Smart Agriculture System Based on Deep Learning

Fiehn, Heinz Boehmer; Schiebel, Lewis; Avila, Arturo Freydig; Miller, Bennett; Mickelson, Alan

doi:10.1145/3289100.3289126

Cited by 11 publications

(6 citation statements)

References 2 publications

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“…In the works analyzed, applied the DL tool in several proposals for solutions for combating and controlling pests in agriculture. Models that use DL were proposed for actions to map regions and perform classification for the identification and diagnosis of pests (Albattah et al 2022;Fiehn et al 2018;Liu et al 2019b;Patel;Vaghela, 2019;Tetila et al 2020a;Truon et al 2018). Liu et al (2019b) used DL in the model to estimate the severity of pest infections.…”

Section: Qp1 -What Are Computational Technologies Used To Combat and ...mentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence and Its Tools in Pest Control for Agricultural Production: A Review

Mignoni,

Soares Monteiro,

Zagonel

et al. 2024

RECIMA21

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Artificial Intelligence (AI) and its tools are being widely used worldwide. In the area of agriculture, AI is being widely studied and expanding. The use of AI in agriculture is being widely studied and expanding from pre-harvest to post-harvest. The increase in world population has triggered the need to increase food production. This need has triggered a search for solutions that promote increased food production and quality. One way to increase food production and quality is pest control. AI and its tools have proven to be a growing and rising solution in controlling and combating pests. This research focuses on reviewing and demonstrating the advances in combating and controlling pests using AI tools and images. It stands out: the classification of pests; insect identification; use and capture of Unmanned aerial vehicle (UAV) footage; using Deep Learning (DL) and Convolutional Neural Network (CNN). A search engine was applied to 5 databases. Cutting criteria were applied in 3 stages, and there were 71 papers at the end. The 71 went through 3 quality assessment questions, leaving 47 works for final analysis. This study demonstrated that the DL and the CNN tool using real images have the potential for insect control and combat solutions. Another tool in recent studies associated with CNN is the attention mechanism, improving pest identification results. Identification of insects through leaf images using CNN requires.

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Section: Qp1 -What Are Computational Technologies Used To Combat and ...mentioning

confidence: 99%

“…developed a two-step system: global hybrid resource (GaFPN) and local resource-enabled resource (LaRPN). Integrated both steps into one solution, using connected CNN to estimate the severity of pest infestations(Liu et al 2019b) Fiehn et al (2018). used DL in a model which performed well in pest identification.…”

mentioning

confidence: 99%

Artificial Intelligence and Its Tools in Pest Control for Agricultural Production: A Review

Mignoni,

Soares Monteiro,

Zagonel

et al. 2024

RECIMA21

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“…ML applications are widely used in agricultural tasks that do not require complex decision making and are routine activities (Khan, Faheem, Bashir, Wechtaisong & Abbas, 2022;Mugiyo et al, 2021;. For solving complex problems, which require more time, effort, and information processing (detection, location, and classification), deep learning, a more developed form of ML, is being applied (Dhanya et al, 2022;Fiehn, Schiebel, Avila, Miller & Mickelson, 2018;Ofori & El-Gayar, 2020). Expert systems are reported to be an efficient tool for complementing agricultural workers' decision-making , Hungilo, Emmanuel & Emanuel, 2019.…”

Section: Ai Resourcesmentioning

confidence: 99%

“…One of the major problems with AgriDSS is that, during their design and development process, the dynamism of the agricultural context is often ignored (Dhanya et al, 2022;Fiehn, Schiebel, Avila, Miller & Mickelson, 2018;. This occurs because the designs of AgriDSS are influenced by an incomplete understanding of the context and flawed perceptions of programmers and systems designers drawn on laboratory results or field experiments in controlled environments.…”

Section: Deployment Roadblocksmentioning

confidence: 99%

Using AI to Improve Sustainable Agricultural Practices: A Literature Review and Research Agenda

Lakshmi,

Corbett

2023

CAIS

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The world is confronted with the grand challenge of food insecurity amidst growing populations and the climate crisis. Artificial intelligence (AI) deployed in agricultural decision support systems (AgriDSS) raises both hopes and concerns for increasing agricultural productivity in sustainable ways. In this paper, we conduct a scoping review to uncover the roadblocks to the use of AI-supported AgriDSS in sustainable agriculture. Based on the corpus of 121 articles, we find that the effective use of AI-supported AgriDSS is hindered at technical, social, ethical, and ecological levels. Then, drawing on the experiential learning perspective, we propose how conjoint experiential learning (CEL) can enhance sustainable agricultural practices by enhancing both AI and human learning and overcoming roadblocks in using AgriDSS. Based on this conceptual framework, we build a research agenda that suggests blind spots and possible directions for future research.

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“…The actual achievement of the IoT in farming depends mostly on increased connectivity. From a telecommunications perspective, connectivity and more value-added essential services have a vast perspective and can significantly affect the whole chain [187]. Almost all telecommunication operators worldwide provide connectivity facilities, but such public services represent only a small portion of the total advanced agriculture market.…”

Section: Communicationmentioning

confidence: 99%

Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture

et al. 2021

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The agricultural industry is getting more data-centric and requires precise, more advanced data and technologies than before, despite being familiar with agricultural processes. The agriculture industry is being advanced by various information and advanced communication technologies, such as the Internet of Things (IoT). The rapid emergence of these advanced technologies has restructured almost all other industries, as well as advanced agriculture, which has shifted the industry from a statistical approach to a quantitative one. This radical change has shaken existing farming techniques and produced the latest prospects in a series of challenges. This comprehensive review article enlightens the potential of the IoT in the advancement of agriculture and the challenges faced when combining these advanced technologies with conventional agricultural systems. A brief analysis of these advanced technologies with sensors is presented in advanced agricultural applications. Numerous sensors that can be implemented for specific agricultural practices require best management practices (e.g., land preparation, irrigation systems, insect, and disease management). This review includes the integration of all suitable techniques, from sowing to harvesting, packaging, transportation, and advanced technologies available for farmers throughout the cropping system. Besides, this review article highlights the utilization of other tools such as unmanned aerial vehicles (UAVs) for crop monitoring and other beneficiary measures, such as optimizing crop yields. In addition, advanced programs based on the IoT are also discussed. Finally, based on our comprehensive review, we identified advanced prospects regarding the IoT, which are essential tools for sustainable agriculture.

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Smart Agriculture System Based on Deep Learning

Cited by 11 publications

References 2 publications

Artificial Intelligence and Its Tools in Pest Control for Agricultural Production: A Review

Artificial Intelligence and Its Tools in Pest Control for Agricultural Production: A Review

Using AI to Improve Sustainable Agricultural Practices: A Literature Review and Research Agenda

Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture

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