Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics

Ferehan, Nourelhouda; Haqiq, Abdelkrim; Ahmad, Mohd Wazih

doi:10.1155/2022/7484088

Cited by 18 publications

(5 citation statements)

References 28 publications

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“…Another recommendation is to concentrate on the use of ensemble techniques along with logistic regression, as they are expected to offer more advanced tools in use, with their combination depending. [23]- [25] . The logistic regression can be used for decision support in precision agriculture and in combination with other similar algorithms can improve their combined power.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

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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“…Moreover, WSNs that incorporate AI-based technologies make use of these capabilities. They adjust their behavior in response to environmental change or changing system needs, saving power and extending sensor node life [16]- [18].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Energy Harvesting Techniques for Enhanced Operation in WSN Using IoT with Deep Learning

D. Hemanand, P. Shobha Rani, Rajalakshmi S

2024

jes

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The recognition of energy harvesting techniques for enhanced operation in Wireless Sensor Networks (WSN) using IoT and deep learning entails a novel approach to improving the energy efficiency and operational durability of sensor nodes deployed in various environments. Energy harvesting refers to the process of collecting energy from diverse sources such as solar power, thermal energy, wind energy, and mechanical vibrations, and converting it into electrical energy to power electronic devices, including sensor nodes in a WSN. The integration of the Internet of Things with WSNs allows for interconnectivity, enabling enhanced communication, data exchange, and remote management capabilities. This integration facilitates the deployment of more intelligent and adaptive sensor networks, capable of making decisions based on collected information. Deep learning, a subset of machine learning, plays a vital role in enhancing the operation of WSNs by providing advanced data analysis and predictive modeling capabilities. Through the utilization of deep learning algorithms, the proposed system can forecast energy consumption patterns, identify optimal periods for energy harvesting, and optimize the energy efficiency of sensor nodes. This includes adjusting operational parameters such as data communication rates or sleep cycles based on the predicted availability of harvested energy. Deep learning also aids in identifying and prioritizing the most viable energy sources in the deployment environment of the WSN. By employing appropriate energy harvesting techniques based on environmental conditions, such as solar energy harvesting in summer or mechanical vibration harvesting in industrial settings, the proposed method ensures more efficient operation and sustainability of sensor networks across various applications.

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Section: Introductionmentioning

confidence: 99%

“…Effort expectations advocate for the integration of intuitive, user-friendly technologies, thus facilitating greater adoption rates among professional farmers [44][45][46]. Social influence incorporates community networking and peer-driven success as catalysts for technology uptake across the industry [47][48][49]. Given the pressing challenges faced by China's hog farming industry, the insights gained from applying the UTAUT model to understand the adoption of these smart farming technologies could prove invaluable.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Hog Farming Adoption Choices Using the Unified Theory of Acceptance and Use of Technology Model: Perspectives from China’s New Agricultural Managers

Wang,

Zhang,

Zhang

2023

Agriculture

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This research delves into the intricacies of decision-making processes underpinning the willingness to upgrade technology within the burgeoning domain of intelligent pig farming in China, employing the UTAUT model to scrutinize how various determinants sway upgrade willingness and the ensuing behavioral modification. By applying the UTAUT model to intelligent pig farming, the inquiry evaluates the impact of performance expectations, effort expectations, social influence, and contributory factors on upgrade willingness and behavior, with data amassed from assorted novel agricultural management entities in China. The findings unveil that performance and effort expectations, social influence, and contributory factors have a favorable influence on upgrade willingness, while contributory factors, alongside the augmentation of upgrade willingness, positively affect upgraded behavior. This inquiry underscores the multifaceted interaction of factors guiding technological upgrade verdicts in intelligent pig farming, furnishing invaluable insights for comprehending technology adoption in agriculture. It lays a groundwork for devising strategies to spur technological advancements, harboring potential for wider applications across varied agricultural vistas.

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Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics

Cited by 18 publications

References 28 publications

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

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

Recognition of Energy Harvesting Techniques for Enhanced Operation in WSN Using IoT with Deep Learning

Intelligent Hog Farming Adoption Choices Using the Unified Theory of Acceptance and Use of Technology Model: Perspectives from China’s New Agricultural Managers

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