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Sensors and Machine Vision Technologies for Stress and Disease Detection...
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Sensors and Machine Vision Technologies for Stress and Disease Detection in Digital Agriculture

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 572

Special Issue Editors

Dr. Md Sultan Mahmud

E-Mail Website
Guest Editor
1. Department of Plant Pathology, University of Georgia, Athens, GA 30602, USA
2. School of Environmental, Civil, Agricultural, and Mechanical Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA
Interests: computer vision; machine learning; 3D Sensing; LiDAR sensing; automation; agricultural robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Tanzeel Ur Rehman

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Auburn University, Auburn, AL 36849, USA
Interests: hyperspectral image processing; spectral and spatial features; spatial distribution analysis; pairwise partial least square discriminant analysis; PLS-DA

Special Issue Information

Dear Colleagues,

The rapid advancement of sensor technologies and machine vision systems is revolutionizing the field of digital agriculture. These technologies significantly enhanced our ability to detect and manage stresses and diseases in crops, leading to more efficient and sustainable farming practices. Early implementations focused on basic imaging and environmental sensors, but recent developments have introduced highly sophisticated systems capable of real-time monitoring and precise intervention. This special issue aims to delve into the latest breakthroughs in sensor and machine vision technologies for stress and disease detection in digital agriculture. We invite researchers, industry experts, and policymakers to contribute their insights and findings. Topics of interest include, but are not limited to, advanced imaging techniques, sensor fusion, machine learning applications, and autonomous monitoring systems. Additionally, we seek papers that discuss the economic and environmental impacts of these technologies, as well as their potential to enhance food security and promote sustainable agricultural practices.

Dr. Md Sultan Mahmud
Dr. Tanzeel Ur Rehman
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

  • computer vision
  • artificial intelligence
  • smart sensors
  • deep learning models
  • crop sensing
  • disease detection and monitoring
  • data-driven agriculture
  • AgRobotics

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Published Papers (1 paper)

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Research

25 pages, 3068 KB  
Article
Enhanced Image Annotation in Wild Blueberry (Vaccinium angustifolium Ait.) Fields Using Sequential Zero-Shot Detection and Segmentation Models
by Connor C. Mullins, Travis J. Esau, Riley Johnstone, Chloe L. Toombs and Patrick J. Hennessy
Sensors 2025, 25(23), 7325; https://doi.org/10.3390/s25237325 - 2 Dec 2025
Viewed by 244
Abstract
This research addresses the critical need for efficient image annotation in precision agriculture, using the wild blueberry (Vaccinium angustifolium Ait.) cropping system as a representative application to enable data-driven crop management. Tasks such as automated berry ripeness detection, plant disease identification, plant [...] Read more.
This research addresses the critical need for efficient image annotation in precision agriculture, using the wild blueberry (Vaccinium angustifolium Ait.) cropping system as a representative application to enable data-driven crop management. Tasks such as automated berry ripeness detection, plant disease identification, plant growth stage monitoring, and weed detection rely on extensive annotated datasets. However, manual annotation is labor-intensive, time-consuming, and impractical for large-scale agricultural systems. To address this challenge, this study evaluates an automated annotation pipeline that integrates zero-shot detection models from two frameworks (Grounding DINO and YOLO-World) with the Segment Anything Model version 2 (SAM2). The models were tested on detecting and segmenting ripe wild blueberries, developmental wild blueberry buds, hair fescue (Festuca filiformis Pourr.), and red leaf disease (Exobasidium vaccinii). Grounding DINO consistently outperformed YOLO-World, with its Swin-T achieving mean Intersection over Union (mIoU) scores of 0.694 ± 0.175 for fescue grass and 0.905 ± 0.114 for red leaf disease when paired with SAM2-Large. For ripe wild blueberry detection, Swin-B with SAM2-Small achieved the highest performance (mIoU of 0.738 ± 0.189). Whereas for wild blueberry buds, Swin-B with SAM2-Large yielded the highest performance (0.751 ± 0.154). Processing times were also evaluated, with SAM2-Tiny, Small, and Base demonstrating the shortest durations when paired with Swin-T (0.30–0.33 s) and Swin-B (0.35–0.38 s). SAM2-Large, despite higher segmentation accuracy, had significantly longer processing times (significance level α = 0.05), making it less practical for real-time applications. This research offers a scalable solution for rapid, accurate annotation of agricultural images, improving targeted crop management. Future research should optimize these models for different cropping systems, such as orchard-based agriculture, row crops, and greenhouse farming, and expand their application to diverse crops to validate their generalizability. Full article
(This article belongs to the Special Issue Sensors and Machine Vision Technologies for Stress and Disease Detection in Digital Agriculture)
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