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Smart Sensors for Sustainable Agriculture
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Smart Sensors for Sustainable Agriculture

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

Deadline for manuscript submissions: 20 August 2025 | Viewed by 4186

Special Issue Editors

Dr. Nick Sigrimis

E-Mail Website1 Website2
Guest Editor
Geosmart Automations Systems, CTO. Emeritus Professor of Agricultural University of Athens, School of Environment and Agricultural Engineering, X-Director of Laboratory of Farm Machinery and Automation, Athens, Greece
Interests: automation and electronics in agriculture; sensors; wireless sensor networks; precision agriculture; artificial intelligence; machine learning; smart sensors; edge computing and reinforcement learning; smart agriculture; Internet of Things; embedded intelligence; technology governance; Agriculture 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Konstantinos G. Arvanitis

E-Mail Website
Guest Editor
Department of Natural Resources Development and Agricultural Engineering, School of Environment and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos Street, 11855 Athens, Greece
Interests: process control; computational intelligence; automation in agriculture; wireless sensor networks; microgrids’ management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Minzan Li

E-Mail Website
Guest Editor
Research Center for Precision Agriculture, China Agricultural University, Beijing 100083, China
Interests: smart sensors for agriculture; soil and spectral sensors; greenhouse and hydroponic smart control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

According to international organizations such as FAO, ensuring food security for the constantly growing global population of over nine billion by 2040, while dealing with the impact of climate change, is exerting serious pressure on farmers to keep up with food demand. This is a major challenge that requires producing more food with less natural resources, that is “more crop per drop”. In this context, sustainable agriculture is becoming increasingly important as a field of study focused on producing food and other agricultural products while minimizing negative impacts on the environment, promoting social and economic well-being, and ensuring the long-term viability of agricultural production systems. With automated agricultural process optimisation, and the recent innovation within the landscape of robotics and autonomous machines, all empowered by smart sensing, provides an immense promise to make sustainability possible. It is possible if sensors understand “speaking plants and animals” and implies using practices that conserve natural resources and biodiversity, support local communities, and preventively protect the health of plants and welfare of animals.

Smart Sensor technologies are seen as a promising solution for supporting and promoting sustainable agriculture, by decreasing the ecological consequences of farming, boosting profitability for farmers, and raising consumer approval of agricultural technologies and quality of goods. Advanced smart sensors along with methodological and technical solutions enable the gathering of measurements, storage and integration of data, and extraction of value-added insights, that are utilized by control and decision support systems to automate and optimize agricultural processes. Metaverse is one of the key enablers for the future society, which is envisioned to bring a new revolution to the digital world of Agricultural IOT (AIOT). For Metaverse, the physical world sensing will be integrated with the cyber world through digitizing physical objects to create smart sensing digital twins. Hyperautomation is the number one strategic technology trend, and twins are in the centre. AI can help developing twins and sensor fusion so that Model Predictive Control can vastly improve the sustainability but of course we must start from a good physical model. The new direction to unite technology into one end-to-end automation platform, requires very good information for all system variables and that is enhanced by the present expansion of smart sensing. This technological revolution poses significant challenges resulting in substantial changes in agricultural practices and offering valuable opportunities.

The objective of this Special Issue is to showcase cutting-edge papers focused on various aspects of smart sensors’ technologies in sustainable agriculture. These papers may include reviews, research papers, communications, technical papers, research concepts, and perspectives that highlight the applications and advantages of smart sensors for sustainable agriculture. Given the inherently dynamic and unstructured environment and the unforgiving conditions of agriculture with the aggravating force of climate change, we must apply knowledge-based sensor historical data analytics for ensuring data integrity and multivariable information enabling sustainable production of food. This smart sensing environment is what will empower the FAO mandate of SCPI (Sustainable Crop Production Intensification). The understanding of smart agriculture is important to be identified by the scientific community as a significant issue because it is needed for nations to develop and adopt these emerging technologies for the food chain.

Dr. Nick Sigrimis
Dr. Konstantinos G. Arvanitis
Prof. Dr. Minzan Li
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 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

  • smart sensor: sensing elements
  • sensor signals
  • sensor fusion
  • image sensing
  • hyperspectral sensing
  • multispectral sensing
  • sensor information
  • machine learning
  • metaversing
  • model twins
  • hyperautomation

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Published Papers (3 papers)

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Research

22 pages, 35768 KiB  
Article
Incoherent Region-Aware Occlusion Instance Synthesis for Grape Amodal Detection
by Yihan Wang, Shide Xiao and Xiangyin Meng
Sensors 2025, 25(5), 1546; https://doi.org/10.3390/s25051546 - 2 Mar 2025
Viewed by 679
Abstract
Occlusion presents a significant challenge in grape phenotyping detection, where predicting occluded content (amodal detection) can greatly enhance detection accuracy. Recognizing that amodal detection performance is heavily influenced by the segmentation quality between occluder and occluded grape instances, we propose a grape instance [...] Read more.
Occlusion presents a significant challenge in grape phenotyping detection, where predicting occluded content (amodal detection) can greatly enhance detection accuracy. Recognizing that amodal detection performance is heavily influenced by the segmentation quality between occluder and occluded grape instances, we propose a grape instance segmentation model designed to precisely predict error-prone regions caused by mask size transformations during segmentation, with a particular focus on overlapping regions. To address the limitations of current occlusion synthesis methods in amodal detection, a novel overlapping cover strategy is introduced to replace the existing random cover strategy. This approach ensures that synthetic grape instances better align with real-world occlusion scenarios. Quantitative comparison experiments conducted on the grape amodal detection dataset demonstrate that the proposed grape instance segmentation model achieves superior amodal detection performance, with an IoU score of 0.7931. Additionally, the proposed overlapping cover strategy significantly outperforms the random cover strategy in amodal detection performance. Full article
(This article belongs to the Special Issue Smart Sensors for Sustainable Agriculture)
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30 pages, 14057 KiB  
Article
Precision Soil Moisture Monitoring Through Drone-Based Hyperspectral Imaging and PCA-Driven Machine Learning
by Milad Vahidi, Sanaz Shafian and William Hunter Frame
Sensors 2025, 25(3), 782; https://doi.org/10.3390/s25030782 - 28 Jan 2025
Cited by 2 | Viewed by 1501
Abstract
Accurately estimating soil moisture at multiple depths is essential for sustainable farming practices, as it supports efficient irrigation management, optimizes crop yields, and conserves water resources. This study integrates a drone-mounted hyperspectral sensor with machine learning techniques to enhance soil moisture estimation at [...] Read more.
Accurately estimating soil moisture at multiple depths is essential for sustainable farming practices, as it supports efficient irrigation management, optimizes crop yields, and conserves water resources. This study integrates a drone-mounted hyperspectral sensor with machine learning techniques to enhance soil moisture estimation at 10 cm and 30 cm depths in a cornfield. The primary aim was to understand the relationship between root zone water content and canopy reflectance, pinpoint the depths where this relationship is most significant, identify the most informative wavelengths, and train a machine learning model using those wavelengths to estimate soil moisture. Our results demonstrate that PCA effectively detected critical variables for soil moisture estimation, with the ANN model outperforming other machine learning algorithms, including Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (XGBoost). Model comparisons between irrigated and non-irrigated treatments showed that soil moisture in non-irrigated plots could be estimated with greater accuracy across various dates. This finding indicates that plants experiencing high water stress exhibit more significant spectral variability in their canopy, enhancing the correlation with soil moisture in the root zone. Moreover, over the growing season, when corn exhibits high chlorophyll content and increased resilience to environmental stressors, the correlation between canopy spectrum and root zone soil moisture weakens. Error analysis revealed the lowest relative estimation errors in non-irrigated plots at a 30 cm depth, aligning with periods of elevated water stress at shallower levels, which drove deeper root growth and strengthened the canopy reflectance relationship. This correlation corresponded to lower RMSE values, highlighting improved model accuracy. Full article
(This article belongs to the Special Issue Smart Sensors for Sustainable Agriculture)
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22 pages, 6505 KiB  
Article
Adaptive Step RRT*-Based Method for Path Planning of Tea-Picking Robotic Arm
by Xin Li, Jingwen Yang, Xin Wang, Leiyang Fu and Shaowen Li
Sensors 2024, 24(23), 7759; https://doi.org/10.3390/s24237759 - 4 Dec 2024
Cited by 2 | Viewed by 1059
Abstract
The Adaptive Step RRT* (AS-RRT*) path planning algorithm for tea-picking robotic arms was proposed as a solution to the autonomy, safety, and efficiency problems inherent to tea-picking robots in tea plantations. The algorithm employs an accumulator-based sampling point selection strategy to enhance the [...] Read more.
The Adaptive Step RRT* (AS-RRT*) path planning algorithm for tea-picking robotic arms was proposed as a solution to the autonomy, safety, and efficiency problems inherent to tea-picking robots in tea plantations. The algorithm employs an accumulator-based sampling point selection strategy to enhance the efficiency of path planning and the quality of the resulting path. It combines fast connectivity and pruning optimization methods to identify collision-free paths in a shorter time and to reduce the computational burden. It also incorporates a dynamic step length adjustment mechanism following collision detection, ensuring that the robot arm can avoid obstacles in real time. Furthermore, the generated paths were optimized through the introduction of redundant node removal and curve smoothing techniques. In the robotic arm motion planning experiments, the depth vision sensor was employed to obtain three-dimensional information within the tea plantation as the data source. The experimental results demonstrate that the AS-RRT* algorithm reduces the path length by 14.18% and the path planning time is less than 1 s, indicating that the proposed method enhances the efficiency of path planning and obstacle avoidance performance of the tea-picking robot arm. Full article
(This article belongs to the Special Issue Smart Sensors for Sustainable Agriculture)
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