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Smart Sensors in Precision Agriculture
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Smart Sensors in Precision Agriculture

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

Deadline for manuscript submissions: 25 December 2026 | Viewed by 5687

Editor

Dr. Carlos Campillo

E-Mail Website
Guest Editor
Centre for Scientific and Technological Research of Extremadura (CICYTEX), Department of Horticulture, Finca La Orden, Regional Government of Extremadura, Highway A-V, Km 372, 06187 Guadajira, Badajoz, Spain
Interests: water use efficiency; precision fertilization and irrigation; digital agriculture; remote sensing; crop and soil monitoring; crop and soil modelling; irrigation and fertilization scheduling; automatic irrigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart sensors play a crucial role in Precision Agriculture by providing real-time, accurate data that enable farmers to make informed decisions. These sensors, integrated with IoT (Internet of Things), AI, and data analytics, optimize resource usage, enhance crop yields, and ensure sustainable farming practices. Smart sensors are revolutionizing precision agriculture by transforming traditional farming into a data-driven, efficient, and sustainable practice. By enabling the real-time monitoring of soil, crops, and environmental conditions, these sensors empower farmers to make smarter decisions, optimize resources, and improve the overall productivity of farms while reducing their environmental impact. These technologies also facilitate the management and interpretation of this information, with Artificial Intelligence (AI) transforming smart agriculture by enabling data-driven decision-making, predictive analytics, and automation. Digital Twin technology has the potential to revolutionize agriculture by enabling data-driven, efficient, and sustainable farming practices. Via the integration of real-time data from IoT sensors, drones, satellite imagery, weather stations, and other sources, digital twins provide a dynamic and data-driven model of farms in a sensor network, allowing the generation of automated integrated decision-making systems that  consider the spatial variability of farms when making decisions related to each part of the farm and evaluating their effect.

Dr. Carlos Campillo
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

  • soil moisture sensors
  • irrigation and fertilization management sensors
  • crop monitoring sensors
  • spatial variability management
  • precision agriculture
  • digital twin integration
  • decision support system
  • AI & machine learning integration
  • automation of management process in precision agriculture
  • variable rate technology
  • wireless sensor networks
  • blockchain integration in precision agriculture

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Published Papers (5 papers)

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Research

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15 pages, 32174 KB  
Article
YOLO-FSEP: An Improved YOLOv8n Algorithm for Sugar Orange Detection in Orchards
by Tianfa Deng, Jinchao Sun, Qingjuan Zhao and Faguo Huang
Sensors 2026, 26(12), 3848; https://doi.org/10.3390/s26123848 - 17 Jun 2026
Viewed by 161
Abstract
To address the challenges of detecting sugar orange fruits in complex natural orchard environments—where fruits are frequently occluded by leaves and branches and may be mutually occluded due to dense growth, leading to missed detections, false positives, and low detection confidence—we propose an [...] Read more.
To address the challenges of detecting sugar orange fruits in complex natural orchard environments—where fruits are frequently occluded by leaves and branches and may be mutually occluded due to dense growth, leading to missed detections, false positives, and low detection confidence—we propose an improved algorithm based on YOLOv8n, named YOLO-FSEP. A Spatial-Channel Synergistic Attention (SCSA) module is introduced into the main network to enhance feature extraction capabilities; the IoU loss function is replaced with Focal_SIOU to improve the detection accuracy for difficult samples; and an SE attention mechanism is embedded in the detection head, with the addition of a P6 high-resolution detection layer to optimize multi-scale object performance. Experimental results on a self-built sugar orange dataset show that, compared to the baseline YOLOv8n, the improved model achieves a 0.9% increase in accuracy, a 1.3% increase in recall, and a 3.2% increase in mAP50-95, while maintaining an inference speed of 62.6 FPS. To evaluate the model under dynamic conditions, we performed a 200-frame continuous test of the 3D localization pipeline on a laptop with a RealSense D435i camera. The average YOLO inference time was 49.90 ms, post-processing (depth extraction and 3D coordinate conversion) took 0.24 ms, and the total processing time was 50.15 ms. Given that the typical response time for a robotic arm’s single positioning operation is 100–200 ms, this real-time performance meets the dynamic localization requirements of sugar orange harvesting. Full article
(This article belongs to the Special Issue Smart Sensors in Precision Agriculture)
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23 pages, 4187 KB  
Article
Latent Salinity Stress Detection in Opuntia ficus-indica Using Hyperspectral Imaging and a 3D-CNN Framework
by Juan Arredondo-Valdez, Horacio Abdiel Rodríguez-Garza, Héctor Flores-Breceda, Zayd Eliud Rangel-Nava, Néstor Everardo Aranda-Ledesma, Jesús Rodolfo Valenzuela-García, Moisés Hinojosa-Rivera, Ajay Kumar, Urbano Luna-Maldonado and Alejandro Isabel Luna-Maldonado
Sensors 2026, 26(12), 3641; https://doi.org/10.3390/s26123641 - 7 Jun 2026
Viewed by 397
Abstract
Salinity stress remains a major bottleneck for agriculture in arid regions. While Opuntia ficus-indica is known for its resilience, its young cladodes maintain a misleadingly healthy visual appearance and stable biomass even under heavy saline pressure, making traditional vegetation indices and standard statistics [...] Read more.
Salinity stress remains a major bottleneck for agriculture in arid regions. While Opuntia ficus-indica is known for its resilience, its young cladodes maintain a misleadingly healthy visual appearance and stable biomass even under heavy saline pressure, making traditional vegetation indices and standard statistics unreliable for early diagnosis. The objective of this study was to develop a non-destructive phenotyping framework for the early detection of latent salinity stress in young Opuntia cladodes. Controlled experiments were conducted using hyperspectral data cubes (400–1000 nm) acquired from plants exposed to six distinct salinity levels ranging from 2 to 21 dS m−1. Our methodology integrates these high-dimensional spatial–spectral data with a tailor-made 3D Convolutional Neural Network (3D-CNN). Seven physiological vegetation indices—NDVI, PRI, WI, PSRI, MCARI, SIPI, and NDRE were extracted to track sub-clinical shifts and processed as a volumetric depth dimension within the network to preserve spatial–spectral integrity. The optimized 3D-CNN framework achieved a validation accuracy of 99.7% and a weighted F1-score of 99.1%, delivering 100% precision at critical stress thresholds (13 and 21 dS m−1). Spatial confidence maps (Softmax > 0.95) further confirmed the high reliability of the diagnostic output. Requiring a training duration of approximately 8 s, this framework provides a robust basis for precision early-warning irrigation systems to sustain Opuntia cultivation in challenging environments. Full article
(This article belongs to the Special Issue Smart Sensors in Precision Agriculture)
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16 pages, 6928 KB  
Article
An M5Stamp Pico-Based IoT Soil Monitoring System for Soil Water–Salinity Diagnosis in a Coastal Reclaimed Pepper Greenhouse
by Leon Nakayama and Ieyasu Tokumoto
Sensors 2026, 26(11), 3309; https://doi.org/10.3390/s26113309 - 22 May 2026
Viewed by 439
Abstract
Coastal reclaimed polders with shallow saline groundwater support intensive greenhouse horticulture but require timely diagnosis of root-zone water and salinity conditions. This study developed a compact Internet-of-Things (IoT) monitoring system based on the M5Stamp Pico microcontroller to acquire SDI-12 soil-sensor data, buffer records [...] Read more.
Coastal reclaimed polders with shallow saline groundwater support intensive greenhouse horticulture but require timely diagnosis of root-zone water and salinity conditions. This study developed a compact Internet-of-Things (IoT) monitoring system based on the M5Stamp Pico microcontroller to acquire SDI-12 soil-sensor data, buffer records locally, and transfer them to a low-cost cloud dashboard. Outside-greenhouse validation showed high operational reliability, with a missing observation rate of only 0.9%, and acceptable agreement with a reference TDR100 for both volumetric water content (θ) and bulk electrical conductivity (ECb). The system was then applied to ridge-position monitoring in a commercial pepper greenhouse on a coastal reclaimed polder. The ridge records captured depth-dependent infiltration and salinity redistribution under drip irrigation, together with contrasting responses between the cultivated layer and shallow groundwater. Potential-based interpretation indicated that the monitored ridge root zone was often not strongly limited by matric potential, whereas osmotic potential derived from pore-water salinity showed reduced water availability even when the soil remained relatively wet. These results demonstrate that continuous real-time monitoring at the ridge position can support diagnosis of root-zone stress and provide useful information for irrigation and fertigation management in salt-affected greenhouse soils. Full article
(This article belongs to the Special Issue Smart Sensors in Precision Agriculture)
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25 pages, 10711 KB  
Article
Research on Enhanced Dynamic Pig Counting Based on YOLOv8n and Deep SORT
by Peng Shen, Keyu Mei, Haori Xue, Tenglong Li, Guoqing Zhang, Yongxiang Zhao, Wei Luo and Liang Mao
Sensors 2025, 25(9), 2680; https://doi.org/10.3390/s25092680 - 24 Apr 2025
Cited by 6 | Viewed by 1708
Abstract
Pig counting is an essential activity in the administration of pig farming. Currently, manual counting is inefficient, costly, and unsuitable for systematic analysis. However, research on dynamic pig counting encounters challenges, including inadequate detection accuracy stemming from crowding, occlusion, deformation, and low-light conditions. [...] Read more.
Pig counting is an essential activity in the administration of pig farming. Currently, manual counting is inefficient, costly, and unsuitable for systematic analysis. However, research on dynamic pig counting encounters challenges, including inadequate detection accuracy stemming from crowding, occlusion, deformation, and low-light conditions. Target tracking issues characterized by poor accuracy, frequent identity confusion, and false positive trajectories ultimately lead to diminished accuracy in the final counting outcomes. Given these existing limitations, this paper proposes an enhanced algorithm based on the YOLOv8n+Deep SORT model. The ELA attention mechanism, GSConv, and VoVGSCSP lightweight convolution modules are introduced in YOLOv8n, which improve detection accuracy and speed for pig target recognition. Additionally, Deep SORT is enhanced by integrating the DenseNet feature extraction network and CIoU matching algorithm, improving the accuracy and stability of target tracking. Experimental results indicate that the improved Deep SORT-P pig tracking algorithm attains MOTA and MOTP values of 89.2% and 90.4%, respectively, reflecting improvements of 4.2% and 1.7%, while IDSW is diminished by 25.5%. Finally, counting experiments were performed on videos of pigs traversing the farm passage using both the original and improved algorithms. The improved YOLOv8n-EGV+Deep SORT-P algorithm achieved a counting accuracy of 92.1%, reflecting a 17.5% improvement over the original algorithm. Meanwhile, the improved algorithm presented in this study successfully attained stable dynamic pig counting in practical environments, offering valuable data and references for research on dynamic pig counting. Full article
(This article belongs to the Special Issue Smart Sensors in Precision Agriculture)
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Review

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28 pages, 280 KB  
Review
Research Progress and Prospects of Intelligent Measurement and Control Technology for Tillage Depth in Subsoiling Operations
by Yue Deng, Wenyi Zhang, Bing Qi, Yunxia Wang, Youqiang Ding and Haojie Zhang
Sensors 2025, 25(12), 3821; https://doi.org/10.3390/s25123821 - 19 Jun 2025
Cited by 9 | Viewed by 1946
Abstract
Deep tillage is a conservation tillage method that aims to break the plow pan layer. It provides significant benefits, including enhanced root development, improved soil quality, and substantial increases in crop yields. The depth of tillage is a crucial factor in assessing the [...] Read more.
Deep tillage is a conservation tillage method that aims to break the plow pan layer. It provides significant benefits, including enhanced root development, improved soil quality, and substantial increases in crop yields. The depth of tillage is a crucial factor in assessing the effectiveness of deep tillage operations. Accurate regulation of tillage depth in deep tillage equipment is vital for ensuring the high-quality and efficient execution of these practices. The distribution of mechanical resistance within the soil can effectively indicate the location of the plow pan layer and serves as the main reference for setting the tillage depth for machinery. This paper examined the current state of research on tillage depth control technology for deep tillage operations. It focused on three main technical areas: soil mechanical resistance detection, tillage depth measurement, and tillage depth regulation. The report discussed the working principles of various technologies and compared the existing methods. Additionally, the paper analyzed the challenges faced in the development of tillage depth control technology in China and offers recommendations for future advancements. It highlighted that leveraging information and digital technologies to determine the distribution of the soil plow pan layer, along with the integration of efficient and intelligent control technologies for precise tillage depth regulation, represented a key direction for the future development of deep tillage operations. Full article
(This article belongs to the Special Issue Smart Sensors in Precision Agriculture)
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