Smart Farming: An IOT Based Automation

Dhingra, Sunita; Dhingra, Ashwani Kumar; Gupta, Satinder Bal

doi:10.1007/978-981-16-7952-0_8

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…AI has the potential to be embedded in agriculture enterprises and make them smarter. Advancements in these technologies will replace repetitive tasks and help farmers to produce the most appropriate output and improve the quality of production [22]. Figure 3 shows different technologies associated with smart agriculture practices such as machine learning, deep learning, WSN, robotics, etc.…”

Section: Technologies For Exploring Smart Agriculturementioning

confidence: 99%

Artificial Intelligence in Smart Agriculture: Applications and Challenges

Nitin,

Bal Gupta

2023

Curr. Appl. Sci. Technol.

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Artificial intelligence has been categorized as a subfield of computer science wherein machines perform smart learning tasks with the help of data and statical methods. Agriculture is one of the oldest social activities performed by humans. It provides many crucial things like raw materials, food, and employment. Due to the increasing population, it is the need of the hour that the agriculture sector should increase production of resources to match actual demand. Many agronomic factors such as weeds, pests, water condition and availability, and climate conditions impact overall yield. At present, methods used by farmers for management are traditional and insufficient to meet increased demand. To match future demand, new innovative agriculture methos need to be adopted. Artificial intelligence techniques in smart farm monitoring can enhance the quality and quantity of yield. This paper surveys different areas in agriculture where artificial intelligence is applicable. Artificial intelligence enables farmers to access farm-related data and analytical methods that will foster better agronomy, reduce waste, and improve efficiencies with minimum environmental impact. Various artificial intelligence techniques that make agriculture smarter than its previous forms are discussed. In this paper, the implementation of various artificial intelligence techniques in smart agriculture is studied. The aim of this study is to present different key applications and associated challenges to open up new future opportunities.

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Section: Technologies For Exploring Smart Agriculturementioning

confidence: 99%

Artificial Intelligence in Smart Agriculture: Applications and Challenges

Nitin,

Bal Gupta

2023

Curr. Appl. Sci. Technol.

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“…Continuously, advancements in smart devices and image processing techniques help in increasing the interest to adopt modern technologies in agriculture to transform into precision agriculture, which further enables farmers to monitor plant health and potential infestation status. Machine vision technology is novel and successfully applicable in precision agriculture for the automatic pest management system ( Dhingra et al., 2022 ). In this regard, Deguine et al.…”

Section: Introductionmentioning

confidence: 99%

Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests

Nitin

Gupta²,

Yadav³

et al. 2023

Front. Plant Sci.

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Castor (Ricinus communis L.) is an important nonedible industrial crop that produces oil, which is used in the production of medicines, lubricants, and other products. However, the quality and quantity of castor oil are critical factors that can be degraded by various insect pest attacks. The traditional method of identifying the correct category of pests required a significant amount of time and expertise. To solve this issue, automatic insect pest detection methods combined with precision agriculture can help farmers in providing adequate support for sustainable agriculture development. For accurate predictions, the recognition system requires a sufficient amount of data from a real-world situation, which is not always available. In this regard, data augmentation is a popular technique used for data enrichment. The research conducted in this investigation established an insect pest dataset of common castor pests. This paper proposes a hybrid manipulation-based approach for data augmentation to solve the issue of the lack of a suitable dataset for effective vision-based model training. The deep convolutional neural networks VGG16, VGG19, and ResNet50 are then adopted to analyze the effects of the proposed augmentation method. The prediction results show that the proposed method addresses the challenges associated with adequate dataset size and significantly improves overall performance when compared to previous methods.

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“…As the population increases, more food is needed, so the production of food is increased through the use of technology [9]. IoT is one of the emerging technologies today.…”

Section: Introductionmentioning

confidence: 99%

Applying Adaptive Security Techniques for Risk Analysis of Internet of Things (IoT)-Based Smart Agriculture

et al. 2022

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In modern times, the Internet of Things (IoT) is having a major impact on agriculture. Risk and security parameters are always linked when researching, developing, implementing and deploying IoT-based devices. It is a myth that security does not play a major role in IoT applications in agriculture. Data accuracy and availability is a high-priority requirement for farmers who help achieve high yields. A secure IoT network requires a situational approach, often referred to as dynamic security. An advanced security approach to improving IoT security is adaptive security, a cybersecurity-based approach. The lack of security in a smart farming environment is a very important factor for agricultural growth. In this study, we introduce IoT together with adaptive security operations and integrate it into a smart farming environment. We propose an evaluation framework that can be applied to diverse smart farming environments. Several scenarios of an agricultural environment with smart devices and sensors are described for execution. Storylines with real-time environments are then derived from these scenarios to extend and incorporate adaptive security frameworks and scenarios in IoT-based agriculture.

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Smart Farming: An IOT Based Automation

Cited by 5 publications

References 18 publications

Artificial Intelligence in Smart Agriculture: Applications and Challenges

Artificial Intelligence in Smart Agriculture: Applications and Challenges

Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests

Applying Adaptive Security Techniques for Risk Analysis of Internet of Things (IoT)-Based Smart Agriculture

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