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Feature Papers in Smart Agriculture 2026
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Feature Papers in Smart Agriculture 2026

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 4556

Editor

Prof. Dr. Wonsuk (Daniel) Lee

E-Mail Website
Guest Editor
Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611-0570, USA
Interests: precision agriculture; artificial intelligence; sensor development; machine vision/image processing; GNSS/GIS; variable rate technology; yield mapping; machine systems design; instrumentation; remote sensing; NIR spectroscopy; farm automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the Smart Agriculture section is now compiling a collection of papers submitted by the Editorial Board Members (EBMs) of our section and outstanding scholars in this research field. We welcome contributions as well as recommendations from the EBMs.

The purpose of this Special Issue is to publish a set of papers that typifies the very best insightful and influential original articles or reviews in which our section’s EBMs and outstanding scholars discuss key topics in the field. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be published in a printed edition book after the deadline and will be extensively promoted. The Special Issue engages in topics such as artificial intelligence, IoT, UAVs, and robots and their applications in the field of smart farming, precision livestock management, aquaculture, greenhouse technology, etc. In addition, any articles related to smart agriculture that highlight technological innovation in software and hardware development applied to crop and animal production are welcome.

We would also like to take this opportunity to ask more scholars to join the Smart Agriculture section so that we can work together to further develop this exciting field of research.

Prof. Dr. Wonsuk (Daniel) Lee
Guest Editor

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-anonymized 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

  • sensor
  • artificial intelligence
  • IoT
  • UAV
  • robot
  • smart agriculture
  • smart farming
  • precision livestock management

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Related Special Issue

Published Papers (6 papers)

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Research

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25 pages, 15591 KB  
Article
A Comparative Benchmark of Real-Time Detectors for Canopy Image-Based Blueberry Detection Toward Precision Orchard Management
by Xinyang Mu, Yuzhen Lu and Boyang Deng
Sensors 2026, 26(14), 4373; https://doi.org/10.3390/s26144373 - 10 Jul 2026
Abstract
Computer vision with artificial intelligence (AI) offers a promising tool for blueberry growers to accomplish orchard tasks such as harvest maturity assessment and yield estimation, which otherwise would be labor-intensive and prone to error. However, blueberry detection in natural environments remains challenging due [...] Read more.
Computer vision with artificial intelligence (AI) offers a promising tool for blueberry growers to accomplish orchard tasks such as harvest maturity assessment and yield estimation, which otherwise would be labor-intensive and prone to error. However, blueberry detection in natural environments remains challenging due to variable natural lighting, frequent occlusions by leaves and branches, and motion blur due to environmental factors and imaging devices. AI models such as deep learning-based object detectors promise to address these challenges, but they are data-driven, demanding a large-scale, diverse dataset that captures the complexities of real-world orchard conditions. Deployment of these models in practical scenarios often faces limited computing resources, highlighting the importance of achieving the right accuracy/speed/memory trade-off in model selection. This study presents a novel comparative benchmark analysis of advanced real-time object detectors, including YOLO (You Only Look Once) (v8–v12) and RT-DETR (Real-Time Detection Transformers) (v1–v2) families, consisting of 36 model variants, evaluated on a newly curated large dataset for blueberry detection. This dataset contained 661 canopy images collected with smartphones during the 2022–2023 seasons, consisting of 85,879 manually annotated instances (including 36,256 ripe and 49,623 unripe blueberries) that represent a broad range of lighting conditions, occlusions, and fruit maturity stages. Among the YOLO models, YOLOv12m achieved the best accuracy with a mAP@50 of 93.3%, while RT-DETRv2-X obtained a mAP@50 of 93.6%, the highest among all RT-DETR variants. The inference time varied with the model scale and complexity, and the mid-sized models appeared to offer a good balance between accuracy and speed. To further improve fruit detection performance, all models were fine-tuned using Unbiased Mean Teacher-based semi-supervised learning (SSL) with 1644 cross-source unlabeled canopy images acquired from ground-based machine vision platforms. SSL resulted in accuracy improvements of up to 2.0%, with RT-DETR-v2-X achieving the highest mAP@50 of 95.5%. These findings highlight the efficacy of SSL for leveraging cross-domain unlabeled data, although further research is needed to fully exploit its benefits. The curated dataset and developed software programs are publicly available to facilitate further research and practical deployment. Full article
(This article belongs to the Special Issue Feature Papers in Smart Agriculture 2026)
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11 pages, 7674 KB  
Article
Detection of Chewing Strokes from Jaw Movement Signals in Dairy Cows Using a Nose-Mounted Accelerometer
by Saskia Strutzke, Daniel Fiske and Gundula Hoffmann
Sensors 2026, 26(13), 4148; https://doi.org/10.3390/s26134148 - 1 Jul 2026
Viewed by 233
Abstract
This study evaluated a non-invasive nose-mounted accelerometer for automated detection of chewing strokes in dairy cows. Data were collected from 15 Holstein Friesians and validated against manual video annotations. Chewing strokes were identified using a peak detection algorithm applied to smoothed acceleration data. [...] Read more.
This study evaluated a non-invasive nose-mounted accelerometer for automated detection of chewing strokes in dairy cows. Data were collected from 15 Holstein Friesians and validated against manual video annotations. Chewing strokes were identified using a peak detection algorithm applied to smoothed acceleration data. Two algorithm versions were analyzed: a raw version and a cleaned version that excluded a five-second interval during regurgitation, where no physiological chewing occurs. The cleaned version showed higher agreement with the reference method (Intraclass Correlation Coefficient [ICC] = 0.91; 95% Confidence Interval [CI]: 0.77–0.96) and lower error metrics (Mean Absolute Error [MAE]: 3.67; Root Mean Square Error [RMSE]: 4.72; Mean Absolute Percentage Error [MAPE]: 5.64%) compared to the raw version (ICC = 0.67; MAE: 10.00; RMSE: 11.48; MAPE: 15.27%). Both methods demonstrated that reliable detection of chewing activity is feasible using this sensor system. Automated chewing stroke detection may contribute to the assessment of rumen function, feeding behaviour, and animal welfare and may support future precision livestock farming applications by providing objective information on chewing activity. Full article
(This article belongs to the Special Issue Feature Papers in Smart Agriculture 2026)
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22 pages, 7492 KB  
Article
IoT-Based Precision Irrigation System Featuring Multi-Sensor Monitoring and Scheduled Automated Water-Control Gates for Rice Production
by Mir Nurul Hasan Mahmud, Younsuk Dong, Md Mahbubul Alam and Jinat Sharmin
Sensors 2026, 26(9), 2692; https://doi.org/10.3390/s26092692 - 26 Apr 2026
Viewed by 1708
Abstract
Despite its significant water-saving potential, the adoption of alternate wetting and drying (AWD) irrigation remains limited due to infrastructure constraints and intensive manual monitoring requirements. An automated precision irrigation system was developed and tested at the Bangladesh Rice Research Institute research farm in [...] Read more.
Despite its significant water-saving potential, the adoption of alternate wetting and drying (AWD) irrigation remains limited due to infrastructure constraints and intensive manual monitoring requirements. An automated precision irrigation system was developed and tested at the Bangladesh Rice Research Institute research farm in Gazipur, Bangladesh. The system combined ultrasonic water-level sensors, capacitive soil moisture sensors, an Arduino-based microcontroller, a GSM communication module, and solar-powered automatic control gates. Field performance was evaluated following a Randomized Complete Block Design (RCBD) under four irrigation treatments: IRRISAT, IRRI35, IRRI25, and continuous flooding (CF). The first three irrigation treatments were operated using scheduled daily decision windows, in which irrigation actions were automatically triggered based on predefined schedules and sensor threshold values. In IRRISAT, irrigation started when soil moisture dropped slightly below saturation and stopped at a ponding depth of 5 cm, while IRRI35 and IRRI25 were triggered at volumetric soil water contents of 35% and 25%, respectively, with the same upper cutoff of 5 cm ponding depth; CF served as the control. The IRRI35 treatment achieved a high grain yield (7.76 t ha−1) while reducing water use by 28% and energy consumption by 37% compared to CF. Water use efficiency was considerably higher under IRRI35 (9.4 kg ha−1 mm−1) than under CF (6.7 kg ha−1 mm−1). The automated system proved to be reliable and precise in scheduled irrigation control, significantly reducing water use and labor requirements. The findings suggest that large-scale adoption of the system under real-world cultivation conditions could reduce irrigation energy needs and contribute to sustainable water governance in rice production. Full article
(This article belongs to the Special Issue Feature Papers in Smart Agriculture 2026)
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25 pages, 12478 KB  
Article
RD-GuideNet: A Depth-Guided Framework for Robust Detection, Segmentation, and Temporal Tracking of White Button Mushrooms
by Namrata Dutt, Daeun Choi, Yiannis Ampatzidis, Won Suk Lee, Sanjeev J. Koppal and Xu Wang
Sensors 2026, 26(6), 1935; https://doi.org/10.3390/s26061935 - 19 Mar 2026
Viewed by 582
Abstract
Mushroom farms in the United States continue to face persistent labor shortages, especially during the harvesting of white button mushrooms (Agaricus bisporus) which requires selective picking by skilled workers. This study addresses this challenge by developing a depth-guided computer vision framework [...] Read more.
Mushroom farms in the United States continue to face persistent labor shortages, especially during the harvesting of white button mushrooms (Agaricus bisporus) which requires selective picking by skilled workers. This study addresses this challenge by developing a depth-guided computer vision framework for automated mushroom detection, segmentation, and tracking to support timely harvest decisions, providing the foundation needed to support selective and timely robotic harvesting. The specific objectives of the study were to (1) develop a novel image-processing algorithm (RD-GuideNet) that integrates RGB and depth images for accurate detection and segmentation of mushrooms; (2) implement a custom depth-guided tracking algorithm to preserve mushroom identities across sequential frames; (3) compare the performance of RD-GuideNet against state-of-the-art deep learning models, YOLOv8 and YOLOv11, focusing on segmentation and tracking accuracies. The proposed RD-GuideNet achieved an F1-score of 0.93 for segmentation, outperforming YOLOv8 (0.88) and YOLOv11 (0.86), and produced sharper, more geometrically consistent boundaries that closely followed true mushroom cap contours. Its tracking consistency reached 92.7%, compared to YOLOv8 (95.3%) and YOLOv11 (94.6%). Although slightly lower, RD-GuideNet maintained high temporal consistency across dense mushroom beds. These results suggest that depth-based geometric reasoning and deep learning approaches exhibit complementary strengths in dense production scenes. Combining the two may further enhance detection reliability and shape fidelity, supporting high-precision perception for autonomous mushroom harvesting. A comprehensive quantitative evaluation of such a hybrid framework will be investigated in future work. Full article
(This article belongs to the Special Issue Feature Papers in Smart Agriculture 2026)
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Review

Jump to: Research

44 pages, 2461 KB  
Review
Computer Vision for Cattle Health and Welfare Monitoring: A Comprehensive Review of Methods, Applications, and Interdisciplinary Integration in Smart Agriculture
by Md Nafiul Islam, J. Lannett Edwards, Robert Burns, Hairong Qi and Hao Gan
Sensors 2026, 26(13), 4271; https://doi.org/10.3390/s26134271 - 4 Jul 2026
Viewed by 426
Abstract
The global cattle industry is experiencing significant growth, requiring advanced methods for monitoring animal health and welfare to ensure productivity and sustainability. Traditional manual monitoring techniques are labor-intensive and often impractical for large-scale operations. This review provides a comprehensive analysis of existing and [...] Read more.
The global cattle industry is experiencing significant growth, requiring advanced methods for monitoring animal health and welfare to ensure productivity and sustainability. Traditional manual monitoring techniques are labor-intensive and often impractical for large-scale operations. This review provides a comprehensive analysis of existing and emerging computer vision tools applied to the monitoring of cattle health and welfare. By systematically examining studies across major databases, this paper addresses six key research questions focusing on (1) the issues addressed by computer vision technologies, (2) data acquisition systems, (3) implemented techniques and algorithms, (4) performance outcomes, (5) challenges faced, and (6) potential applications for underexplored health and welfare aspects in cattle farming. The findings show that computer vision technologies have significantly progressed in areas such as body condition score detection, lameness detection, weight estimation, estrus detection, monitoring of feeding and drinking behavior, breathing detection, and recognition of general behaviors. Despite the progress, challenges such as variability in environmental conditions, the need for large annotated datasets, and the high cost of advanced imaging equipment persist. The review emphasizes future research opportunities to address these challenges by focusing on disease-specific monitoring. This review aims to provide veterinarians, farmers, and animal health professionals with greater insight into computer vision technologies and to promote their adoption by discussing their practical applications. Full article
(This article belongs to the Special Issue Feature Papers in Smart Agriculture 2026)
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33 pages, 2402 KB  
Review
Toward Advanced Sensing and Data-Driven Approaches for Maturity Assessment of Indeterminate Peanut Cropping Systems: Review of Current State and Prospects
by Sathish Raymond Emmanuel Sahayaraj, Abhilash K. Chandel, Pius Jjagwe, Ranadheer Reddy Vennam, Maria Balota and Arunachalam Manimozhian
Sensors 2026, 26(7), 2208; https://doi.org/10.3390/s26072208 - 2 Apr 2026
Cited by 1 | Viewed by 963
Abstract
Determining the optimal harvest time is among the most critical economic decisions for peanut (Arachis hypogaea L.) growers, directly influencing yield, quality, and market value. Unlike many other crops, peanuts are indeterminate, continuing to flower and produce pods throughout their life cycle. [...] Read more.
Determining the optimal harvest time is among the most critical economic decisions for peanut (Arachis hypogaea L.) growers, directly influencing yield, quality, and market value. Unlike many other crops, peanuts are indeterminate, continuing to flower and produce pods throughout their life cycle. As a result, pod development and maturation are asynchronous, making harvest timing particularly challenging. Conventional maturity estimation techniques, including the hull scrape method, pod blasting, and visual maturity profiling, are invasive, labor-intensive, time-consuming, and spatially limited. Moreover, differences in cultivar maturity rates and agroclimatic conditions exacerbate inconsistencies in maturity prediction. These challenges highlight the urgent need for scalable, objective, and data-driven methods to support growers in achieving optimal harvest outcomes. This review synthesizes the current understanding of peanut pod maturity and evaluates existing traditional and non-invasive approaches for maturity estimation. It aims to identify the limitations of conventional techniques and explore the integration of advanced sensing technologies, artificial intelligence (AI), and geospatial analytics to enhance precision and scalability in peanut maturity assessment and harvest decision-making. This review examines traditional destructive techniques such as the hull scrape method and pod blasting, followed by emerging non-invasive methods employing proximal and remote sensing platforms. Applications of vegetation indices, multispectral and hyperspectral imaging, and AI-based data analytics are discussed in the context of maturity prediction. Additionally, the potential of multimodal remote sensing data fusion and digital frameworks integrating spatial big data analytics, centralized data management, and cloud-based graphical interfaces is explored as a pathway toward end-to-end decision-support systems. Recent advances in non-invasive sensing and AI-assisted modeling have demonstrated significant improvements in scalability, precision, and automation compared with traditional manual approaches. However, their effectiveness remains constrained by the limited inclusion of agroclimatic, phenological, and cultivar-specific variables. Furthermore, the translation of model outputs into actionable, field-level harvest decisions is still underdeveloped, underscoring the need for integrated, user-centric digital infrastructure. Achieving a robust and transferable digital peanut maturity estimation system will require comprehensive ground-truth data across cultivars, regions, and growing seasons. Multidisciplinary collaborations among agronomists, data scientists, growers, and technology providers will be essential for developing practical, field-ready solutions. Integrating AI, multimodal sensing, and geospatial analytics holds immense potential to transform peanut maturity estimation. Such innovations promise to enhance harvest precision, economic returns, and sustainability while reducing manual effort and uncertainty, ultimately improving the efficiency and quality of life for peanut producers worldwide. Full article
(This article belongs to the Special Issue Feature Papers in Smart Agriculture 2026)
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