Enabling Artificial Intelligence and Cyber Security in Smart Manufacturing

Kumar, Raj; Keerthana, G.; Murali, L.; Chidambaranathan, S.; Premkumar, C.D.; Mahaveerakannan, R.

doi:10.1002/9781119821809.ch17

Cited by 2 publications

(3 citation statements)

References 27 publications

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“…According to recent investigations, AI-assisted crop phenotyping and yield predictions have improved crop phenotyping [36][37][38][39][40][41][42]. Furthermore, the following applications of AI-assisted high-throughput phenotyping platforms have been proven as successful: identifying the plant growth stage in wheat and maize [37] and segmenting plant images [38]; semantic segmentation of crops and weeds for oilseed crops [39]; phenotyping crop disease resistance [40]; and boosting the productivity of crops [41,42]. The importance of AI technologies in various fields is shown in Figure 4.…”

Section: Crop Monitoring and Phenotyping Using Artificial Intelligencementioning

confidence: 99%

Seeding a Sustainable Future: Navigating the Digital Horizon of Smart Agriculture

Balyan,

Jangir,

Tripathi

et al. 2024

Sustainability

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Agriculture is essential to the existence of the human race, as well as the foundation of our civilization, because it provides food, fuel, fiber, and other resources necessary for survival; however, it is facing critical challenges due to anthropogenic climate change, which hampers food and nutritional security. Consequently, the agriculture industry must adjust to farming issues, such as the shift in global temperatures and environmental degradation, the scarcity of farm workers, population growth, and dietary changes. Several measures have been implemented to enhance agricultural productivity, including plant breeding, genetic engineering, and precision agriculture. In recent years, the world has witnessed the burgeoning development of novel scientific innovations and technological advancements enabled by drones, smart sensors, robotics, and remote sensing, resulting in a plethora of revolutionary methods that can be applied to real-time crop modeling, high-throughput phenotyping, weather forecasting, yield prediction, fertilizer application, disease detection, market trading, farming practices, and other environmental practices vital to crop growth, yield, and quality. Furthermore, the rise in big data, advanced analytics, falling technology costs, faster internet connections, increased connectivity, and increases in computational power are all part of the current digitalization wave that has the potential to support commercial agriculture in achieving its goals of smart farming, resilience, productivity, and sustainability. These technologies enable efficient monitoring of crops, soil, and environmental conditions over large areas, providing farmers with data to support precise management that optimizes productivity and minimizes environmental impacts. Though smart farming has significant potential, challenges like high implementation costs, data security concerns, and inadequate digital literacy among farmers remain. In summary, agriculture is rapidly transforming from conventional to digital farming, offering global solutions, efficient resource utilization, and minimized input costs while fostering farmer livelihoods and economic growth. Delivering a comprehensive view of how technology could help in tackling critical issues like environmental degradation and threatened world biodiversity, this perspective emphasizes the perks of digitalization. Future advancements may involve data encryption, digital literacy, and particular economic policies.

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Section: Crop Monitoring and Phenotyping Using Artificial Intelligencementioning

confidence: 99%

Seeding a Sustainable Future: Navigating the Digital Horizon of Smart Agriculture

Balyan,

Jangir,

Tripathi

et al. 2024

Sustainability

View full text Add to dashboard Cite

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“…To increase their accuracy, AI technologies require a significant amount of data from numerous sources. This opens up the possibility of investing more in the customization of current technologies for field-data collecting, and the use of already available AI adaptable technology, such as smartphones, to boost the number and quality of data collected [ 39 ]. Smartphones have become common consumer items and the ease with which their sensors may be used suggests their great application in agriculture [ 36 ].…”

Section: Ai Technologies Overcoming the Phenomics Bottlenecksmentioning

confidence: 99%

“…Advanced signal processing on smartphones must contend with constraints such as low battery life, limited computational power, and limited bandwidth [ 36 ]. The use of citizen science in data collecting alongside professional researchers has the potential to increase the amount of data collected [ 39 ]. The main purpose of using these approaches and technology is to offer the infrastructure for tracking how plants progress during the growing season and to make the data analysis, management, and use of results via AI methods easier [ 40 ].…”

Section: Ai Technologies Overcoming the Phenomics Bottlenecksmentioning

confidence: 99%

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

Khan

Wang

et al. 2022

IJMS

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Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.

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Enabling Artificial Intelligence and Cyber Security in Smart Manufacturing

Cited by 2 publications

References 27 publications

Seeding a Sustainable Future: Navigating the Digital Horizon of Smart Agriculture

Seeding a Sustainable Future: Navigating the Digital Horizon of Smart Agriculture

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

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