The hydro nutrient management (HNM) for crop yield is effectively improved using proposed system. A hydro-crop growth prediction system (HCGPS) is designed using machine learning. The reconfigurable nutrients uptake crop yield prediction rate is enhanced. This proposed HCGPS is used to predict the crop yield by considering input parameters such as nutrient index (NI), electric conductivity limit (ECL), ion concentration factors (ICF) and dry weight of the crop and crop yield rate (CYR) to analyze the positive and negative correlation with crop growth. The proposed system is used to find correlation Index of input and output parameters to determine the prediction rate of crop yield. The proposed design improves smart prediction rate and efficiency of crop growth rate with optimal utilization of input variables. This proposed HCGPS is very helpful to achieve good quality yield with optimal utilization of input parameters.
Predictive fertilization models for potato crops using machine learning techn...IJECEIAES
Given the influence of several factors, including weather, soils, land management, genotypes, and the severity of pests and diseases, prescribing adequate nutrient levels is difficult. A potato’s performance can be predicted using machine learning techniques in cases when there is enough data. This study aimed to develop a highly precise model for determining the optimal levels of nitrogen, phosphorus, and potassium required to achieve both high-quality and high-yield potato crops, taking into account the impact of various environmental factors such as weather, soil type, and land management practices. We used 900 field experiments from Kaggle as part of a data set. We developed, evaluated, and compared prediction models of k-nearest neighbor (KNN), linear support vector machine (SVM), naive Bayes (NB) classifier, decision tree (DT) regressor, random forest (RF) regressor, and eXtreme gradient boosting (XGBoost). We used measures such as mean average error (MAE), mean squared error (MSE), R-Squared (RS), and R 2 Root mean squared error (RMSE) to describe the model’s mistakes and prediction capacity. It turned out that the XGBoost model has the greatest R 2 , MSE and MAE values. Overall, the XGBoost model outperforms the other machine learning models. In the end, we suggested a hardware implementation to help farmers in the field.
Food is a basic necessity of life. It is the means by which man is nourished and strengthened to carry out his daily activities. The need for food for the upkeep of man has placed agriculture at the helm of man’s affairs on earth. With a rapidly increasing population on earth, man has invented newer and innovative ways to cultivate crops. This cultivation is mainly concentrated in rural areas of countries around the world; but with the massive urbanization happening in the world today; it is becoming increasingly difficult to have enough agricultural produce that will cater for the massive population. Taking Nigeria as a case study, the increased urbanization has placed a massive demand on land, energy and water resources within urban areas of the country. Majority of the food consumed in the urban areas is cultivated in the rural areas. This system however requires longer transportation times from rural areas to urban areas which lead to contamination and spoilage in many instances. This research paper provides a solution in which food crops can be cultivated easily in urban areas by planting in vertically stacked layers in order to save space and use minimal energy and water for irrigation.
Predictive fertilization models for potato crops using machine learning techn...IJECEIAES
Given the influence of several factors, including weather, soils, land management, genotypes, and the severity of pests and diseases, prescribing adequate nutrient levels is difficult. A potato’s performance can be predicted using machine learning techniques in cases when there is enough data. This study aimed to develop a highly precise model for determining the optimal levels of nitrogen, phosphorus, and potassium required to achieve both high-quality and high-yield potato crops, taking into account the impact of various environmental factors such as weather, soil type, and land management practices. We used 900 field experiments from Kaggle as part of a data set. We developed, evaluated, and compared prediction models of k-nearest neighbor (KNN), linear support vector machine (SVM), naive Bayes (NB) classifier, decision tree (DT) regressor, random forest (RF) regressor, and eXtreme gradient boosting (XGBoost). We used measures such as mean average error (MAE), mean squared error (MSE), R-Squared (RS), and R 2 Root mean squared error (RMSE) to describe the model’s mistakes and prediction capacity. It turned out that the XGBoost model has the greatest R 2 , MSE and MAE values. Overall, the XGBoost model outperforms the other machine learning models. In the end, we suggested a hardware implementation to help farmers in the field.
Food is a basic necessity of life. It is the means by which man is nourished and strengthened to carry out his daily activities. The need for food for the upkeep of man has placed agriculture at the helm of man’s affairs on earth. With a rapidly increasing population on earth, man has invented newer and innovative ways to cultivate crops. This cultivation is mainly concentrated in rural areas of countries around the world; but with the massive urbanization happening in the world today; it is becoming increasingly difficult to have enough agricultural produce that will cater for the massive population. Taking Nigeria as a case study, the increased urbanization has placed a massive demand on land, energy and water resources within urban areas of the country. Majority of the food consumed in the urban areas is cultivated in the rural areas. This system however requires longer transportation times from rural areas to urban areas which lead to contamination and spoilage in many instances. This research paper provides a solution in which food crops can be cultivated easily in urban areas by planting in vertically stacked layers in order to save space and use minimal energy and water for irrigation.
The global agriculture system faces significant challenges in meeting the
growing demand for food production, particularly given projections that the
world's population will reach 70% by 2050. Hydroponic farming is an
increasingly popular technique in this field, offering a promising solution to
these challenges. This paper will present the improvement of the current
traditional hydroponic method by providing a system that can be used to
monitor and control the important element in order to help the plant grow up
smoothly. This proposed system is quite efficient and user-friendly that can
be used by anyone. This is a combination of a traditional hydroponic system,
an automatic control system and a smartphone. The primary objective is to
develop a smart system capable of monitoring and controlling potential
hydrogen (pH) levels, a key factor that affects hydroponic plant growth.
Ultimately, this paper offers an alternative approach to address the challenges
of the existing agricultural system and promote the production of clean,
disease-free, and healthy food for a better future.
Internet of things based automated monitoring for indoor aeroponic systemIJECEIAES
On the ever-rising urgency of global food security, efforts are required to develop a robust farming technique. This includes the capability of farming in non-agricultural land or indoor spaces. Farming in the air medium, i.e., aeroponic, has persistently stepped in as a viable solution. Aeroponic farming allows efficient water usage while preventing soil related diseases and pests. With the assistance of light emitting diodes (LEDs) and precise electronic monitoring and control, aeroponic may become the suitable farming technique of the future. This work presents an aeroponic system capable of automated monitoring and control of farming parameters. The system achieved both robustness in indoor farming and remote access by employing LED as an artificial lighting system and the internet-of-things (IoT) connectivity, respectively. The test result demonstrated that the system successfully maintained the root chamber temperature below 30 °C with a typical average temperature of 28.8 °C. The system managed a humidity level which prevented plants from drying out. It was also evident that the LED assistance significantly improved the growth quality of Ipomea reptans. The system, data, and analysis presented in this work is expected to facilitate further development of a robust food production system in overcoming the global food crisis.
Global custom-tailored machine learning of soil water content for locale spec...Agriculture Journal IJOEAR
Abstract—A novel approach to irrigation modeling is presented: the locale specific machine learning of soil moisture data. The merits of this new patent pending technique are clear when compared to existing methods, such as the AquaCrop program created by the Food and Agricultural Organization (FAO). From a case study on the comparative performance of AquaCrop and machine learning in the extrapolative modeling of soil moisture, AquaCrop performed with a mean squared error of 0.00165 whereas the machine learning received 0.00013, an order of magnitude lower. In addition, a novel algorithm, the ConserWater™ algorithm, has been created for the purpose of machine learning soil moisture with accuracy and efficiency. The performance of the algorithm is very superior when compared to other popular machine learning techniques, as applied to soil moisture. Finally, to allow this technology to reach agriculturalists at the grassroots level, the entire world has been machine learned and the resultant models have been encapsulated into a lightweight easy-to-use smartphone application.
Microcontroller based adaptive irrigation system using wsn for variety crops ...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
More Related Content
Similar to An efficient hydro-crop growth prediction system for nutrient analysis using machine learning algorithm
The global agriculture system faces significant challenges in meeting the
growing demand for food production, particularly given projections that the
world's population will reach 70% by 2050. Hydroponic farming is an
increasingly popular technique in this field, offering a promising solution to
these challenges. This paper will present the improvement of the current
traditional hydroponic method by providing a system that can be used to
monitor and control the important element in order to help the plant grow up
smoothly. This proposed system is quite efficient and user-friendly that can
be used by anyone. This is a combination of a traditional hydroponic system,
an automatic control system and a smartphone. The primary objective is to
develop a smart system capable of monitoring and controlling potential
hydrogen (pH) levels, a key factor that affects hydroponic plant growth.
Ultimately, this paper offers an alternative approach to address the challenges
of the existing agricultural system and promote the production of clean,
disease-free, and healthy food for a better future.
Internet of things based automated monitoring for indoor aeroponic systemIJECEIAES
On the ever-rising urgency of global food security, efforts are required to develop a robust farming technique. This includes the capability of farming in non-agricultural land or indoor spaces. Farming in the air medium, i.e., aeroponic, has persistently stepped in as a viable solution. Aeroponic farming allows efficient water usage while preventing soil related diseases and pests. With the assistance of light emitting diodes (LEDs) and precise electronic monitoring and control, aeroponic may become the suitable farming technique of the future. This work presents an aeroponic system capable of automated monitoring and control of farming parameters. The system achieved both robustness in indoor farming and remote access by employing LED as an artificial lighting system and the internet-of-things (IoT) connectivity, respectively. The test result demonstrated that the system successfully maintained the root chamber temperature below 30 °C with a typical average temperature of 28.8 °C. The system managed a humidity level which prevented plants from drying out. It was also evident that the LED assistance significantly improved the growth quality of Ipomea reptans. The system, data, and analysis presented in this work is expected to facilitate further development of a robust food production system in overcoming the global food crisis.
Global custom-tailored machine learning of soil water content for locale spec...Agriculture Journal IJOEAR
Abstract—A novel approach to irrigation modeling is presented: the locale specific machine learning of soil moisture data. The merits of this new patent pending technique are clear when compared to existing methods, such as the AquaCrop program created by the Food and Agricultural Organization (FAO). From a case study on the comparative performance of AquaCrop and machine learning in the extrapolative modeling of soil moisture, AquaCrop performed with a mean squared error of 0.00165 whereas the machine learning received 0.00013, an order of magnitude lower. In addition, a novel algorithm, the ConserWater™ algorithm, has been created for the purpose of machine learning soil moisture with accuracy and efficiency. The performance of the algorithm is very superior when compared to other popular machine learning techniques, as applied to soil moisture. Finally, to allow this technology to reach agriculturalists at the grassroots level, the entire world has been machine learned and the resultant models have been encapsulated into a lightweight easy-to-use smartphone application.
Microcontroller based adaptive irrigation system using wsn for variety crops ...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
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Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
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An efficient hydro-crop growth prediction system for nutrient analysis using machine learning algorithm
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 6, December 2023, pp. 6681~6690
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i6.pp6681-6690 6681
Journal homepage: http://ijece.iaescore.com
An efficient hydro-crop growth prediction system for nutrient
analysis using machine learning algorithm
Chandana Chikkasiddaiah1,2
, Parthasarathy Govindaswamy2
, Mallikarjunaswamy Srikantaswamy3
1
Master of Computer Applications, JSS Academy of Technical Education, Bengaluru, India
2
School of Computing and Information Technology, REVA University, Bengaluru, India
3
Electronics and Communication Engineering, JSS Academy of Technical Education, Bengaluru, India
Article Info ABSTRACT
Article history:
Received Jan 28, 2023
Revised Mar 12, 2023
Accepted Apr 7, 2023
The hydro nutrient management (HNM) for crop yield is effectively improved
using proposed system. A hydro-crop growth prediction system (HCGPS) is
designed using machine learning. The reconfigurable nutrients uptake crop
yield prediction rate is enhanced. This proposed HCGPS is used to predict the
crop yield by considering input parameters such as nutrient index (NI), electric
conductivity limit (ECL), ion concentration factors (ICF) and dry weight of
the crop and crop yield rate (CYR) to analyze the positive and negative
correlation with crop growth. The proposed system is used to find correlation
Index of input and output parameters to determine the prediction rate of crop
yield. The proposed design improves smart prediction rate and efficiency of
crop growth rate with optimal utilization of input variables. This proposed
HCGPS is very helpful to achieve good quality yield with optimal utilization
of input parameters.
Keywords:
Crop yield rate
Dry weight
Electric conductivity limit
Hydro nutrient management
Nutrient index
This is an open access article under the CC BY-SA license.
Corresponding Author:
Chandana Chikkasiddaiah
Master of Computer Applications, JSS Academy of Technical Education
Bengaluru, Karnataka, India
Email: punyachandu@gmail.com
1. INTRODUCTION
Hydro nutrient management is a soilless technique to nurture yields in water. Such zero-soil farming
way characterizes kind of an outstanding chance for the cultivation sphere, particularly in fields connecting
with trials like uncontrollable soil deprivation and controlled water bases. Additionally, such farming practice
proves outstanding consequences concerning an atmosphere and user responsive agriculture. It is similarly a
consistent tool for the forthcoming trials in food security. Numerous areas of the world which face trials like
volatile weather forms, extreme heat, condensed space obtainability, are captivating hydro nutrient
management (HNM) as a substitute method to agriculture and a probable answer for such difficulties. The
global marketable HNM business has increased four-five-fold in the preceding 10 years and is presently
estimated amid 21k and 26k hectares with a farm gate value of US $5.9 to $7.9 billion. As a consequence,
agriculture perceives are quickly shifting to smarter and accuracy agriculture observes for enhanced crop
harvests and commercial gains. With the novel age of big data, approaches like machine learning (ML) and
artificial intelligence (AI) available, data can be used to analyze, yield patterns and make forecasts [1], [2].
Depending on the above concerns investigators have advised and exasperated to overcome such
breaches by uniting machine HNM agriculture can be mainly estranged to 3 types: nutrient film technique (NFT),
Aquaponics and Deep Flow method. The NFT is 1 of the hydroponic ways such that a thin torrent of water having
the compulsory dissolved nutrients otherwise known as NS is used that is an upright standard for development of
the plant. This one is re-disseminated in the plants roots in the pipelines that are watertight, furthermore called
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 6681-6690
6682
otherwise as channels. In this scheme, NS is augmented with substances such as rock wool, sand that is
conceded and re- disseminated inside a slope containing of plants positioned in a plastic furrow [3], [4].
This delivers the finest quantity of nutrients through its root system to the plant. HNM can also be called
as a capable apparatus in the zones having restricted land assets. It bids main benefits upon the age-old soil farming
and additional agriculture approaches. Low cost of installment, tranquil operation, supervision and actual
utilization of nutrients and recycling of water are few of the motives that has made it a prevalent option [5].
The area of HNM is comparatively new and coalescing it with progressing tools and calculation can
give cost and time effective resolutions. Few restrictions inside the zone of HNM contain the absence of:
i) active supervision of nutrients castoff in the hydroponic harvests water; ii) comprehensive investigation and
learning of factors which endorse quick development with decent quality harvests; and iii) relaxed and clever
ways, wherein mechanization procedures by means of ML can aid us in forecasting education methods with
HNM.
Diverse kinds of algorithms are castoff in order to project and forecast precise standards of the factors
convoluted throughout the development procedure of the yields. This will improve the effectiveness and make
HNM agriculture a relaxed and smart method to exploit yields. Hydroponic nutrient management is the practice
of supplying plants with the necessary nutrients they need to grow in a hydroponic system. Hydroponics is a
method of growing plants without soil, where plants are instead grown in a nutrient-rich water solution. Proper
nutrient management is crucial for ensuring optimal plant growth, health, and productivity in hydroponic systems.
2. RELATED WORK
The research work [6], [7] explores the submission of fuzzy algorithm Mamdani technique to forecast
the hydroponic watercress expansion. The idea in this effort is to utilize the fuzzy inference system (FIS) that
is fundamentally a calculating system which functions on the fuzzy reasoning principle and a human reasoning
similitude to forecast harvest development. Shan et al. [8] projected a scheme to assess the development of the
plant by means of internet of things (IoT) and a logic of fuzzy to preserve finest water supply and levels of
nutrients. The project is active when likened to the old-style approaches cast-off in HNM to assess the water
and nutrient necessities throughout the lifespan of the plant. When we see research [9], neural networks which
is artificial were pragmatic to a prototype which forecasts standards of electric conductivity (EC) and pH of
the scheme. Such forecasts remain highly significant as they are needed by supplementary intellectual schemes
which safeguard the correct and best process of the hydroponic system. This effort likewise attaches AI and
ML with hydroponic systems that unlocks the technique to additional progression and education of clever
systems in the arena of HNM which would lead to extra accurate farming [10].
3. PROPOSED HYDRO-CROP GROWTH PREDICTION SYSTEM
The planned system depicted in Figure 1, portrays the projected framework for forecast of complete
crop yield rate (CYR) by means of ML [11]. The stages trailed are data congregation that comprises water
sample investigation, which experiences the procedure of ion chromatography, to find ion concentrations. The
results coming through the data congregation and analytics phases would be served to the ML algorithm to
descend forecast consequences. The whole procedure is explained in three segments: design architecture,
forecast system, flowchart of the scheme [12].
Figure 1. Fundamental block diagram of HNM system overview
3. Int J Elec & Comp Eng ISSN: 2088-8708
An efficient hydro-crop growth prediction system for nutrient analysis … (Chandana Chikkasiddaiah)
6683
3.1. System plan architecture
The whole arrangement is alienated into 3 chief portions: data fetching, analytics and forecast
consequences. The scheme is not rigid and can be utilized for yields appropriate for NFT. The harvests of
tomatos stayed cultured with a soilless NFT, a re-disseminated close scheme. Here, we deliberate samples of
tap water for examination though rain or recycled water can also be utilized in NFT. The temperature least
assessment was kept at 11.9 °
C, and the temperature of ventilation was fixed to 19.9 °
C. The nutrient solution
(NS) resolution could be accepted premixed depending on magnitude necessities [13], [14]. Each two days, all
cistern was replenished with renewed NS till the early volume. We also observe that the quantity of nutrients,
filths or salts inside the water, is raised to the limit of EC. In order to preserve finest levels of EC, researchers
fix the EC to the following points (5.1, 7.6 and 10.1 dS·m−1
).
EC-dependent nutrient regulator for HNM harvest farming might deliver quantities of nutrients which
are not in ideal quantities for the plants, hence cannot be cast-off unswervingly to find the ions concentration.
A procedure of ion chromatography Dionex model DX500; was utilized to show the complete liquefied ion
absorptions in the resolution [15], [16]. The info gave the standards for the ion concentration with in the several
portions of the tomatos that is sectioned into the fruits, roots leaf, stem. A least temperature of 12.1 °
C stood
fixed and temperature of ventilation was 20.1 °
C having a comparative humidity that remained continuously
upper than 50.1%, excluding for two days, where it touched 44.1%. Once in 15 days, lone plant from every trial
field was engaged for research. The density of the plant was witnessed to be 3.31 plants m−2
.
The parameters that were checked comprised complete plants dry matter weight along with EC limit,
NI ingestion, total CYR complete Nitrogen content of fruits and leaves, the ion absorptions for each organ
remained alienated as per their origin. Such particulars are utilized for the numerical data examination and the
highly correlated parameters shall be nourished into the ML algorithm to forecast a yield for the goal parameter.
Meanwhile the information is analyzed at every phase, it would assist in computing the elements concentration
hooked on to the NI and the plants composition of minerals on an everyday scenario [17], [18].
3.2. The dataset comprises
The whole dry matter load of the tomatos and dry weights of the dissimilar portions ingesting. NS
fresh plant, NI added plant and finally the NI remaining of the plant which is absent through the cycle and
towards the conclusion of it was measured, endorsement of inorganic cations, specifically magnesium (Mg),
sodium (Na), potassium (K), calcium (Ca) split for the different portions and is shown in gm L-1
d-1
.
3.3. Proposed prediction model
The prediction share of the scheme design is dependent on the ground of utilizing ML methods to
assess the complete plants CYR, ‘xx’ days forward in stint. The prototype targets to forecast the complete CYR
yield dependent over factors like: plants dry weight matter, NI, ion approvals of the fruit models prior and
subsequent yield, EC limit and whole nitrogen factor of the fruit. Overall CYR of the harvest could be computed
by the agreed formula [19].
𝑃𝑟𝑜𝑝𝑜𝑠𝑒𝑑 crop yield rate =
𝑋2− 𝑋1
𝜏2−𝜏1
(1)
where 𝑋1 and 𝑋2 is represents dry weights of the tomatos in grams with respect to the 𝜏1 and 𝜏2 days. Year
wise total crop yield prediction (𝐶𝑓)=∑ 𝑋𝑖
𝐴
− 𝑋𝑖
𝑃
12
𝑖=0 . Where 𝑋𝑖
𝐴
is represented the actual crop yield with respect
to month wise, 𝑋𝑖
𝑃
is described as the prediction crop yield with respect to month wise and i is identified as
number of months. Five-year total crop yield prediction (𝐶𝑓𝑓)=∑ 𝐶𝑓𝑗
𝐴
− 𝐶𝑓𝑗
𝑝
5
𝑗=0 . Where 𝐶𝑓𝑗
𝐴
is represented the
five wise actual crop yield, 𝐶𝑓𝑗
𝑝
is described the five wise prediction crop yield and j represented the number
years. Year wise total fertilizer utilization input parameters (𝐹𝑢)=∑ 𝐶𝑎𝑖
𝐴
+ 𝑀𝑔𝑖
𝐴
+ 𝐾𝑖
𝐴
+ 𝑁𝑎𝑖
𝐴
−
12
𝑖=0 𝐶𝑎𝑖
𝑃
−
𝑀𝑔𝑖
𝑃
− 𝐾𝑖
𝑃
− 𝑁𝑎𝑖
𝑃
. Where 𝐶𝑎𝑖
𝐴
is represented the year wise actual calcium ion, 𝑀𝑔𝑖
𝐴
is described as the year
wise actual magnesium ion, 𝐾𝑖
𝐴
is identified as actual potassium ion, 𝑁𝑎𝑖
𝐴
is described as the year wise actual
sodium ion, 𝐶𝑎𝑖
𝑃
is represented the year wise prediction calcium ion, 𝑀𝑔𝑖
𝑃
is described as the year wise
prediction magnesium ion, 𝐾𝑖
𝑃
is identified as prediction potassium ion and 𝑁𝑎𝑖
𝑃
is described as the year wise
prediction sodium ion. Five-year wise fertilizer utilization 𝐹𝑢𝑓 = ∑ 𝐹𝑢
𝐴
− 𝐹𝑢
𝑃
5
𝑗=0 . Where 𝐹𝑢
𝐴
is described the
five wise actual fertilizer utilization and 𝐹𝑢
𝑃
is identified as the five wise predicted fertilizer utilization.
3.4. System flowchart
The below phases define the method in more aspect and are revealed in Figure 2.
Phase 1: Gathering water trials for examination.
Phase 2: Setup and alteration system factors as per (tomatos) harvest needs EC limit range led lights range
410-710 nm and NI injection.
4. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 6681-6690
6684
Phase 3: In order to find ion concentration, ion chromatography is utilized.
Phase 4: Gathering for the analysis of nutrient data by enchanting input parameters such as ion concentration,
EC limit, NI, nitrogen content and complete plants dry weights.
Phase 5: Computation of the complete crop development ratio by means of (1).
Phase 6: Relationship amid input parameters and target parameter absolute CYR.
Phase 7: Submission of ML algorithm on the dataset of the parameters given.
Phase 8: Forecast consequences.
Figure 2. Proposed system of flowchart
4. DATA AND PREDICTION RATE ANALYSIS
4.1. Analysis of nutrients
A NI is considered as a liquid fertilizer resolution attuned in immovable arrangements so as to augment
harvest development and defend it through nutrient shortages. The plant’s requirement is satisfied over the
nutrients ionic form provided over the water and added nutrient fertilizer. The interpretations were engaged
over the duration of 140 days which is, 21 weeks around. The NI ingesting was mostly because of transpiration,
since in a NFT scheme the vaporization is negligible, mentioned for a lone plant [20], [21].
4.2. Analysis of fruit
The disparaging samples (prior yield) of tomatos plant were engaged each in the weeks of around 2
to 3 till 55 days after transplantation (DAT) to define the ion acceptance examination of fruits magnesium
(Mg), calcium (Ca), sodium (Na), potassium (K) and nitrogen (N) content. An obligatory draft oven was
utilized to compute the dry matter weight and an equilibrium with 2 places of decimal was utilized, all
observation standards are engaged in grams. The development factors comprised tomatos dry weight matter
and plants absolute CYR [22]–[26].
5. Int J Elec & Comp Eng ISSN: 2088-8708
An efficient hydro-crop growth prediction system for nutrient analysis … (Chandana Chikkasiddaiah)
6685
5. RESULTS AND ANALYSIS
5.1. Complete development rate of the whole plant and stages of development of tomatos
The fruits were primarily seen on the plant through the early Phase I after 55 DAT, as depicted in
Figure 3. The tomatos complete CYR remained 2.65 gms/day through this phase. Subsequently 76 days after
NS explanation remained added numerous stints to the tomatos. In Phase II: (where in Fruit ripening happens)
next 92 days an exponential development rate of the plant was seen having a maximum total CYR of x.28
grams per day. The NS novel plant was added many periods through such phase as well. In phase III (where in
Fruit maturity and harvest happens), 117 DAT the fruits remained ripened and garnered, nevertheless a
reduction in the complete CYR was seen at this phase having a worth of 2.8 grams per day.
6
7
8
9
10
10
20
30
40
50
60
70
80
90
100
100
-4 -2 0 2 4
Absolute CYR
Days
after
Transplant
(DAT)
Absolute Crop yield Rate Analysis
Figure 3. The proposed NFT-HNM analysis of absolute CYR for every tomatos plant over the period of
20 weeks (140 days)
5.2. The tomatos growth rate in different stages of nutrient ion (before and after harvesting) analysis
and its impact on absolute CYR
The macro and micro-nutrient ion acceptances of the grape’s berries throughout the whole
development procedure of 140 days are shown in Tables 1 and 2. Grapes, like other plants, have specific macro
and micronutrient requirements for healthy growth and development. Here are some key macro and
micronutrients and their roles in grape cultivation.
Table 1. The tomatos crop yield rate (before harvesting)
Ion-Concentration (Before Harvesting in gms)
Crop yield Level NA Mg Ca N K DAT Proposed CYR
Phase_1 1.01 1.08 0.82 0 22.199 57 2.6
Phase_2
0.86 1.06 0.79 1.74 24.87 71 1.33
0.76 1.24 0.90 1.77 22.96 94 4.15
Phase_3
1.06 1.53 0.24 1.91 22.85 117 4.28
1.33 1.56 1.002 1.78 25.66 129 -0.66
1.66 1.75 1.009 1.80 24.54 142 -4.92
1.43 1.19 0.82 1.97 31.55 158 0.02
Table 2. The tomatos crop yield rate (after harvesting)
Ion-Concentration (After Harvesting in gms)
Crop yield Level NA Mg Ca N K DAT Dry Crop Weight
Phase_1 1.19 0.88 0.74 0 27.22 117 39.22
Phase_2
1.30 1.01 0.83 0 29.77 125 46.83
1.35 1.02 0.97 0 30.65 131 35.12
Phase_3
1.53 1.18 0.79 0 32.48 140 27.45
1.31 1.56 2.13 1.83 28.88 147 26.73
1.35 1.03 0.95 1.49 30.11 159 37.99
1.54 1.17 0.79 1.57 32.48 168 23.99
6. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 6681-6690
6686
Figures 4 and 5 shows the crop weight analysis of the tomatos before (I and phase II stages) and after
harvest (phase III stages) respectively using proposed system. Figures 6 and 7 shows the sodium ion analysis
of the tomatos before (I and phase II stages) and after harvest (phase III stages) respectively using proposed
system. The crop yield of tomatoes can vary depending on several factors, including cultivation practices,
environmental conditions, tomato variety, and management techniques.
Figure 4. Crop weight analysis of the tomatos
(before harvest) in phase I and phase II stages and
its analysis using proposed system
Figure 5. Crop weight analysis of the tomatos (after
harvest) in phase III stages and its analysis using
proposed system
Figure 6. Sodium ion analysis of the tomatos
(before harvest) in phase I and phase II stages and
its analysis using proposed system
Figure 7. Sodium ion analysis of the tomatos (after
harvest) in phase III stages and its analysis using
proposed system
Figures 8 and 9 shows the magnesium ion analysis of the tomatos before (I and phase II stages) and
after harvest (phase III stages) respectively using proposed system. Figures 10 and 11 shows the calcium ion
analysis of the tomatos before (I and phase II stages) and after harvest (phase III stages) respectively using
proposed system. The crop yield of tomatoes can vary depending on several factors, including cultivation
practices, environmental conditions, tomato variety, and management techniques.
Figures 12 and 13 shows the nitrogen analysis of the tomatos before (I and phase II stages) and after
harvest (phase III stages) respectively using proposed system. Figures 14 and 15 shows the potassium analysis
of the tomatos before (I and phase II stages) and after harvest (phase III stages) respectively using proposed
system. An incessant distribution of NS was preserved until 168 days through the phase 3, that showed an
upsurge in the claim of ions by the plants, a minor upsurge of 0.099 in the CYR remained seen throughout the
later phases of yield.
7. Int J Elec & Comp Eng ISSN: 2088-8708
An efficient hydro-crop growth prediction system for nutrient analysis … (Chandana Chikkasiddaiah)
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Figure 8. Magnesium ion analysis of the tomatos
(before harvest) in phase I and phase II stages and
its analysis using proposed system
Figure 9. Magnesium ion analysis of the tomatos
(after harvest) in phase III stages and its analysis
using proposed system
Figure 10. Calcium ion analysis of the tomatos
(before harvest) in phase I and phase II stages and
its analysis using proposed system
Figure 11. Calcium ion analysis of the tomatos
(after harvest) in phase III stages and its analysis
using proposed system
Figure 12. Nitrogen analysis of the tomatos (before
harvest) in phase I and phase II stages and its
analysis using proposed system
Figure 13. Nitrogen analysis of the tomatos (after
harvest) in phase III stages and its analysis using
proposed system
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Figure 14. Potassium analysis of the tomatos
(before harvest) in phase I and phase II stages and
its analysis using proposed system
Figure 15. Potassium analysis of the tomatos (after
harvest) in phase III stages and its analysis using
proposed system
The ion upsurge rates remained in the directive K>N>Na>Mg>Ca throughout the phases (earlier and
later yield). In the preliminary time of Phase I and Phase II (where in Fruit set and Fruit ripening happens) the
absorption of the ion upsurge was seen in the range (0 to 1.79) gm L-1
d-1
, nevertheless the K ion depicted an
important variant in the uptake with peak values of uptake in range (22 to 34) gm L-1
d-1
The acceptance rates
of the nutrients augmented expressively when the fruits matured and remained ready to yield till day 143. The
difference tendencies in ion absorption represent a growing tendency for all the ions after 94 and uppermost
absolute CYR (4.28 gm d-1
) was seen parallelly. In the future time a declining tendency was seen for the CYR
and captivation stages of all the ions excluding K ions correspondingly. The extreme uptake rates of K, N, Na,
Mg and Ca was 32.48, 1.91, 1.68, 1.75 and 1.25 gm L-1
d-1
correspondingly. Figure 15 depicts the nutrient
acceptances dynamics since day 117, the yield phases and its influence over the dry weight matter of the fruits.
The absorption of nitrogen remained seen to be too low and that is why it was not seen till day 138. Dry weight
substances of the fruits recorded an upsurge till 125. A quick reduction in weights was seen until day 147. The
tendencies in dissimilarity for Mg, N, Na, Ca and K ions did not track the tendencies in disparity of the dry
weight matter. Extreme concentrations standards seen were 1.68, 1.009, 1.75, 1.91,31.48 respectively.
We projected and premeditated a bendable structure for HNM which could be castoff in the forecast
of the complete CYR. The three phases in the scheme are dependent through each other and target to forecast
the CYR in a relaxed way using input parameters significant through the development procedure. The
ecological situations fixed in the NFT closed hydroponic scheme remained decent to assist the ideal farming
of the tomatos. A thorough investigation depicts the nutrient concentrations and undercurrents of ions uptakes
of Na Ca, N, Mg, K at diverse phases of tomatos development. The absolute CYR of the plant was computed
and perceived for every dissimilar phase of the. Any important variations were not seen amid the absolute CYR
and the dissimilar phases of fruit development. The association investigation later recommended the input
parameters that had a robust influence on the development factors similar to dry weight of the complete fruits.
It stood like nutrient ion Na required the uppermost association, such ions improve the progress and expansion
of the tomatos and likewise donate in refining the flavors of the eatable parts. Ca had a bottommost rate of
acceptance and the feeblest association in the dry weight matter of fruits, showing the quantity in the nutrient
elucidation seen was not ideal for development of tomatos. The K and Mg ions had optimistic relationships
and dissimilarity trends through the development procedure. Raised stages of K ions can progress the fruits.
Sophisticated EC values show additional ions in the result. The EC boundary persisted perpetual (5, 7.5, 10)
through the development of tomatos showing that a comparable quantity of ions was seen at this intermission.
Such results show that precise ion monitoring and adjustments are for healthier uptakes that can outcome in
respectable development and growth of the plants in HNM.
6. CONCLUSION
The proposed hydro-crop growth prediction system (HCGPS) is used to predict crop yield rate using
machine learning by adopting HNM method. The proposed system’s process is reconfigured to reduce the
complexity to achieve the better-quality crop growth and yield rate. The proposed HNM is used to analyze
Effects of straw return with N fertilizer reduction on crop yield, plant diseases and pests and potential heavy
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An efficient hydro-crop growth prediction system for nutrient analysis … (Chandana Chikkasiddaiah)
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metal risk in a Chinese rice paddy: A field study of 2 consecutive wheat-rice cycles the water nutrients and its
concentration to get better crop growth and yield rate. The proposed method improves the Na and K ion uptake
0.98% and 1.025% respectively as compared to conventional methods. According to the analysis, the Ca ion
utilization is very low (0.78%) during entire crop growth process. this indicates better adjustment of NI. The
proposed HCGPS regularly monitors the various ions instead of ECL based control. due to this, it prevents the
nutrient deficiencies during absorption process to get better yield of tomatos. The machine learning based
HCGPS provides the information and prediction rate of water nutrients index and reduces the complexity
during the process to get better crop yield prediction rate.
7. FUTURE SCOPE
The proposed system can predict the crop yield rate using machine learning algorithm. But when
prediction analysis will be done on huge varieties of crops and climate changes, the system has to be developed
using random forest and deep neural network.
ACKNOWLEDGEMENTS
This work is partially supported by the REVA University, JSS Academy of Technical Education,
Bengaluru for all the support and VGST K-FIST L2 Sponsored Smart Grid Laboratory at JSS Academy of
Technical Education, Bengaluru encouragement provided by them to take up this research work and publish
this paper.
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BIOGRAPHIES OF AUTHORS
Chandana Chikkasiddaiah currently working as Assistant Professor in the
department of Master of Computer Applications, JSS Academy of Technical Education,
Bengaluru. She received his Bachelor’s Degree in Computer Science and Engineering from
Coorg Institute of Technology, South Kodagu, Karnataka, India during the year 2008 and M.
Tech in Computer Science and Engineering from SJCIT, Bengaluru, Karnataka, India during the
year 2014. She is having close to 14 years of teaching experience. She is available in this mail
id: punyachandu@gmail.com.
Parthasarathy Govindaswamy Associate Professor at School of Computing and
Information Technology, REVA University, Bangalore. He has pursued his Doctorate (Ph.D.)
degree in Computer Science and Engineering department at Sathyabma University, Chennai,
India in 2017. He received the M.E. degree from the Department of Computer Science and
Engineering, Sathyabama University, Chennai, India in 2006. He has vast experience of more
than 16 years in teaching under various colleges and Universities. He has published thirty papers
in international level Journals and Conferences. He has published 8 patents in various domains.
He is playing the role of reviewer for about four International reputed Journals like J. of
Information and Knowledge Management (JIKM) and Conferences. He is a Microsoft Certified
Professional and he is a member of Computer Society of India (CSI) and ACM. His research
interests are in data mining, web mining, big data analytics, cloud computing, sentiment analysis
and information retrieval. He is available in this mail id: parthasarathy.g@reva.edu.in.
Mallikarjunaswamy Srikantaswamy is currently working as an associate professor
in Department of Electronics and Communication Engineering at JSS Academy of Technical
Education, Bangalore. He obtained his B.E. degree in telecommunication engineering from
Visvesvaraya Technological University Belgaum in 2008, M.Tech. degree from Visvesvaraya
Technological University Belgaum in 2010 and was awarded Ph.D. from Jain University in 2015.
He has 11+ years of teaching experience. His research work has been published in more than 65
International Journals and conference. He received funds from different funding agencies.
Currently guiding five research scholars in Visvesvaraya Technological University Belgaum. He
can be contacted at email: mallikarjunaswamys@jssateb.ac.in.