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Artificial Intelligence and Intelligent Sensing Applications in Precision Agriculture

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: closed (31 October 2025) | Viewed by 2378

Special Issue Editors


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Guest Editor
College of Engineering, Anhui Agricultural University, Hefei 230036, China
Interests: agricultural machinery; information perception; agricultural biomechanics

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Guest Editor
School of Engineering, Anhui Agricultural University, Hefei 230036, China
Interests: machine vision; deep learning; smart agriculture

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Guest Editor
Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing, School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China
Interests: multimodal fusion

Special Issue Information

Dear Colleagues,

Intelligent sensing technologies are transforming agriculture into a data-driven industry, and artificial intelligence is an important part of precision agriculture. Applying artificial intelligence and intelligent sensing technologies can significantly enhance productivity, efficiency, and sustainability. The temporal, spatial, and individual information related to the growing environment of crops and crop characteristics are gathered through various intelligent sensing technologies. Artificial intelligence algorithms, including deep learning, image processing, and multimodal fusion, are then applied to process or combine and analyze data from various sources to achieve agricultural intelligent production and management, such as with precision irrigation, crop monitoring, fertilizer inputs, etc..

This Special Issue seeks to present the most recent research on artificial intelligence or intelligent sensing technologies in regard to precision agriculture. Authors are encouraged to submit high-quality research papers on intelligent agricultural sensors, information fusion technology, crop recognition, disease and pest detection, autonomous navigation technology, growth state recognition, agricultural robots (weeding, planting, fertilization, etc.), and other related topics.

Prof. Dr. Dequan Zhu
Dr. Juan Liao
Dr. Wentao Ma
Dr. Yuwei Wang
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • image processing
  • multimodal fusion
  • intelligent sensing
  • precision agriculture

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Published Papers (1 paper)

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Review

38 pages, 3011 KB  
Review
Harnessing Beneficial Microbes and Sensor Technologies for Sustainable Smart Agriculture
by Younes Rezaee Danesh
Sensors 2025, 25(21), 6631; https://doi.org/10.3390/s25216631 - 29 Oct 2025
Cited by 1 | Viewed by 1837
Abstract
The integration of beneficial microorganisms with sensor technologies represents a transformative advancement toward sustainable smart agriculture. This review synthesizes recent progress in combining microbial bioinoculants with sensor-based monitoring systems to enhance crop productivity, resource-use efficiency, and environmental resilience. Beneficial bacteria and fungi improve [...] Read more.
The integration of beneficial microorganisms with sensor technologies represents a transformative advancement toward sustainable smart agriculture. This review synthesizes recent progress in combining microbial bioinoculants with sensor-based monitoring systems to enhance crop productivity, resource-use efficiency, and environmental resilience. Beneficial bacteria and fungi improve nutrient cycling, stress tolerance, and soil fertility thereby reducing the reliance on chemical fertilizers and pesticides. In parallel, sensor networks—including soil moisture, nutrient, environmental, and remote-sensing platforms—enable real-time, data-driven management of agroecosystems. Integrated microbe–sensor approaches have demonstrated 10–25% yield increases and up to 30% reductions in agrochemical inputs under optimized field conditions. We propose an integrative Microbe–Sensor Closed Loop (MSCL) framework in which microbial activity and sensor feedback interact dynamically to optimize inputs, monitor plant–soil interactions, and sustain productivity. Key applications include precision fertilization, stress diagnostics, and early detection of nutrient or pathogen imbalances. The review also highlights barriers to large-scale adoption, such as variable field performance of inoculants, high sensor costs, and limited interoperability of data systems. Addressing these challenges through standardization, cross-disciplinary collaboration, and farmer training will accelerate the transition toward climate-smart, self-regulating agricultural systems. Collectively, the integration of biological and technological innovations provides a clear pathway toward resilient, resource-efficient, and ecologically sound food production. Full article
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