Real-Time Monitoring of Agricultural Land with Crop Prediction and Animal Intrusion Prevention using Internet of Things and Machine Learning at Edge

Nikhil, R; Anisha, B S; P, Ramakanth Kumar

doi:10.1109/conecct50063.2020.9198508

Cited by 26 publications

(10 citation statements)

References 9 publications

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“…Simple regression algorithms -include linear, Extreme Gradient Boosting (XGBoost), and Stacked regressors-has been utelised to estimate the crop yield [228][229][230]. Decision tree, K-nearest neighbors (KNN), random forest, Naïve Bayes Classifier, and support vector machine (SVM) are simple classifiers used to predict the most suitable crop for a region [210,[231][232][233][234][235][236]. The simplicity of those algorithms enable presenting relationship between data with out demanding extensive computation power, hence can be employed at the edge.…”

Section: E Data Analysis 1) Machine Learning Techniquesmentioning

confidence: 99%

Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

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As today's agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgrading the farming practices by shifting towards Smart Farming (SF) -utilizing advanced information and communication technologies to improve the quantity and quality of the crop with minimal labor interference. SF has gained lots of interest in recent years utilizing a variety of technological innovations in the field, which imposes a challenge on farmers and technology integrators to identify the suitable technologies and best practices for a particular application. This paper provides a survey of the most recent SF scientific literature to identify common practices toward technology integration, challenges, and solutions. The survey was conducted on 588 papers published on the IEEE database following Cochrane methods to ensure appropriate analysis and interpretation of results. The papers' contributions were analyzed to identify necessary technologies that constitute SF, and consequently, research themes were identified. The identified themes are sensors, communication, big data, actuators and machines, and data analysis. Besides presenting an in-depth analysis of each identified theme, the paper discusses integrating more than one technology in systems to achieve independency. The most common SF systems are remote monitoring, autonomous, and intelligent decision-making systems.

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Section: E Data Analysis 1) Machine Learning Techniquesmentioning

confidence: 99%

Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

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“…Slimmer version ML algorithms (i.e., TfLite, TinyML) has been used in edge devices for diverse applications such as smart irrigation of agricultural systems and detection of soil contaminations (Figure 4a). 138 Specifically, water surveillance (e.g., soil moisture, drought, water depth) in RTCSM has widely adopted ML algorithms (e.g., support vector machine, random forest, artificial neural network) for estimation and prediction of agricultural productions (Table 2). 139 Some well-developed ML algorithms have even been used to evaluate SOC, benefiting the soil mapping.…”

Section: Current State and Challenges Of Rtcsm Data Transmission Data...mentioning

confidence: 99%

“…Adding intelligence to edge devices makes them self-contained and allows them to make intelligent decisions based on the data they collect. Slimmer version ML algorithms (i.e., TfLite, TinyML) has been used in edge devices for diverse applications such as smart irrigation of agricultural systems and detection of soil contaminations (Figure a) . Specifically, water surveillance (e.g., soil moisture, drought, water depth) in RTCSM has widely adopted ML algorithms (e.g., support vector machine, random forest, artificial neural network) for estimation and prediction of agricultural productions (Table ).…”

Section: Current State and Challenges Of Rtcsm Data Transmission Data...mentioning

confidence: 99%

A Critical Review for Real-Time Continuous Soil Monitoring: Advantages, Challenges, and Perspectives

Fan

Wang

Funk

et al. 2022

Environ. Sci. Technol.

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Most soil quality measurements have been limited to laboratory-based methods that suffer from time delay, high cost, intensive labor requirement, discrete data collection, and tedious sample pretreatment. Real-time continuous soil monitoring (RTCSM) possesses a great potential to revolutionize field measurements by providing first-hand information for continuously tracking variations of heterogeneous soil parameters and diverse pollutants in a timely manner and thus enable constant updates essential for system control and decision-making. Through a systematic literature search and comprehensive analysis of state-of-the-art RTCSM technologies, extensive discussion of their vital hurdles, and sharing of our future perspectives, this critical review bridges the knowledge gap of spatiotemporal uninterrupted soil monitoring and soil management execution. First, the barriers for reliable RTCSM data acquisition are elucidated by examining typical soil monitoring techniques (e.g., electrochemical and spectroscopic sensors). Next, the prevailing challenges of the RTCSM sensor network, data transmission, data processing, and personalized data management are comprehensively discussed. Furthermore, this review explores RTCSM data application for updating diverse strategies including high-fidelity soil process models, control methodologies, digital soil mapping, soil degradation, food security, and climate change mitigation. Finally, the significance of RTCSM implementation in agricultural and environmental fields is underscored through illuminating future directions and perspectives in this systematic review.

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“…Tree Automata based on Automatic Approximations for the Analysis of Security Protocols, abbreviated as TA4SP, processes the intruder knowledge using regular tree language [85]. Nikhil et al [86] propose an integrated technique for prediction and prevention in agriculture sector with smart connected devices. The experiment conducted on the real- [88].…”

Section: A Ml-based Prevention Models For Iotmentioning

confidence: 99%

Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

et al. 2022

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The increasing number of connected devices in the era of Internet of Thing (IoT) has also increased the number intrusions. Intrusion Detection System (IDS) is a secondary intelligent system to monitor, detect, and alert about malicious activities; an Intrusion Prevention System (IPS) is an extension of a detection system that triggers relevant action when an attack is suspected in a futuristic aspect. Both IDS and IPS systems are significant and useful for developing a security model. Several studies exist to review the detection and prevention models; however, the coherence in the opportunistic or advancements in the models is missing. Besides, the existing models also have some limitations, which need to be surveyed to develop new security models. Our survey is the first one to present a study of risk factor analysis using mapping technique, and provide a proposal for hybrid framework for an efficient security model for intrusion detection and/or prevention. We explore the importance of various Artificial Intelligence (AI)-based techniques, tools, and methods used for the detection and/or prevention systems in IoTs. More specifically, we emphasize on Machine Learning (ML) and Deep Learning (DL) techniques for intrusion detection-prevention systems and provide a comparative analysis focusing on the feasibility, compatibility, challenges, and real-time issues. This present survey is beneficial for industry and academia to categorize the challenges and issues in the current security models and generate the new dimensions of developments of security frameworks with efficient ML or DL methods.

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Real-Time Monitoring of Agricultural Land with Crop Prediction and Animal Intrusion Prevention using Internet of Things and Machine Learning at Edge

Cited by 26 publications

References 9 publications

Technologies Driving the Shift to Smart Farming: A Review

Technologies Driving the Shift to Smart Farming: A Review

A Critical Review for Real-Time Continuous Soil Monitoring: Advantages, Challenges, and Perspectives

Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

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