AI-Equipped IoT Applications in High-Tech Agriculture Using Machine Learning

AI-Equipped IoT Applications in High-Tech Agriculture Using Machine Learning

Mariya Vinshon V., A.V. Senthil Kumar, Priyanka Sharma, Sarabjeet Kaur, Omar S. Saleh, H.R. Chennamma, Ankita Chaturvedi
Copyright: © 2023 |Pages: 27
DOI: 10.4018/978-1-6684-9231-4.ch003
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Abstract

Crop raiding by animals has become one of the most common human-animal disputes as a result of human encroachment of wildlife habitats and deforestation. Farmers suffer huge crop loss due to crop raiding by wild animals like elephants, wild boar, and deer. One of the main concerns of today's farmers is protecting crops from wild animal attacks. Nevertheless, some of the traditional methods have environmental pollution effects on both humans and ungulates, while others are very expensive with high maintenance costs, with limited reliability and limited effectiveness. In this chapter, the authors develop a system that combines computer vision using DCNN for detecting and recognizing animal species and specific ultrasound emission for repelling them. The edge computing device activates the camera then executes its DCNN software to identify the target, and if an animal is detected, it sends back a message to the animal repelling module including the type of ultrasound to be generated according to the category of the animal.
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Literature Review

Comprehensive survey of IoT-based applications of artificial intelligence in agriculture. The authors explore the various ways in which AI is being implemented in agriculture, including improving crop yield, enhancing resource efficiency, precision agriculture, automating crop monitoring and maintenance, and more. The article also discusses the challenges facing the implementation of AI in agriculture, such as ethical considerations and legal and regulatory challenges. Overall, the survey provides valuable insights into the current state and future potential of AI in agriculture, highlighting its potential to revolutionize the industry and address some of the biggest challenges facing global food production (Chen, et al., 2019).

Review of the integration of IoT and AI in precision agriculture. The authors highlighted the potential benefits of this integration, including improved resource management, increased crop yield, and reduced environmental impact. The review covered various aspects of precision agriculture, including mapping and analysing farm data, automating crop monitoring and maintenance, and machine learning for weather forecasting and crop planning. The authors also discussed some of the challenges associated with IoT and AI in agriculture, such as data security and privacy concerns. Overall, the review provided insights into the current state of precision agriculture and highlighted the potential for future advancements in this field (Djenouri, Behja, and Benmohammed 2020).

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