Advances in Proximal and Remote Sensing for Optimizing Agricultural and Horticultural Production and Postharvest Systems

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1058

Special Issue Editors


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Guest Editor
Institute for Viticulture and Oenology, Hungarian University of Agriculture and Life Sciences, Budapest, Hungary
Interests: viticulture; phenotyping; image analysis; RGB-based vegetation indices; morphometry
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Guest Editor
Department of Plant Production and Agricultural Technology, University of Castilla-La Mancha, Albacete Campus, Albacete, Spain
Interests: UAV; crop; precision agriculture; phytosanitary monitoring with remote sensing

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Guest Editor
Department of Food Measurement and Process Control, Hungarian University of Agriculture and Life Sciences, Somloi u. 14-16, 1118 Budapest, Hungary
Interests: digital image processing; computer vision; data analysis; simulation; biophysics; food physics; food science and technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing offers an innovative and efficient alternative to traditional ground-based manual scouting, enabling more precise crop monitoring, disease inspection, insect prediction, weed classification, water management, yield estimations, and land-use analysis. By providing accurate and timely data, remote sensing supports the development and implementation of both short- and long-term strategies for improving agricultural productivity and sustainability. Key agronomic practices, such as monitoring crop growth, assessing soil quality, tracking plant health, and optimizing irrigation systems, greatly benefit from these technologies.

In recent years, the agricultural sector has increasingly adopted advanced tools, including satellites, drones, robots, and handheld proximal sensing devices. While satellite-based remote sensing has been extensively utilized over the past five decades, challenges such as cloud cover and limited spatial resolution have spurred interest in higher-resolution imagery and on-demand data acquisition through Unmanned Aerial Systems (UAS). These flying platforms, along with ground-based robots, provide detailed and frequent insights into crop health and soil conditions, enabling precision interventions in irrigation scheduling, pest and disease control, and nutrient management.

Moreover, advancements in software technologies—leveraging artificial intelligence (AI) and machine learning (ML)—have significantly enhanced data analysis capabilities. These systems enable near real-time decision-making, facilitating efficient farm operations such as field-level variability assessments, varietal performance evaluations in breeding programs, and the timely adjustment of management practices. By integrating hardware and software innovations, precision agriculture offers transformative potential to sustainably manage resources, increase yields, and maintain soil and plant health for long-term productivity.

Dr. Bodor-Pesti Péter
Dr. Rocío Arias-Calderón
Prof. Dr. László Baranyai
Guest Editors

Manuscript Submission Information

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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

  • crop monitoring
  • data-driven farming
  • irrigation management
  • plant health monitoring
  • precision agriculture
  • proximal sensing
  • remote sensing
  • sustainable farming
  • soil quality assessments

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Published Papers (1 paper)

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Research

19 pages, 5366 KiB  
Article
Integration of Color Analysis, Firmness Testing, and visNIR Spectroscopy for Comprehensive Tomato Quality Assessment and Shelf-Life Prediction
by Sotirios Tasioulas, Jessie Watson, Dimitrios S. Kasampalis and Pavlos Tsouvaltzis
Agronomy 2025, 15(2), 478; https://doi.org/10.3390/agronomy15020478 - 16 Feb 2025
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Abstract
This study evaluates the potential of integrating visible and near-infrared (visNIR) spectroscopy, color analysis, and firmness testing for non-destructive tomato quality assessment and shelf-life prediction. Tomato fruit (cv. HM1823) harvested at four ripening stages were monitored over 12 days at 22 °C to [...] Read more.
This study evaluates the potential of integrating visible and near-infrared (visNIR) spectroscopy, color analysis, and firmness testing for non-destructive tomato quality assessment and shelf-life prediction. Tomato fruit (cv. HM1823) harvested at four ripening stages were monitored over 12 days at 22 °C to investigate ripening stage-specific variations in key quality parameters, including color (hue angle), firmness (compression), and nutritional composition (pH, soluble solids content, and titratable acidity ratio). Significant changes in these parameters during storage highlighted the need for advanced tools to monitor and predict quality attributes. Spectral data (340–2500 nm) captured using advanced and cost-effective portable spectroradiometers, coupled with chemometric models such as partial least squares regression (PLSR), demonstrated reliable predictions of shelf-life and nutritional quality. The near-infrared spectrum (900–1700 nm) was particularly effective, with variable selection methods such as genetic algorithm (GA) and variable importance in projection (VIP) scores enhancing model accuracy. This study highlights the promising role of visNIR spectroscopy as a rapid, non-destructive tool for optimizing postharvest management in tomato. By enabling real-time quality assessments, these technologies support sustainable agricultural practices through improved decision-making, reduced postharvest losses, and enhanced consumer satisfaction. The findings also validate the utility of affordable spectroradiometers, offering practical solutions for stakeholders aiming to balance cost efficiency and reliability in postharvest quality monitoring. Full article
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