Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things

Waleed, Muhammad; Um, Tai-Won; Kamal, Tariq; Usman, Syed Muhammad

doi:10.3390/sym13030403

Cited by 27 publications

(15 citation statements)

References 38 publications

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“…The predicted classes in model SVM are made based on the side of the hyperplane where the data point falls. SVM is a kind of supervised learning algorithm based on structural risk minimization [21]. As a popular machine learning algorithm, SVM is widely used in many fields, such as finance and information retrieval, it provides high accuracy on current and future data.…”

Section: Support Vector Machinementioning

confidence: 99%

“…As the name suggests, RFs are formed by simply assembling multiple decision trees, usually ranging from a few tens to thousands of trees. This bagging method forms patterns, which are responsible for increased performance [21]. In addition, the random process in the construction of the trees makes it possible to ensure a low correlation between them.…”

Section: Random Forestmentioning

confidence: 99%

“…The datasets are trained according to their class. The KNN algorithm needs a distance calculation function between observations, it must be predicted to calculate the distance with the nearest "K" points [21]. Using the formulas, there are several distance calculation methods including, Minkowski distance, Manhattan distance, Euclidean distance, and Hamming distance.…”

Section: K-nearest Neighbormentioning

confidence: 99%

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Machine learning for internet of things classification using network traffic parameters

Elhaloui¹,

Filali²,

Benlahmar³

et al. 2023

IJECE

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With the growth of the internet of things (IoT) smart objects, managing these objects becomes a very important challenge, to know the total number of interconnected objects on a heterogeneous network, and if they are functioning correctly; the use of IoT objects can have advantages in terms of comfort, efficiency, and cost. In this context, the identification of IoT objects is the first step to help owners manage them and ensure the security of their IoT environments such as smart homes, smart buildings, or smart cities. In this paper, to meet the need for IoT object identification, we have deployed an intelligent environment to collect all network traffic traces based on a diverse list of IoT in real-time conditions. In the exploratory phase of this traffic, we have developed learning models capable of identifying and classifying connected IoT objects in our environment. We have applied the six supervised machine learning algorithms: support vector machine, decision tree (DT), random forest (RF), k-nearest neighbors, naive Bayes, and stochastic gradient descent classifier. Finally, the experimental results indicate that the DT and RF models proved to be the most effective and demonstrate an accuracy of 97.72% on the analysis of network traffic data and more particularly information contained in network protocols. Most IoT objects are identified and classified with an accuracy of 99.21%.

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Section: Support Vector Machinementioning

confidence: 99%

Section: Random Forestmentioning

confidence: 99%

Section: K-nearest Neighbormentioning

confidence: 99%

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Machine learning for internet of things classification using network traffic parameters

Elhaloui¹,

Filali²,

Benlahmar³

et al. 2023

IJECE

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show abstract

“…Some research reports using smartphones and sensors to remotely monitor the soil condition and enable smart irrigation [5]. A more complex system combines IoT and artificial intelligence techniques such as machine learning [6], Fuzzy logic [7], deep Q-learning [8], artificial neural network [9] and Multi-Agent systems [10] [11] , and to handle diverse aspects, e.g., irrigation, fertilization, or pesticide treatment and so on. In what follows we analyze and discuss succinctly several studies that use a multi-agent system and IoT to perform intelligent farming systems.…”

Section: Related Workmentioning

confidence: 99%

Internet of things and agent-based system to improve water use efficiency in collective irrigation

Ikidid

Sadgal

2023

ComSIS

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The efficient management of water resources is a major issue in the field of sustainable development. Several models of solving this problem can be found in the literature, especially in the agricultural sector which represents the main consumer through irrigation. Therefore, Irrigation management is an important and innovative field that has been the subject of several types of research and studies to deal with the different activities, behaviors, and conflicts between the different users. This article introduces an intelligent irrigation system based on smart sensors that can be used moderately and economically to monitor farms by integrating some connected electronic devices and other advantageous instruments widely used in the field of IoT, it determines the water requirement of each farm according to the water loss due to the process of evapotranspiration. The water requirement is calculated from data collected from a series of sensors installed in the plantation farm. This project focuses on smart irrigation based on IoT and agent technology, it can be used by farmer associations whose endowments and irrigation planning are defined according to the need and quantity of water available in the rural municipality. The system includes a microcontroller with the integration of sensors, actuators, and valve modules where each node serves as an IoT device. Environmental parameters are monitored directly through a multi-agent system that facilitates the control of each node and the configuration of irrigation parameters. The amount of water calculated for irrigation is based on the Penman model for calculating the daily evapotranspiration baseline. Compared to the conventional irrigation method, it is expected that the proposed irrigation model would contribute to saving water use and distributing it impartially without compromising its production.

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“…Machine learning is an excellent technique to overcome the computational complexity issue in any complicated engineering problem because it is a self-learner, and it does not need to be reprogrammed [ 32 , 33 , 34 , 35 ]. Based on background studies, there are three types of machine learning approaches (i.e., supervised, unsupervised, and reinforcement learning), which have been intelligently utilized for energy optimization.…”

Section: Related Workmentioning

confidence: 99%

Energy Conservation for Internet of Things Tracking Applications Using Deep Reinforcement Learning

Sultan

Waleed

Pyun

et al. 2021

Sensors

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The Internet of Things (IoT)-based target tracking system is required for applications such as smart farm, smart factory, and smart city where many sensor devices are jointly connected to collect the moving target positions. Each sensor device continuously runs on battery-operated power, consuming energy while perceiving target information in a particular environment. To reduce sensor device energy consumption in real-time IoT tracking applications, many traditional methods such as clustering, information-driven, and other approaches have previously been utilized to select the best sensor. However, applying machine learning methods, particularly deep reinforcement learning (Deep RL), to address the problem of sensor selection in tracking applications is quite demanding because of the limited sensor node battery lifetime. In this study, we proposed a long short-term memory deep Q-network (DQN)-based Deep RL target tracking model to overcome the problem of energy consumption in IoT target applications. The proposed method is utilized to select the energy-efficient best sensor while tracking the target. The best sensor is defined by the minimum distance function (i.e., derived as the state), which leads to lower energy consumption. The simulation results show favorable features in terms of the best sensor selection and energy consumption.

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Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things

Cited by 27 publications

References 38 publications

Machine learning for internet of things classification using network traffic parameters

Machine learning for internet of things classification using network traffic parameters

Internet of things and agent-based system to improve water use efficiency in collective irrigation

Energy Conservation for Internet of Things Tracking Applications Using Deep Reinforcement Learning

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