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Sensing Technology and Computer Vision for Precision Agriculture and Smart Farming

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1015

Special Issue Editor

Dr. Simone Pasinetti

E-Mail Website
Guest Editor
MMTLab (Mechanical and Thermal Measurements Lab), Department of Mechanical and Industrial Engineering, University of Brescia, 25123 Brescia, Italy
Interests: machine visione; precision agriculture; instrumentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global demand for food security, resource efficiency, and environmental sustainability has driven rapid advancements in agricultural technology. Recent innovations in sensor technologies and artificial intelligence (AI) are transforming traditional agricultural practices into data-driven precision agriculture systems.

This Special Issue aims to explore the development, deployment, and application of advanced sensors and intelligent systems to enhance agricultural productivity, reduce environmental impact, and support sustainable farming practices. The integration of sensors with AI technologies has revolutionized how data is collected, processed, and utilized in precision agriculture. Topics of interest include, but are not limited to, the use of multispectral and hyperspectral imaging systems for crop health analysis, smart irrigation systems powered by real-time soil moisture sensors, and AI-based predictive analytics for optimizing planting schedules and resource allocation.

The Special Issue will focus on both fundamental research and applied studies, providing insights into the challenges of sensor deployment in harsh outdoor environments, energy-efficient sensor design, and integration with IoT platforms for seamless data acquisition and management. Additionally, we welcome case studies that demonstrate the real-world impact of sensor-based technologies in improving agricultural practices.

Researchers, developers, and industry professionals are invited to contribute innovative solutions and novel methodologies that enhance the role of sensors in modern agriculture, ensuring global food security and environmental sustainability.

Dr. Simone Pasinetti
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • precision agriculture
  • AI in agriculture
  • sensor technologies
  • computer vision
  • IoT-based monitoring
  • remote sensing
  • smart farming
  • data analytics
  • environmental monitoring

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

Published Papers (3 papers)

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Research

11 pages, 1139 KiB  
Article
Comparative Study of Hops Moisture Content and the Relative Humidity of the Drying Environment in a Hop Belt Dryer
by Petr Heřmánek, Adolf Rybka, Ivo Honzík, Tomáš Hlavsa and Jiří Marčan
Sensors 2025, 25(15), 4526; https://doi.org/10.3390/s25154526 - 22 Jul 2025
Viewed by 181
Abstract
The paper concerns a study of drying and the creation of a statistical model for measuring the relative humidity of the drying environment in a belt dryer, as well as the moisture content of hop heads, stems, and bracts. The SAAZ variety was [...] Read more.
The paper concerns a study of drying and the creation of a statistical model for measuring the relative humidity of the drying environment in a belt dryer, as well as the moisture content of hop heads, stems, and bracts. The SAAZ variety was used, which is widely cultivated in the Czech Republic, and the data from harvesting seasons since 2017 were recorded. The findings demonstrated the influence and dependence of the moisture content of hop cones and their parts on the relative humidity of the drying environment in a belt dryer of hops. This dependence was confirmed by a statistical analysis of the measured values. Furthermore, a quadratic model was developed based on measurements taken over three harvest seasons. The model is applicable to predict the moisture content of the hops at a given location based on the relative humidity of the drying environment in the belt dryer and could be useful for developing an automatic hop-drying system. Full article
(This article belongs to the Special Issue Sensing Technology and Computer Vision for Precision Agriculture and Smart Farming)
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20 pages, 4148 KiB  
Article
Automated Discrimination of Appearance Quality Grade of Mushroom (Stropharia rugoso-annulata) Using Computer Vision-Based Air-Blown System
by Meng Lv, Lei Kong, Qi-Yuan Zhang and Wen-Hao Su
Sensors 2025, 25(14), 4482; https://doi.org/10.3390/s25144482 - 18 Jul 2025
Viewed by 275
Abstract
The mushroom Stropharia rugoso-annulata is one of the most popular varieties in the international market because it is highly nutritious and has a delicious flavor. However, grading is still performed manually, leading to inconsistent grading standards and low efficiency. In this study, deep [...] Read more.
The mushroom Stropharia rugoso-annulata is one of the most popular varieties in the international market because it is highly nutritious and has a delicious flavor. However, grading is still performed manually, leading to inconsistent grading standards and low efficiency. In this study, deep learning and computer vision techniques were used to develop an automated air-blown grading system for classifying this mushroom into three quality grades. The system consisted of a classification module and a grading module. In the classification module, the cap and stalk regions were extracted using the YOLOv8-seg algorithm, then post-processed using OpenCV based on quantitative grading indexes, forming the proposed SegGrade algorithm. In the grading module, an air-blown grading system with an automatic feeding unit was developed in combination with the SegGrade algorithm. The experimental results show that for 150 randomly selected mushrooms, the trained YOLOv8-seg algorithm achieved an accuracy of 99.5% in segmenting the cap and stalk regions, while the SegGrade algorithm achieved an accuracy of 94.67%. Furthermore, the system ultimately achieved an average grading accuracy of 80.66% and maintained the integrity of the mushrooms. This system can be further expanded according to production needs, improving sorting efficiency and meeting market demands. Full article
(This article belongs to the Special Issue Sensing Technology and Computer Vision for Precision Agriculture and Smart Farming)
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25 pages, 6123 KiB  
Article
SDA-YOLO: An Object Detection Method for Peach Fruits in Complex Orchard Environments
by Xudong Lin, Dehao Liao, Zhiguo Du, Bin Wen, Zhihui Wu and Xianzhi Tu
Sensors 2025, 25(14), 4457; https://doi.org/10.3390/s25144457 - 17 Jul 2025
Viewed by 337
Abstract
To address the challenges of leaf–branch occlusion, fruit mutual occlusion, complex background interference, and scale variations in peach detection within complex orchard environments, this study proposes an improved YOLOv11n-based peach detection method named SDA-YOLO. First, in the backbone network, the LSKA module is [...] Read more.
To address the challenges of leaf–branch occlusion, fruit mutual occlusion, complex background interference, and scale variations in peach detection within complex orchard environments, this study proposes an improved YOLOv11n-based peach detection method named SDA-YOLO. First, in the backbone network, the LSKA module is embedded into the SPPF module to construct an SPPF-LSKA fusion module, enhancing multi-scale feature representation for peach targets. Second, an MPDIoU-based bounding box regression loss function replaces CIoU to improve localization accuracy for overlapping and occluded peaches. The DyHead Block is integrated into the detection head to form a DMDetect module, strengthening feature discrimination for small and occluded targets in complex backgrounds. To address insufficient feature fusion flexibility caused by scale variations from occlusion and illumination differences in multi-scale peach detection, a novel Adaptive Multi-Scale Fusion Pyramid (AMFP) module is proposed to enhance the neck network, improving flexibility in processing complex features. Experimental results demonstrate that SDA-YOLO achieves precision (P), recall (R), mAP@0.95, and mAP@0.5:0.95 of 90.8%, 85.4%, 90%, and 62.7%, respectively, surpassing YOLOv11n by 2.7%, 4.8%, 2.7%, and 7.2%. This verifies the method’s robustness in complex orchard environments and provides effective technical support for intelligent fruit harvesting and yield estimation. Full article
(This article belongs to the Special Issue Sensing Technology and Computer Vision for Precision Agriculture and Smart Farming)
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