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Advances in Sensor Technologies and Measurement Techniques for Smart Agri-Food
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Advances in Sensor Technologies and Measurement Techniques for Smart Agri-Food

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

Deadline for manuscript submissions: 1 April 2026 | Viewed by 759

Special Issue Editors

Dr. Cristina Nuzzi

E-Mail Website
Guest Editor
Department of Mechanical and Industrial Engineering (DIMI), University of Brescia, 25123 Brescia, Italy
Interests: hand gesture recognition; computer vision; robotics; wearable sensors; deep learning; metrology
Special Issues, Collections and Topics in MDPI journals
Dr. Valentina Bello

E-Mail Website
Guest Editor
Laboratory of ElectroOptics, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, Italy
Interests: optical (bio)sensors; optofluidic; opto-electronics; optical instrumentation and measurements; artificial intelligence for optical sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Ilaria Negri

E-Mail Website
Guest Editor
Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Milan, Italy
Interests: insect vectors of plant pathogens and microbial manipulators of insect reproduction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaoshuan Zhang

E-Mail Website
Guest Editor
Beijing Laboratory of Food Quality and Safety, Department of Mechatronics at the College of Engineering, China Agricultural University (East Campus), Beijing 100083, China
Interests: sensors (IoT, flexible sensors) and data processing in food supply chain/industrial engineering; live animal management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue welcomes the contribution of studies focusing on innovative sensors, measurement solutions, and data analysis methods for application in the precision agriculture and food quality assessment research fields. Examples include (but are not limited to):

  • hybrid measurements in crop fields or forestry regions to analyze the status of plants (health, growth, presence of pests or diseases)
  • remote sensing technologies (e.g., drones) for large-scale crop health monitoring and yield prediction
  • measurements on food either in the production line or afterwards to assess their quality before consumption or mixing with other ingredients to obtain processed food
  • optical, chemical, electronic sensors for quality controls food and the identification of food fraud and adulteration
  • food safety sensors for detecting contaminants or pathogens in food products or tracking freshness or spoilage during processing, packaging, and transportation
  • sensor systems for soil and water quality monitoring in agricultural environments, assessing key parameters like moisture levels, pH, and nutrient content
  • IoT-based solutions for real-time tracking of environmental conditions in greenhouses or controlled agricultural systems

Topics of interest include data collection, modeling and analysis, interpretation, and elaboration of results.
Studies that explore and/or develop novel data analysis techniques, including artificial intelligence, contactless sensors, and applications in uncontrolled environments, will be of particular interest.

Dr. Cristina Nuzzi
Dr. Valentina Bello
Dr. Ilaria Negri
Prof. Dr. Xiaoshuan Zhang
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

  • contactless measurements 
  • measurement science
  • artificial intelligence, machine learning, and deep learning
  • optical, chemical, electronic sensing
  • hyperspectral imaging
  • data analysis
  • sensor fusion
  • precision agriculture
  • precision food quality assessment
  • food fraud and adulteration

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

Published Papers (2 papers)

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Research

22 pages, 3025 KiB  
Article
A Novel Hybrid Technique for Detecting and Classifying Hyperspectral Images of Tomato Fungal Diseases Based on Deep Feature Extraction and Manhattan Distance
by Guifu Ma, Seyed Mohamad Javidan, Yiannis Ampatzidis and Zhao Zhang
Sensors 2025, 25(14), 4285; https://doi.org/10.3390/s25144285 - 9 Jul 2025
Viewed by 238
Abstract
Accurate and early detection of plant diseases is essential for effective management and the advancement of sustainable smart agriculture. However, building large annotated datasets for disease classification is often costly and time-consuming, requiring expert input. To address this challenge, this study explores the [...] Read more.
Accurate and early detection of plant diseases is essential for effective management and the advancement of sustainable smart agriculture. However, building large annotated datasets for disease classification is often costly and time-consuming, requiring expert input. To address this challenge, this study explores the integration of few-shot learning with hyperspectral imaging to detect four major fungal diseases in tomato plants: Alternaria alternata, Alternaria solani, Botrytis cinerea, and Fusarium oxysporum. Following inoculation, hyperspectral images were captured every other day from Day 1 to Day 7 post inoculation. The proposed hybrid method includes three main steps: (1) preprocessing of hyperspectral image cubes, (2) deep feature extraction using the EfficientNet model, and (3) classification using Manhattan distance within a few-shot learning framework. This combination leverages the strengths of both spectral imaging and deep learning for robust detection with minimal data. The few-shot learning approach achieved high detection accuracies of 85.73%, 80.05%, 90.33%, and 82.09% for A. alternata, A. solani, B. cinerea, and F. oxysporum, respectively, based on data collected on Day 7 post inoculation using only three training images per class. Accuracy improved over time, reflecting the progressive nature of symptom development and the model’s adaptability with limited data. Notably, A. alternata and B. cinerea were reliably detected by Day 3, while A. solani and F. oxysporum reached dependable detection levels by Day 5. Routine visual assessments showed that A. alternata and B. cinerea developed visible symptoms by Day 5, whereas A. solani and F. oxysporum remained asymptomatic until Day 7. The model’s ability to detect infections up to two days before visual symptoms emerged highlights its value for pre-symptomatic diagnosis. These findings support the use of few-shot learning and hyperspectral imaging for early, accurate disease detection, offering a practical solution for precision agriculture and timely intervention. Full article
(This article belongs to the Special Issue Advances in Sensor Technologies and Measurement Techniques for Smart Agri-Food)
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22 pages, 22557 KiB  
Article
Depth from 2D Images: Development and Metrological Evaluation of System Uncertainty Applied to Agricultural Scenarios
by Bernardo Lanza, Cristina Nuzzi and Simone Pasinetti
Sensors 2025, 25(12), 3790; https://doi.org/10.3390/s25123790 - 17 Jun 2025
Viewed by 322
Abstract
This article describes the development, experimental validation, and uncertainty analysis of a simple-to-use model for monocular depth estimation based on optical flow. The idea is deeply rooted in the agricultural scenario, for which vehicles that move around the field are equipped with low-cost [...] Read more.
This article describes the development, experimental validation, and uncertainty analysis of a simple-to-use model for monocular depth estimation based on optical flow. The idea is deeply rooted in the agricultural scenario, for which vehicles that move around the field are equipped with low-cost cameras. In the experiment, the camera was mounted on a robot moving linearly at five different constant speeds looking at the target measurands (ArUco markers) positioned at different depths. The acquired data was processed and filtered with a moving average window-based filter to reduce noise in the estimated apparent depths of the ArUco markers and in the estimated optical flow image speeds. Two methods are proposed for model validation: a generalized approach and a complete approach that separates the input data according to their image speed to account for the exponential nature of the proposed model. The practical result obtained by the two analyses is that, to reduce the impact of uncertainty on depth estimates, it is best to have image speeds higher than 500–800 px/s. This is obtained by either moving the camera faster or by increasing the camera’s frame rate. The best-case scenario is achieved when the camera moves at 0.50–0.75 m/s and the frame rate is set to 60 fps (effectively reduced to 20 fps after filtering). As a further contribution, two practical examples are provided to offer guidance for untrained personnel in selecting the camera’s speed and camera characteristics. The developed code is made publicly available on GitHub. Full article
(This article belongs to the Special Issue Advances in Sensor Technologies and Measurement Techniques for Smart Agri-Food)
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