Precision agriculture using IoT data analytics and machine learning

Akhter, Ravesa; Sofi, Shabir Ahmad

doi:10.1016/j.jksuci.2021.05.013

Cited by 211 publications

(101 citation statements)

References 60 publications

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“…Biradar et al (2019) [29] estimating the crop water requirement and incorporating IoT, Cloud computing, and CPS-Cyber-physical systems which plays a vital role in improving productivity, feeding the world, and preventing starvation. Ravesa Akhter et al (2021) [30] this article summarizes the latest trend in interfacing the IoT, Wireless sensor networks, data mining cum analytics, and Machine learning in agriculture. Smart agriculture is the trending technology, and this article mainly predicts the apple disease in apple orchards in Kashmir valley using machine learning (simple regression model) and IOT Data analytics.…”

Section: Literature Reviewmentioning

confidence: 99%

IoT Framework for Measurement and Precision Agriculture: Predicting the Crop Using Machine Learning Algorithms

et al. 2022

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IoT architectures facilitate us to generate data for large and remote agriculture areas and the same can be utilized for Crop predictions using this machine learning algorithm. Recommendations are based on the following N, P, K, pH, Temperature, Humidity, and Rainfall these attributes decide the crop to be recommended. The data set has 2200 instances and 8 attributes. Nearly 22 different crops are recommended for a different combination of 8 attributes. Using the supervised learning method, the optimum model is attained using selected machine learning algorithms in WEKA. The Machine learning algorithm selected for classifying is multilayer perceptron rules-based classifier JRip, and decision table classifier. The main objective of this case study is to end up with a model which predicts the high yield crop and precision agriculture. The proposed system modeling incorporates the trending technology, IoT, and Agriculture needy measurements. The performance assessed by the selected classifiers is 98.2273%, the Weighted average Receiver Operator Characteristics is 1 with the maximum time taken to build the model being 8.05 s.

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Section: Literature Reviewmentioning

confidence: 99%

IoT Framework for Measurement and Precision Agriculture: Predicting the Crop Using Machine Learning Algorithms

et al. 2022

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“…Before the advancement of information technology, farmers have had to be on their farms for physical examination of the plants, while also examining the moisture content of the soil [178]. However, with the recent progress in the area of wireless sensor networks, the IoT has made it possible for data to be collected from various sensors, such as soil moisture content, soil and air temperature, humidity, and plant parameters like the vegetation index, and then viewed remotely.…”

Section: Data Analytics and Visualizationmentioning

confidence: 99%

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

et al. 2022

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Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.

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“…Akhter and Sofi 59 has proposed a prediction model of Apple disease in the apple orchards of Kashmir valley based on machine learning and data analytics in an IoT system. This article has reviewed the trending technologies adoption and impacts on precision agriculture.…”

Section: Literature Surveymentioning

confidence: 99%

Importance of semantic interoperability in smart agriculture systems

Khatoon

Ahmed

2022

Trans Emerging Tel Tech

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The Internet of Things (IoT) connects people with real-world objects to exchange data. It is performed using services and devices present in the user's activities based on the IoT. Based on a common vocabulary or mappings, knowledge and information can be exchanged by different agents, services, and devices in this scenario. The heterogeneous sources can be represented and integrated by an ontology. The Semantic Web offers semantic interoperability that facilitates communication between heterogeneous devices and technology platforms. The state-of-the-art of IoT semantic interoperability is assessed and reviewed in this work. The importance and challenges of interoperability are discussed in detail with an in-depth analysis of the requirements. IoT based semantic interoperability model is discussed with semantic annotations of data required for heterogeneous IoT devices. Additionally, it has been presented which Semantic Web technologies are incorporated, and the challenges have been studied in this research area. Agriculture is a domain where IoT applications have a lot of potentials. The market is filled with several devices that collect data from the farms and send it to the cloud. Semantic Web technology for applications in agriculture has been discussed in detail with data integration. Semantic resources for agriculture have been enlisted with linked data hubs and semantic data standards. The article aims to address the need and requirement of IoT with semantic interoperability in the field of agriculture. The use of IoT interoperability in agriculture can bring long term benefits to the farmers and increase productivity while reducing the overall costs incurred.

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Precision agriculture using IoT data analytics and machine learning

Cited by 211 publications

References 60 publications

IoT Framework for Measurement and Precision Agriculture: Predicting the Crop Using Machine Learning Algorithms

IoT Framework for Measurement and Precision Agriculture: Predicting the Crop Using Machine Learning Algorithms

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

Importance of semantic interoperability in smart agriculture systems

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