 1984 (NMSE = 0.0164) for the identification experiment. For validation, the value of NMSE is 5.9894 × 10−4 and the value of NMSE is 0.0202, respectively. Table 1 summarizes the results.

Table 1

Summary of results of the identification experiment

Years NMSE NMSE
Identification Validation
Average of 1964 and 2016 0.0035 0.0039
Average of 2017 and 2021 0.0042 0.0047
1989 5.72 × 10−4 5.99 × 10−4
1984 0.0164 0.0202

In the identification of the system with the ARX linear structure, a better fit of the data is observed in years with little rain compared to years with abundant rain. As an example, the Year 1989 (little rain) and the Year 1984 (abundant rain) are shown.

Figures 5 and 6 show the results of the validation experiment for the years 1989 and 1984, respectively.

Figure 5 
                  Model fit and data for data from the Year 1989.
Figure 5

Model fit and data for data from the Year 1989.

Figure 6 
                  Model fit and data for data from the Year 1984.
Figure 6

Model fit and data for data from the Year 1984.

The following parameters were obtained: a = −0.9808, b 1 = −0.5518, b 2 = 0.60810, and b 3 = −0.7516.

3.2 MPC controller

The control variable for this work is the water deficit (mm), and the manipulated variable is irrigation (mm), while the measurable disturbances are evapotranspiration (mm) and precipitation (mm). Figure 7 shows the closed loop implemented in MATLAB that uses the ARX model calculated in the identification experiment to predict the behavior of the system, and this information is taken by the optimizer to calculate the value of the input. The process was simulated by the AquaCrop-OS model (Figure 7).

Figure 7 
                  Predictive controller loop implemented in MATLAB.
Figure 7

Predictive controller loop implemented in MATLAB.

Figure 8 shows the result of the simulation of the MPC controller for a simulation of the Year 2017. The evapotranspiration and precipitation of that year are shown in Figure 9. Table 2 shows the crop yield per hectare (ton/h) and the total irrigation (mm) of the simulated methods.

Figure 8 
                  Comparison of the results of the irrigation methods for the Year 2017.
Figure 8

Comparison of the results of the irrigation methods for the Year 2017.

Figure 9 
                  Precipitation and evapotranspiration in the Year 2017.
Figure 9

Precipitation and evapotranspiration in the Year 2017.

Table 2

Field yield and total irrigation for the Year 2017

Method Yield (Ton/h) Full irrigation (mm)
Method 0 4.04 0
Method 1 4.22 100
Method 2 4.22 293.1
Method 3 4.22 320
Method 4 4.22 115.89
MPC 4.22 48.19

Figure 9 shows the relationship between rainfall and crop evapotranspiration loss; it is evident that on days without rain, the evapotranspiration values are high; these impacts are direct with respect to the water deficit suffered by the crop, and therefore, the irrigation requirement will be higher compared with other days (Figure 8). In the 2017 Quinoa cultivation campaign, using the MPC irrigation method, we obtain the most optimal water requirement and it is recommended between days 90 and 125 (months from November to December) of the season (Table 2).

Figure 10 shows the result of the simulation of the MPC controller for a simulation of the Year 2020. The evapotranspiration and precipitation of that year are shown in Figure 11. Table 3 shows the crop yield per hectare (ton/h) and the total irrigation (mm) of the simulated methods.

Figure 10 
                  Comparison of the results of the irrigation methods for the Year 2020.
Figure 10

Comparison of the results of the irrigation methods for the Year 2020.

Figure 11 
                  Rainfall and evapotranspiration in the Year 2020.
Figure 11

Rainfall and evapotranspiration in the Year 2020.

Table 3

Yield and total irrigation for the Year 2020

Method Yield (Ton/h) Full irrigation (mm)
Method 0 4.92 0
Method 1 4.92 125
Method 2 4.92 279.66
Method 3 4.92 320
Method 4 4.92 109.3
MPC 4.92 26.75

Tables 2 and 3 compare the results of the different irrigation methods. It is observed that the MPC controller presents a better performance taking into account the yield of the field and the total irrigation in a year with little rain as it occurs in the Year 2017. It achieves a performance equal to methods 1–4 with lower consumption of water (Table 2). In method 0, lower water consumption is obtained, but with lower field yield.

Table 3 shows that the MPC controller has the best performance, taking into account the yield of the field and the total irrigation in a year with a lot of rain, such as the one in 2020. It achieves a performance equal to methods 1–4 with less water consumption. Results similar to method 0 are obtained, with a major water consumption.

Figure 11 shows the trends in rainfall data and evapotranspiration for the Quinoa cultivation campaign in 2020, and the behavior is similar to that of the Year 2017 shown in Figure 9; however, the increase in rainwater presents in 2020, and it makes irrigation requirements minimal. Using the MPC irrigation method for this scenario, it will also be optimal and the irrigation recommendations would be between days 80 and 95 (months from October to November) of the season (Table 3).

4 Conclusion

In this work, the problem of linear identification and MPC control of water deficit applied in a Quinoa (Chenopodium Quinoa Willdenow) crop model using AquaCrop-OS is presented. An ARX structure with multiple inputs and one output is proposed as a prediction model.

The structure proposed for the prediction model (ARX) presents a good adjustment for years with little rainfall, and its prediction capacity is lower for rainy years.

The proposed irrigation methodology presents the best performance among the simulated methods, both for rainy and dry years. This work aims to shed light for new studies and proposals that delve into the use of simulators to improve irrigation techniques in high Andean crop areas.

Regarding the link between MPC irrigation and crop yield, we can conclude that Quinoa is a drought-tolerant crop and that water yield has relative effects. For seasons with a rainy year (See Table 3), it reaches its maximum yield, there is no water deficit, so it does not require irrigation. In seasons with a dry year (See Table 2), the yield has increased with MPC irrigation, completing the need for crop water.

Acknowledgements

The results presented are part of the doctoral research project sponsored by the National Fund for Scientific, Technological Development, and Technological Innovation (FONDECYT), nowadays PROCIENCIA, one of the ten doctoral programs subsidized by PROCIENCIA in Peru – Contract 04-2018-FONDECYT/BM. The authors, therefore, acknowledge with thanks DSR for technical and financial support.

  1. Funding information: This research has been financed by Concytec – World Bank Project “Improvement and Expansion of the Services of the National System of Science, Technology and Technological Innovation” 8682-PE, through its executing unit ProCiencia [contract 04-2018-FONDECYT/BM].

  2. Author contributions: Iván Beltrán Ccama – conceptualization, data curation, formal analysis, funding acquisition, investigation, visualization, writing-original draft; Bruno Faccini Santoro – methodology, project administration, supervision, validation, writing-review and editing; José Oliden Semino – software, resources.

  3. Conflict of interest: The authors declare no conflict of interest.

  4. Ethical approval: The conducted research is not related to either human or animal use.

  5. Data availability statement: All data generated or analyzed during this study are included in this published article.

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Received: 2022-08-20
Revised: 2022-11-30
Accepted: 2022-12-04
Published Online: 2022-12-31

© 2022 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  54. Theoretical investigations on the excited-state intramolecular proton transfer in the solvated 2-hydroxy-1-naphthaldehyde carbohydrazone
  55. Mechanical and gamma-ray shielding examinations of Bi2O3–PbO–CdO–B2O3 glass system
  56. Machine learning-based forecasting of potability of drinking water through adaptive boosting model
  57. The potential effect of the Rumex vesicarius water seeds extract treatment on mice before and during pregnancy on the serum enzymes and the histology of kidney and liver
  58. Impact of benzimidazole functional groups on the n-doping properties of benzimidazole derivatives
  59. Extraction of red pigment from Chinese jujube peel and the antioxidant activity of the pigment extracts
  60. Flexural strength and thermal properties of carbon black nanoparticle reinforced epoxy composites obtained from waste tires
  61. A focusing study on radioprotective and antioxidant effects of Annona muricata leaf extract in the circulation and liver tissue: Clinical and experimental studies
  62. Clinical comprehensive and experimental assessment of the radioprotective effect of Annona muricata leaf extract to prevent cellular damage in the ileum tissue
  63. Effect of WC content on ultrasonic properties, thermal and electrical conductivity of WC–Co–Ni–Cr composites
  64. Influence of various class cleaning agents for prosthesis on Co–Cr alloy surface
  65. The synthesis of nanocellulose-based nanocomposites for the effective removal of hexavalent chromium ions from aqueous solution
  66. Study on the influence of physical interlayers on the remaining oil production under different development modes
  67. Optimized linear regression control of DC motor under various disturbances
  68. Influence of different sample preparation strategies on hypothesis-driven shotgun proteomic analysis of human saliva
  69. Determination of flow distance of the fluid metal due to fluidity in ductile iron casting by artificial neural networks approach
  70. Investigation of mechanical activation effect on high-volume natural pozzolanic cements
  71. In vitro: Anti-coccidia activity of Calotropis procera leaf extract on Eimeria papillata oocysts sporulation and sporozoite
  72. Determination of oil composition of cowpea (Vigna unguiculata L.) seeds under influence of organic fertilizer forms
  73. Activated partial thromboplastin time maybe associated with the prognosis of papillary thyroid carcinoma
  74. Treatment of rat brain ischemia model by NSCs-polymer scaffold transplantation
  75. Lead and cadmium removal with native yeast from coastal wetlands
  76. Characterization of electroless Ni-coated Fe–Co composite using powder metallurgy
  77. Ferrate synthesis using NaOCl and its application for dye removal
  78. Antioxidant, antidiabetic, and anticholinesterase potential of Chenopodium murale L. extracts using in vitro and in vivo approaches
  79. Study on essential oil, antioxidant activity, anti-human prostate cancer effects, and induction of apoptosis by Equisetum arvense
  80. Experimental study on turning machine with permanent magnetic cutting tool
  81. Numerical simulation and mathematical modeling of the casting process for pearlitic spheroidal graphite cast iron
  82. Design, synthesis, and cytotoxicity evaluation of novel thiophene, pyrimidine, pyridazine, and pyridine: Griseofulvin heterocyclic extension derivatives
  83. Isolation and identification of promising antibiotic-producing bacteria
  84. Ultrasonic-induced reversible blood–brain barrier opening: Safety evaluation into the cellular level
  85. Evaluation of phytochemical and antioxidant potential of various extracts from traditionally used medicinal plants of Pakistan
  86. Effect of calcium lactate in standard diet on selected markers of oxidative stress and inflammation in ovariectomized rats
  87. Identification of crucial salivary proteins/genes and pathways involved in pathogenesis of temporomandibular disorders
  88. Zirconium-modified attapulgite was used for removing of Cr(vi) in aqueous solution
  89. The stress distribution of different types of restorative materials in primary molar
  90. Reducing surface heat loss in steam boilers
  91. Deformation behavior and formability of friction stir processed DP600 steel
  92. Synthesis and characterization of bismuth oxide/commercial activated carbon composite for battery anode
  93. Phytochemical analysis of Ziziphus jujube leaf at different foliar ages based on widely targeted metabolomics
  94. Effects of in ovo injection of black cumin (Nigella sativa) extract on hatching performance of broiler eggs
  95. Separation and evaluation of potential antioxidant, analgesic, and anti-inflammatory activities of limonene-rich essential oils from Citrus sinensis (L.)
  96. Bioactivity of a polyhydroxy gorgostane steroid from Xenia umbellata
  97. BiCAM-based automated scoring system for digital logic circuit diagrams
  98. Analysis of standard systems with solar monitoring systems
  99. Structural and spectroscopic properties of voriconazole and fluconazole – Experimental and theoretical studies
  100. New plant resistance inducers based on polyamines
  101. Experimental investigation of single-lap bolted and bolted/bonded (hybrid) joints of polymeric plates
  102. Investigation of inlet air pressure and evaporative cooling of four different cogeneration cycles
  103. Review Articles
  104. Comprehensive review on synthesis, physicochemical properties, and application of activated carbon from the Arecaceae plants for enhanced wastewater treatment
  105. Research progress on speciation analysis of arsenic in traditional Chinese medicine
  106. Recent modified air-assisted liquid–liquid microextraction applications for medicines and organic compounds in various samples: A review
  107. An insight on Vietnamese bio-waste materials as activated carbon precursors for multiple applications in environmental protection
  108. Antimicrobial activities of the extracts and secondary metabolites from Clausena genus – A review
  109. Bioremediation of organic/heavy metal contaminants by mixed cultures of microorganisms: A review
  110. Sonodynamic therapy for breast cancer: A literature review
  111. Recent progress of amino acid transporters as a novel antitumor target
  112. Aconitum coreanum Rapaics: Botany, traditional uses, phytochemistry, pharmacology, and toxicology
  113. Corrigendum
  114. Corrigendum to “Petrology and geochemistry of multiphase post-granitic dikes: A case study from the Gabal Serbal area, Southwestern Sinai, Egypt”
  115. Corrigendum to “Design of a Robust sliding mode controller for bioreactor cultures in overflow metabolism via an interdisciplinary approach”
  116. Corrigendum to “Statistical analysis on the radiological assessment and geochemical studies of granite rocks in the north of Um Taghir area, Eastern Desert, Egypt”
  117. Corrigendum to “Aroma components of tobacco powder from different producing areas based on gas chromatography ion mobility spectrometry”
  118. Corrigendum to “Mechanical properties, elastic moduli, transmission factors, and gamma-ray-shielding performances of Bi2O3–P2O5–B2O3–V2O5 quaternary glass system”
  119. Erratum
  120. Erratum to “Copper(ii) complexes supported by modified azo-based ligands: Nucleic acid binding and molecular docking studies”
  121. Special Issue on Applied Biochemistry and Biotechnology (ABB 2021)
  122. Study of solidification and stabilization of heavy metals by passivators in heavy metal-contaminated soil
  123. Human health risk assessment and distribution of VOCs in a chemical site, Weinan, China
  124. Preparation and characterization of Sparassis latifolia β-glucan microcapsules
  125. Special Issue on the Conference of Energy, Fuels, Environment 2020
  126. Improving the thermal performance of existing buildings in light of the requirements of the EU directive 2010/31/EU in Poland
  127. Special Issue on Ethnobotanical, Phytochemical and Biological Investigation of Medicinal Plants
  128. Study of plant resources with ethnomedicinal relevance from district Bagh, Azad Jammu and Kashmir, Pakistan
  129. Studies on the chemical composition of plants used in traditional medicine in Congo
  130. Special Issue on Applied Chemistry in Agriculture and Food Science
  131. Strip spraying technology for precise herbicide application in carrot fields
  132. Special Issue on Pharmacology and Metabolomics of Ethnobotanical and Herbal Medicine
  133. Phytochemical profiling, antibacterial and antioxidant properties of Crocus sativus flower: A comparison between tepals and stigmas
  134. Antioxidant and antimicrobial properties of polyphenolics from Withania adpressa (Coss.) Batt. against selected drug-resistant bacterial strains
  135. Integrating network pharmacology and molecular docking to explore the potential mechanism of Xinguan No. 3 in the treatment of COVID-19
  136. Chemical composition and in vitro and in vivo biological assortment of fixed oil extracted from Ficus benghalensis L.
  137. A review of the pharmacological activities and protective effects of Inonotus obliquus triterpenoids in kidney diseases
  138. Ethnopharmacological study of medicinal plants in Kastamonu province (Türkiye)
  139. Protective effects of asperuloside against cyclophosphamide-induced urotoxicity and hematotoxicity in rats
  140. Special Issue on Essential Oil, Extraction, Phytochemistry, Advances, and Application
  141. Identification of volatile compounds and antioxidant, antibacterial, and antifungal properties against drug-resistant microbes of essential oils from the leaves of Mentha rotundifolia var. apodysa Briq. (Lamiaceae)
  142. Phenolic contents, anticancer, antioxidant, and antimicrobial capacities of MeOH extract from the aerial parts of Trema orientalis plant
  143. Chemical composition and antimicrobial activity of essential oils from Mentha pulegium and Rosmarinus officinalis against multidrug-resistant microbes and their acute toxicity study
  144. Special Issue on Marine Environmental Sciences and Significance of the Multidisciplinary Approaches
  145. An insightful overview of the distribution pattern of polycyclic aromatic hydrocarbon in the marine sediments of the Red Sea
  146. Antifungal–antiproliferative norcycloartane-type triterpenes from the Red Sea green alga Tydemania expeditionis
  147. Solvent effect, dipole moment, and DFT studies of multi donor–acceptor type pyridine derivative
  148. An extensive assessment on the distribution pattern of organic contaminants in the aerosols samples in the Middle East
  149. Special Issue on 4th IC3PE
  150. Energetics of carboxylic acid–pyridine heterosynthon revisited: A computational study of intermolecular hydrogen bond domination on phenylacetic acid–nicotinamide cocrystals
  151. A review: Silver–zinc oxide nanoparticles – organoclay-reinforced chitosan bionanocomposites for food packaging
  152. Green synthesis of magnetic activated carbon from peanut shells functionalized with TiO2 photocatalyst for Batik liquid waste treatment
  153. Coagulation activity of liquid extraction of Leucaena leucocephala and Sesbania grandiflora on the removal of turbidity
  154. Hydrocracking optimization of palm oil over NiMoO4/activated carbon catalyst to produce biogasoline and kerosine
  155. Special Issue on Pharmacology and metabolomics of ethnobotanical and herbal medicine
  156. Cynarin inhibits PDGF-BB-induced proliferation and activation in hepatic stellate cells through PPARγ
  157. Special Issue on The 1st Malaysia International Conference on Nanotechnology & Catalysis (MICNC2021)
  158. Surfactant evaluation for enhanced oil recovery: Phase behavior and interfacial tension
  159. Topical Issue on phytochemicals, biological and toxicological analysis of aromatic medicinal plants
  160. Phytochemical analysis of leaves and stems of Physalis alkekengi L. (Solanaceae)
  161. Phytochemical and pharmacological profiling of Trewia nudiflora Linn. leaf extract deciphers therapeutic potentials against thrombosis, arthritis, helminths, and insects
  162. Pergularia tomentosa coupled with selenium nanoparticles salvaged lead acetate-induced redox imbalance, inflammation, apoptosis, and disruption of neurotransmission in rats’ brain
  163. Protective effect of Allium atroviolaceum-synthesized SeNPs on aluminum-induced brain damage in mice
  164. Mechanism study of Cordyceps sinensis alleviates renal ischemia–reperfusion injury
  165. Plant-derived bisbenzylisoquinoline alkaloid tetrandrine prevents human podocyte injury by regulating the miR-150-5p/NPHS1 axis
  166. Network pharmacology combined with molecular docking to explore the anti-osteoporosis mechanisms of β-ecdysone derived from medicinal plants
  167. Chinese medicinal plant Polygonum cuspidatum ameliorates silicosis via suppressing the Wnt/β-catenin pathway
  168. Special Issue on Advanced Nanomaterials for Energy, Environmental and Biological Applications - Part I
  169. Investigation of improved optical and conductivity properties of poly(methyl methacrylate)–MXenes (PMMA–MXenes) nanocomposite thin films for optoelectronic applications
  170. Special Issue on Applied Biochemistry and Biotechnology (ABB 2022)
  171. Model predictive control for precision irrigation of a Quinoa crop
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