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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 2976

Special Issue 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-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

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

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

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Research

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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 1216
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 472
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

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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
Viewed by 808
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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