Experimental performance of smart IoT-enabled drip irrigation system using and controlled through web-based applications

Jain, Ravi

doi:10.1016/j.atech.2023.100215

Cited by 27 publications

(10 citation statements)

References 19 publications

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“…As well, my previous work focused only on the HTTP protocol for sending the data through the data fusion method. 62 During sending data, the time was taken more, whereas this paper is attempted on different protocols such as HTTP and MQTT protocols with multiple nodes through WSN. The major advantages of the proposed method are that faster communication can be obtained and data are also stored in the IoT cloud easily for data analytics.…”

Section: Time (Min)mentioning

confidence: 99%

Experimental performance of soil monitoring system using IoT technique for automatic drip irrigation

Jain,

Mukherjee,

Karmakar

et al. 2023

Int J Communication

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SummaryThis paper proposes an IoT‐enabled soil monitoring system using wireless sensor network for automatic irrigation in agricultural applications, especially for lemongrass plants, where an automated control system is required for irrigation applications. This can solve the problem of the water crisis, which is faced by the farmers during the cultivation of the crop in the field. This controls the water supply in the irrigation process using an IoT communication system. A system architecture for soil monitoring and controlling irrigation using IoT technique is designed where the different sensors and actuators like humidity, soil moisture, temperature, pump, and so forth are connected with a node microcontrol unit and message queuing telemetry transport (MQTT) protocol for enhancing communication capabilities. This wireless sensor network gives feedback to the system. This provides automation by the on/off pump system during drip irrigation. The sensor data are displayed on a PC or mobile phone through wireless communication and an IoT cloud platform. An experimental testing setup is developed and the experimental performance of a soil monitoring system using IoT technique for automatic drip irrigation has been carried out and soil moisture data are also stored in a cloud server for analytics. The performance shows that the MQTT protocol sends data within 48 s to the IoT cloud so that the data can be acquired in a faster manner. This shows that this kind of soil monitoring system is suitable for automatic drip irrigation, which enhances the farming process and overcomes the water crises in the agricultural system by reducing the wastage of water.

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Section: Time (Min)mentioning

confidence: 99%

Experimental performance of soil monitoring system using IoT technique for automatic drip irrigation

Jain,

Mukherjee,

Karmakar

et al. 2023

Int J Communication

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show abstract

“…Precision agriculture is a modern paradigm of incorporating advanced technologies, such as Machine Learning and Internet of Things , and enhancing farming operations [1]- [3]. The exponential growth of the population combined with resource constraints and environmental issues indicate that innovative solutions are required to manage agricultural processes.…”

Section: Introductionmentioning

confidence: 99%

Machine learning and Sensor-Cloud Based Precision Agriculture for Intelligent Water Management for Enhanced Crop Productivity

Abhishek Sharma

2024

jes

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The combination of Machine Learning algorithms with Internet of Things devices is emerging as an effective solution to redefining precision agriculture for better water management and crop cultivation. The purpose of this study is to use different learning models, such as Artificial Neural Networks , Support Vector Machines , Decision Trees , and Random Forest , to predict the irrigation need based on real-world sensor data. To retrieve the output variable, which is the irrigation requirement, data from temperature, soil moisture, water level, and humidity sensors that are available in a tomato cultivation facility are used. The dataset consists of 3422 readings, which are split into training and testing sets. A designated percentage of 70% is used for training the models, while the remaining 30% are used to test the outcomes. As the results of the study show, the ANN model is the most accurate predictor of irrigation need with the classification rate of 97.6%, followed by SVM , DT , and RF with 95.4%, 91.3%, and 88.9% correspondingly. The differences in the outcomes are demonstrated in confusion matrices, which identify the classification of the cases and indicate the percentage of correct predictions. Evidence of the predictive power of ML models implies that farmers can independently determine when to activate the pump when real-world data serve as the input. Additionally, the ability to collect real-world data using IoT sensors is beneficial, as it empowers farmers with up-to-date information to make a timely decision about pump activation. The limitations are associated with the type of crop and agricultural facility and, for this reason, future studies may investigate the generality of the conclusion with regard to other crop types and facilities.

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“…The identified technology has numerous benefits including but not limited to water effectivity, controlled crop yields, and resources use. In addition, smart irrigation systems have potential for scalability and implementation in various climates [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the machine learning based Efficacy of Decision Tree and Support Vector Machines in Smart Irrigation Systems for Precise Irrigation Status Classification for Optimizing Water Management in Agriculture

Shaik Mabasha

2024

jes

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The research paper deals with the improvement of water management practices in agriculture through the use of machine learning in smart irrigation systems. The efficiency of the Decision Tree and the Support Vector Machine models with real-time sensor data was analyzed. The Internet of Things system was developed to analyze the data collected at the field with the help of various sensors, including temperature, humidity, and soil moisture sensors. The layout and choice of sensors were based on the official parameters required for most average plants and corps. After data collection and development of classifiers, the Decision Tree and Support Vector Machine models were used and analyzed regarding their efficiency for making irrigation-related decisions. As the result, it was found that both models showed a good performance, but the SVM model was slightly better due to a smaller number of false positives and false negatives, which allow it to divide the data into the corresponding categories more accurately. At the same time, both models use history data, and the more recent data are better for decision as it was also noted during the development when the use of models based on history data for irrigation decision result in regular irrigation, which in long term would cause the loss of productivity of plans, due to the issue that ancient and no crops are watered too. As the final results, it can be stated that the models show good performance, and the algorithms that were tested can be stated viable for the use at practice. At the same time, the SVM model shows slightly better performance than DT.

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Experimental performance of smart IoT-enabled drip irrigation system using and controlled through web-based applications

Cited by 27 publications

References 19 publications

Experimental performance of soil monitoring system using IoT technique for automatic drip irrigation

Experimental performance of soil monitoring system using IoT technique for automatic drip irrigation

Machine learning and Sensor-Cloud Based Precision Agriculture for Intelligent Water Management for Enhanced Crop Productivity

Evaluating the machine learning based Efficacy of Decision Tree and Support Vector Machines in Smart Irrigation Systems for Precise Irrigation Status Classification for Optimizing Water Management in Agriculture

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