article · Journal of Food Quality
Smart irrigation and precision agriculture rely increasingly on the Internet of Things and machine learning to address freshwater shortages, boost efficiency, and optimise operational expenses. However, expanding network complexity introduces critical privacy and security vulnerabilities that hinder broader technological adoption. To address these vulnerabilities, a framework has been developed to detect and classify security intrusions within agricultural network environments. The approach processes the standard NSL-KDD dataset by transforming symbolic attributes into numerical formats and applying principal component analysis for feature extraction. Multiple machine learning algorithms, specifically support vector machines, linear regression, and random forests, are employed to classify the data. The performance of these classification algorithms is assessed across metrics including accuracy, precision, and recall, offering a structured method to mitigate cyber risks in connected farming infrastructures.
Water shortages demand smarter, connected agricultural irrigation, but cyber intrusions could compromise vital food and water supplies. Applying machine learning to detect network attacks helps safeguard connected farming operations against digital threats, ensuring that vital resources remain properly managed without risking system disruption through network vulnerabilities.
This technology could be applied by agricultural technology providers and farm managers seeking to secure smart irrigation hardware against digital intrusion. Because the models were tested on the general benchmark NSL-KDD dataset rather than operational farm telemetry, this work represents early-stage research that requires validation in live agricultural settings before reaching commercial deployment.
AI-generated from the published abstract. Always read the original work before citing.
The majority of countries rely largely on agriculture for employment. Irrigation accounts for a sizable amount of water use. Crop irrigation is an important step in crop yield prediction. Field harvesting is very reliant on human supervision and experience. It is critical to safeguard the field’s water supply. The shortage of fresh water is a major challenge for the world, and the situation will deteriorate further in the next years. As a result of the aforementioned challenges, smart irrigation and precision farming are the only viable solutions. Only with the emergence of the Internet of Things and machine learning have smart irrigation and precision agriculture become economically viable. Increased efficiency, expense optimization, energy maximization, forecasting, and general public convenience are all benefits of the Internet of Things (IoT). As systems and data processing become more diversified, security issues arise. Security and privacy concerns are impeding the growth of the Internet of Things. This article establishes a framework for detecting and classifying intrusions into IoT networks used in agriculture. Security and privacy are major concerns not only in agriculture-related IoT networks but in all applications of the Internet of Things as well. In this framework, the NSL KDD data set is used as an input data set. In the preprocessing of the NSL-KDD data set, first all symbolic features are converted to numeric features. Feature extraction is performed using principal component analysis. Then, machine learning algorithms such as support vector machine, linear regression, and random forest are used to classify preprocessed data set. Performance comparisons of machine learning algorithms are evaluated on the basis of accuracy, precision, and recall parameters.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1155/2022/3955514
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
