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Advances in Automation and Controls of Agri-Food Systems

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

Deadline for manuscript submissions: 20 August 2025 | Viewed by 1254

Special Issue Editors


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Guest Editor
Department of Agriculture, Food, Natural Resource and Engineering, University of Foggia, 71122 Foggia, Italy
Interests: agri-food engineering; energy savings; mechanical plants; heat transfer; renewable energy
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Agriculture, Food, Natural Resource and Engineering, University of Foggia, 71122 Foggia, Italy
Interests: agri-food engineering; agricultural machinery; image analysis; mechanical plants; food plants
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The agri-food sector is undergoing a huge change in automation and control, increasingly bridging the technological gap compared to other sectors, such as the manufacturing industry, which are typically leading. This is due to the enormous effort that research has and is continuing to make to identify new technologies that can be applied in contexts that often appear to be complex and uncontrolled. The opposite is often true. The environments in which we operate are often unstructured and subject to significant variability both in space and time. Even the food processing industry, which operates in more controlled environments, suffers the difficulty of working with products that are never uniform in shape, color, and composition, unlike mechanical or electronic products. To this end, it is necessary to continue to focus on designing, testing, and validating new devices and models for optimized and sustainable management of the numerous processes involved in the agri-food sector. Some applications involve using technologies for site-specific crop management, machine vision systems for outdoor and indoor robots, and new management strategies for resource use and energy efficiency.

Therefore, this Special Issue welcomes submissions of original research and review studies in the abovementioned research areas.

Dr. Claudio Perone
Prof. Dr. Roberto Romaniello
Guest Editors

Manuscript Submission Information

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Keywords

  • precision agriculture modeling
  • automation
  • wireless sensor network and IoT
  • smart sensors and actuators
  • strategy for resource use efficiency
  • control and energy optimization
  • decision support systems
  • machine vision systems
  • AG-robotics
  • autonomous guidance

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Published Papers (2 papers)

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Research

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19 pages, 4965 KiB  
Article
Development of a Short-Range Multispectral Camera Calibration Method for Geometric Image Correction and Health Assessment of Baby Crops in Greenhouses
by Sabina Laveglia, Giuseppe Altieri, Francesco Genovese, Attilio Matera, Luciano Scarano and Giovanni Carlo Di Renzo
Appl. Sci. 2025, 15(6), 2893; https://doi.org/10.3390/app15062893 - 7 Mar 2025
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Abstract
Multispectral imaging plays a key role in crop monitoring. A major challenge, however, is spectral band misalignment, which can hinder accurate plant health assessment by distorting the calculation of vegetation indices. This study presents a novel approach for short-range calibration of a multispectral [...] Read more.
Multispectral imaging plays a key role in crop monitoring. A major challenge, however, is spectral band misalignment, which can hinder accurate plant health assessment by distorting the calculation of vegetation indices. This study presents a novel approach for short-range calibration of a multispectral camera, utilizing stereo vision for precise geometric correction of acquired images. By using multispectral camera lenses as binocular pairs, the sensor acquisition distance was estimated, and an alignment model was developed for distances ranging from 500 mm to 1500 mm. The approach relied on selecting the red band image as a reference, while the remaining bands were treated as moving images. The stereo camera calibration algorithm estimated the target distance, enabling the correction of band misalignment through previously developed models. The alignment models were applied to assess the health status of baby leaf crops (Lactuca sativa cv. Maverik) by analyzing spectral indices correlated with chlorophyll content. The results showed that the stereo vision approach used for distance estimation achieved high accuracy, with average reprojection errors of approximately 0.013 pixels (4.485 × 10−5 mm). Additionally, the proposed linear model was able to explain reasonably the effect of distance on alignment offsets. The overall performance of the proposed experimental alignment models was satisfactory, with offset errors on the bands less than 3 pixels. Despite the results being not yet sufficiently robust for a fully predictive model of chlorophyll content in plants, the analysis of vegetation indices demonstrated a clear distinction between healthy and unhealthy plants. Full article
(This article belongs to the Special Issue Advances in Automation and Controls of Agri-Food Systems)
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Review

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13 pages, 783 KiB  
Review
The Combination of Machine Learning Tools with the Rapid Visco Analyser (RVA) to Enhance the Analysis of Starchy Food Ingredients and Products
by Joseph Robert Nastasi, Shanmugam Alagappan and Daniel Cozzolino
Appl. Sci. 2025, 15(6), 3376; https://doi.org/10.3390/app15063376 - 19 Mar 2025
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Abstract
This review discusses how the integration of machine learning (ML) tools enhances the analytical capabilities of the Rapid Visco Analyser (RVA), aiming to provide a deeper understanding of the starch gelatinization in different starchy food ingredients and products. The review also discusses some [...] Read more.
This review discusses how the integration of machine learning (ML) tools enhances the analytical capabilities of the Rapid Visco Analyser (RVA), aiming to provide a deeper understanding of the starch gelatinization in different starchy food ingredients and products. The review also discusses some of the limitations of RVA as a tool for assessing the pasting and viscosity behavior of starch, emphasizing the potential of different ML tools such as principal component analysis (PCA) and partial least squares (PLS) regression to offer a better analytical approach. Examples of the utilization of ML combined with RVA to enhance the analysis of starch and non-starch ingredients are also provided. Furthermore, the importance of preprocessing techniques, such as derivatives, to improve the quality and interpretability of RVA profiles is discussed. The aim of this review is to provide examples of the utilization of RVA combined with ML tools in starchy food ingredients and products. Full article
(This article belongs to the Special Issue Advances in Automation and Controls of Agri-Food Systems)
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