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Applied Sciences
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Application of AI, Sensors, and IoT in Modern Agriculture
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Application of AI, Sensors, and IoT in Modern Agriculture

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: 30 March 2027 | Viewed by 1325

Editor

Prof. Dr. Antonio López-Quílez

E-Mail Website
Guest Editor
Department of Statistics and Operational Research, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Spain
Interests: spatial statistics; Bayesian statistics; environmental statistics; biostatistics; epidemiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Achieving a proportional increase in agricultural production to feed this growing population is one of humanity’s most pressing challenges, and this goal must be pursued against a backdrop of climate change, resource depletion, and increasingly frequent extreme weather events, all of which threaten the stability of global food systems. To address these challenges, the integration of advanced technologies, such as automation, sensors, yield monitors, the Internet of Things (IoT), drones, and robotics, is essential, and these tools, combined with geographic information systems (GISs), artificial intelligence (AI), highly structured mathematical models, and big data analytics, form the foundation of a global “Digital Twin” for agriculture. Spatial analysis of agricultural data plays a pivotal role in this context, allowing for precise decision-making and resource optimization. By leveraging these technologies, we can develop resilient agricultural systems capable of meeting future demands while minimizing environmental impacts.

This Special Issue, which presents some of the most recent advances and novel approaches in the spatial analysis of agricultural data, is intended for a wide and multidisciplinary audience. Papers published under this Special Issue will cover of the following eight major topics:

  • Precision agriculture;
  • Sensors in modern agriculture;
  • The Internet of Things (IoT) in modern agriculture;
  • Drones in modern agriculture;
  • Robotics in modern agriculture;
  • Artificial intelligence (AI) in modern agriculture;
  • Geographic information systems (GISs) in modern agriculture.

Prof. Dr. Antonio López-Quílez
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. Applied Sciences 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 2400 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

  • spatial analysis
  • big data analytics
  • mathematical modeling
  • precision agriculture
  • resource optimization
  • climate resilience
  • internet of things (IoT)
  • drones
  • robotics
  • sensors
  • GIS
  • artificial intelligence (AI)

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Published Papers (3 papers)

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Research

19 pages, 9185 KB  
Article
Lightweight WSS-YOLO Quince Fruit Detection Algorithm Integrating SimAM
by Xingrui Wu, Jinting Zou and Haiwei Wu
Appl. Sci. 2026, 16(13), 6342; https://doi.org/10.3390/app16136342 - 24 Jun 2026
Viewed by 159
Abstract
Real-time fruit maturity detection in unstructured orchards remains challenging because of variable illumination, fruit occlusion, complex backgrounds, and the limited computing capacity of edge devices. To address these challenges, this study proposes WSS-YOLO, a lightweight detection framework based on YOLOv11n for quince maturity [...] Read more.
Real-time fruit maturity detection in unstructured orchards remains challenging because of variable illumination, fruit occlusion, complex backgrounds, and the limited computing capacity of edge devices. To address these challenges, this study proposes WSS-YOLO, a lightweight detection framework based on YOLOv11n for quince maturity detection. The model introduces WaveletPool to reduce texture loss during downsampling, adopts a GSConv-based Slim-neck to improve feature fusion with lower computational cost, and integrates SimAM to enhance discriminative fruit-region responses without adding trainable parameters. Experiments on a multi-scenario quince maturity dataset show that WSS-YOLO achieves 86.4% precision, 87.5% recall, and 93.4% mAP@0.5, improving the YOLOv11n baseline by 2.3, 1.7, and 2.5 percentage points, respectively. The model contains only 2.23 M parameters and requires 4.1 G FLOPs. Deployment on the NVIDIA Jetson Orin Nano achieved a real-time speed of 23.0 FPS, suggesting a favorable trade-off between detection accuracy and computational efficiency under the tested conditions. Full article
(This article belongs to the Special Issue Application of AI, Sensors, and IoT in Modern Agriculture)
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37 pages, 3939 KB  
Article
Reasoning-Centric Framework for Open-Set Wild Plant Recognition
by Dongkai Qi, Chia Sien Lim and Sivakumar Vengusamy
Appl. Sci. 2026, 16(11), 5292; https://doi.org/10.3390/app16115292 - 25 May 2026
Viewed by 277
Abstract
Open-set recognition of wild plants in natural complex scenes is an important task for plant conservation, ecological monitoring, and precision agriculture. Traditional closed-set learning methods struggle to handle unseen species not covered by the training set and complex environmental interferences, while existing open-vocabulary [...] Read more.
Open-set recognition of wild plants in natural complex scenes is an important task for plant conservation, ecological monitoring, and precision agriculture. Traditional closed-set learning methods struggle to handle unseen species not covered by the training set and complex environmental interferences, while existing open-vocabulary methods lack knowledge-driven reasoning capabilities and cannot provide interpretable recognition for unknown categories. This research proposes the Reasoning-Aware Perceptual Framework that integrates open-vocabulary vision-language models, foundation mask-generation tools, and domain knowledge reasoning to achieve known/unknown category recognition, online perception, and interpretable reasoning of unknown wild plant species. Centered on a five-stage closed loop of Perception-Retrieval-Reasoning-Decision-Iteration, the framework captures open concepts through vision-language feature alignment, completes evidence-based reasoning and confidence evaluation in combination with a botanical domain knowledge base, and finally outputs species classification decisions, interpretable reasoning reports with family/genus-level taxonomic affinity, and uncertainty-calibrated confidence scores. The unknown category estimation with family/genus-level taxonomic affinity in this framework refers to a general unknown label combined with taxonomic affinity at the family/genus level, which can clearly reflect the evolutionary relationship between unknown species and known species. Experiments on the self-constructed WildPlantOpenSet-10K dataset and public benchmark datasets report an F1-score of 84.7% for unknown species recognition, AUROC of 0.93 for known/unknown discriminability, and mean F1 of 87.0% across all categories. This framework focuses on open-set wild plant recognition and interpretable reasoning, using off-the-shelf instance extraction to acquire visual features for downstream reasoning. It maintains stable robustness in complex scenarios such as occlusion, strong light, and multi-species coexistence, and can adapt to the open-world environment without relying on large-scale pixel annotations, providing a research prototype for interpretable open-set recognition in complex natural environments. Full article
(This article belongs to the Special Issue Application of AI, Sensors, and IoT in Modern Agriculture)
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27 pages, 7249 KB  
Article
Agroclimatic Forecasting Under Degraded Sensor Data: A Robustness Benchmark of Machine-Learning Models
by Oleksandr Zhabko, Ivan Laktionov, Grygorii Diachenko, Oleksandr Vinyukov and Dmytro Moroz
Appl. Sci. 2026, 16(10), 5075; https://doi.org/10.3390/app16105075 - 19 May 2026
Cited by 1 | Viewed by 351
Abstract
Reliable short-term agroclimatic forecasting is essential for precision agriculture, irrigation planning, disease-risk assessment, and microclimatic decision support. However, field-deployed sensor systems often operate under degraded data conditions, including missing measurements, noise, temporal interruptions, and limited local computational resources. These constraints make it necessary [...] Read more.
Reliable short-term agroclimatic forecasting is essential for precision agriculture, irrigation planning, disease-risk assessment, and microclimatic decision support. However, field-deployed sensor systems often operate under degraded data conditions, including missing measurements, noise, temporal interruptions, and limited local computational resources. These constraints make it necessary to evaluate not only forecasting accuracy under clean data, but also model robustness under realistic sensor-data degradation. The objective of this study is to compare machine-learning models for one-step-ahead agroclimatic time-series forecasting under degraded sensor-data conditions. Using a real meteorological dataset collected by a field weather station in the Dnipro region of Ukraine, twelve regression models were evaluated: Ridge Regression, Random Forest, Extra Trees, Gradient Boosting, HistGradientBoosting, Support Vector Regression, Linear SVR, KNN, PLSRegression, ElasticNet, Lasso, and MultiTaskElasticNet. The models were tested under five controlled scenarios: baseline data, missing values, additive noise, reduced training history, and combined noise–missingness degradation. Quantitatively, Ridge Regression achieved the strongest baseline temperature-forecasting performance, with MAE = 0.318 and R2 ≈ 0.98 under clean data. It also maintained R2 > 0.90 when trained on only 50% of the available history. Under Gaussian noise with σ = 0.05–0.10, Ridge Regression and HistGradientBoosting maintained R2 values in the range of 0.95–0.97, whereas under combined degradation with σ = 0.10 and 20% missing data, HistGradientBoosting retained R2 > 0.85. These findings indicate that machine-learning models differ substantially in their sensitivity to sensor-data degradation and that robustness-oriented benchmarking is necessary before selecting models for agroclimatic forecasting systems. Full article
(This article belongs to the Special Issue Application of AI, Sensors, and IoT in Modern Agriculture)
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