Smart urban farming service model with IoT based  open platform

Oh, Am Suk

doi:10.11591/ijeecs.v20.i1.pp320-328

Cited by 6 publications

(6 citation statements)

References 16 publications

(19 reference statements)

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“…In the future, we can derive new ideas to develop our work such as smart urban farming service concept based on an open IoT platform [32], [33]. Using open source IoT platform such as (NodeMcu, Node Red, and message queue telemetry transport (MQTT)) [34].…”

Section: Resultsmentioning

confidence: 99%

An intelligent irrigation system based on internet of things (IoT) to minimize water loss

Amassmir

Tkatek

Abdoun

et al. 2022

IJEECS

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This paper proposes a comparison of three machine learning algorithms for a better intelligent irrigation system based on internet of things (IoT) for differents products. This work's major contribution is to specify the most accurate algorithm among the three machine learning algorithms (k-nearest neighbors (KNN), support vector machine (SVM), artificial neural network (ANN)). This is achieved by collecting irrigation data of a specific products and split it into training data and test data then compare the accuracy of the three algorithms. To evaluate the performance of our algorithm we built a system of IoT devices. The temperature and humidity sensors are installed in the field interact with the Arduino microcontroller. The Arduino is connected to Raspberry Pi3, which holds the machine learning algorithm. It turned out to be ANN algorithm is the most accurate for such system of irrigation. The ANN algorithm is the best choice for an intelligent system to minimize water loss for some products.

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Section: Resultsmentioning

confidence: 99%

An intelligent irrigation system based on internet of things (IoT) to minimize water loss

Amassmir

Tkatek

Abdoun

et al. 2022

IJEECS

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“…Besides that, the manual control of nutrients may cause quality issues to the lettuce plant is the next problem in this research. If the nutrient supply is too much or less, it will disturb the growth of the lettuce plant either the lettuce plant is dead or stunted [2][3][4][5][6][7]. So, the farmer can monitor through some parameters by preventing lack of nutrients to the lettuce plant.…”

Section: Problem Statementmentioning

confidence: 99%

“…A. S. Oh et al, [6] presents the smart urban farming service model with an IoT based open platform. In this research paper, it is study about the agricultural production or smart farming using IoT sensors, the big-data and cloud administration has demonstrated.…”

Section: Previous Study For Urban Farming Growth Monitoring System Us...mentioning

confidence: 99%

Urban Farming Growth Monitoring System Using Artificial Neural Network (ANN) and Internet of Things (IOT)

Mohamed Mydin M. Abdul Kader,

Muhammad Naufal Mansor,

Wan Azani Mustafa

et al. 2024

ARASET

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As an introduction to this project, the growth-related traits, such as above-ground biomass and leaf area, are critical indicators to characterize the growth of indoor lettuce plants. Currently, non-destructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. It is also one of the problem statements in this project. Based on this project the next problem is manual control of nutrients may cause quality issues to the lettuce plant. If the nutrient supply is too much or less, it will disturb the growth of the lettuce plant either the lettuce plant is dead or stunted. This project is about urban farming growth monitoring system using Artificial Neural Network (ANN) and Internet of Things (IoT). In this project, a method for monitoring the growth of indoor lettuce plants was proposed by using digital images and an ANN using Deep Learning Architecture. DLA is mostly developed by the software of MATLAB or Python to insert and run the coding. DLA is mostly used for image detection, pattern recognition, and natural language processing through the graph for Neural Network. Next, the Internet of Things (IoT) is a medium to store images of indoor lettuce plant growth into the Cloud (Google Drive). Furthermore, it takes indoor lettuce plant images as the input, an ANN was trained to learn the relationship between images and the corresponding growth- related traits with other fixed parameters. The pH level parameters were controlled by other fixed parameters to take the images of indoor lettuce plant growth. The parameters used in this project are temperature and humidity. This helps to compare the results of Artificial Neural Network (ANN), widely adopted methods were also used. Concisely, this project is expected to develop the Deep Learning Architecture using an Artificial Neural Network (ANN) with digital images as a robust tool for the monitoring of the growth of indoor lettuce plants every 30 minutes per day. Generally, focused on an urban farming growth monitoring system using Artificial Neural Network (ANN) and the Internet of Things (IoT).

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“…In the future, we may be able to generate new concepts for expanding our work, such as a smart urban agricultural service concept based on an open IoT platform [36], [37]. Using an open-source IoT platform (NodeMcu, Node-Red, and message queue telemetry transmission) [38].…”

Section: Figure 2 Schema Of Iot Implantationmentioning

confidence: 99%

Predictive fertilization models for potato crops using machine learning techniques in Moroccan Gharb region

Tkatek,

Amassmir,

Belmzoukia

et al. 2023

IJECE

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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 R2Root mean squared error (RMSE) to describe the model’s mistakes and prediction capacity. It turned out that the XGBoost model has the greatest R2, 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.

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Smart urban farming service model with IoT based open platform

Cited by 6 publications

References 16 publications

An intelligent irrigation system based on internet of things (IoT) to minimize water loss

An intelligent irrigation system based on internet of things (IoT) to minimize water loss

Urban Farming Growth Monitoring System Using Artificial Neural Network (ANN) and Internet of Things (IOT)

Predictive fertilization models for potato crops using machine learning techniques in Moroccan Gharb region

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