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Artificial Intelligence and Intelligent Sensing Applications in Precision Agriculture
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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: 31 October 2026 | Viewed by 5600

Special Issue Editors

Prof. Dr. Dequan Zhu

E-Mail Website
Guest Editor
College of Engineering, Anhui Agricultural University, Hefei 230036, China
Interests: agricultural machinery; information perception; agricultural biomechanics
Dr. Juan Liao

E-Mail Website
Guest Editor
School of Engineering, Anhui Agricultural University, Hefei 230036, China
Interests: machine vision; deep learning; smart agriculture
Dr. Wentao Ma

E-Mail Website
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
Dr. Yuwei Wang

E-Mail Website
Guest Editor
School of Engineering, Anhui Agricultural University, Hefei 230036, China
Interests: machine vision; optical measurement; smart agriculture
Special Issues, Collections and Topics in MDPI journals

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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 (3 papers)

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Research

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22 pages, 7661 KB  
Article
YOLOv11-SMS: An Improved Algorithm for Impurity Detection in Seed Cotton
by Wenyan Yuan, Laigang Zhang, Donghe Wang and Zhijun Guo
Sensors 2026, 26(9), 2835; https://doi.org/10.3390/s26092835 - 1 May 2026
Viewed by 855
Abstract
To enhance the precision of cottonseed impurity detection and address issues such as high miss-detection rates and suboptimal performance, this paper introduces an improved YOLOv11 algorithm, termed YOLOv11-SMS. Initially, the algorithm integrates a local self-attention mechanism (LRSA) to design the C2PSA-SL module, which [...] Read more.
To enhance the precision of cottonseed impurity detection and address issues such as high miss-detection rates and suboptimal performance, this paper introduces an improved YOLOv11 algorithm, termed YOLOv11-SMS. Initially, the algorithm integrates a local self-attention mechanism (LRSA) to design the C2PSA-SL module, which augments the model’s ability to learn local information while maintaining global feature awareness. Furthermore, the feature extraction stage and the network head incorporate a multi-branch reparameterized convolution (MBRConv) module, enhancing feature extraction capabilities while preserving the model’s lightweight properties. Lastly, a spatial adaptive modulation (SAFM) module is introduced to optimize the detection of small targets. Experimental results demonstrate that YOLOv11-SMS outperforms the baseline model, with mAP@50–95 increasing from 79.42% to 82.49%, an improvement of 3.07 percentage points. The average mIOU increased from 90.98% to 94.18%, representing a 3.2 percentage point improvement. Moreover, the model achieves an impressive real-time inference speed of 178.63 frames per second (FPS), effectively balancing detection accuracy and speed, offering an efficient and precise solution for cottonseed impurity detection. Full article
(This article belongs to the Special Issue Artificial Intelligence and Intelligent Sensing Applications in Precision Agriculture)
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29 pages, 55772 KB  
Article
Distributed Artificial Intelligence for Organizational and Behavioral Recognition of Bees and Ants
by Apolinar Velarde Martinez and Gilberto Gonzalez Rodriguez
Sensors 2026, 26(2), 622; https://doi.org/10.3390/s26020622 - 16 Jan 2026
Viewed by 473
Abstract
Scientific studies have demonstrated how certain insect species can be used as bioindicators and reverse environmental degradation through their behavior and organization. Studying these species involves capturing and extracting hundreds of insects from a colony for subsequent study, analysis, and observation. This allows [...] Read more.
Scientific studies have demonstrated how certain insect species can be used as bioindicators and reverse environmental degradation through their behavior and organization. Studying these species involves capturing and extracting hundreds of insects from a colony for subsequent study, analysis, and observation. This allows researchers to classify the individuals and also determine the organizational structure and behavioral patterns of the insects within colonies. The miniaturization of hardware devices for data and image acquisition, coupled with new Artificial Intelligence techniques such as Scene Graph Generation (SGG), has evolved from the detection and recognition of objects in an image to the understanding of relationships between objects and the ability to produce textual descriptions based on image content and environmental parameters. This research paper presents the design and functionality of a distributed computing architecture for image and video acquisition of bees and ants in their natural environment, in addition to a parallel computing architecture that hosts two datasets with images of real environments from which scene graphs are generated to recognize, classify, and analyze the behaviors of bees and ants while preserving and protecting these species. The experiments that were carried out are classified into two categories, namely the recognition and classification of objects in the image and the understanding of the relationships between objects and the generation of textual descriptions of the images. The results of the experiments, conducted in real-life environments, show recognition rates above 70%, classification rates above 80%, and comprehension and generation of textual descriptions with an assertive rate of 85%. Full article
(This article belongs to the Special Issue Artificial Intelligence and Intelligent Sensing Applications in Precision Agriculture)
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Review

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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 7 | Viewed by 3511
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
(This article belongs to the Special Issue Artificial Intelligence and Intelligent Sensing Applications in Precision Agriculture)
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