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Agriculture
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Smart Sensor-Based Systems for Crop Monitoring
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Smart Sensor-Based Systems for Crop Monitoring

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2513

Special Issue Editor

Dr. Xanthoula Eirini Pantazi

E-Mail Website
Guest Editor
Faculty of Agriculture, Department of Agricultural Engineering, Aristotle University of Thessaloniki, P.O. Box 275, 15424 Thessaloniki, Greece
Interests: artificial intelligence; biosystems engineering; automation; yield prediction; crop disease detection; weed management; remote sensing; data fusion; machine learning; deep learning; hyperspectral imaging; fluorescence kinetics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern agricultural systems face increasingly complex challenges, including climate variability, population growth, resource depletion, and food security concerns. Precision agriculture systems that employ real-time crop physiology monitoring for the control of crop growth status and health conditions offer transformative potential, integrating direct plant feedback with smart control systems. Such technologies are being integrated into applications that support precision irrigation management, early crop stress detection, automated nutrient delivery, resource optimization, and sustainable crop production, primarily through smart sensor-based systems that can assess crop status.

This Special Issue invites original research articles, reviews, and perspectives that provide valuable insights into the applications of crop physiology monitoring technologies in precision agriculture, focusing on systems that utilize direct plant feedback for crop growth optimization and health condition assessment.

The topics of interest include, but are not limited to, the following:

  • Plant physiology status sensors for real-time crop monitoring and automated control;
  • Sensor-driven irrigation and nutrient management based on plant physiological status;
  • Plant stress detection and early warning systems that use crop physiology monitoring;
  • Microcontroller-based systems for crop condition monitoring;
  • Machine learning algorithms for plant physiological status data interpretation;
  • Decision support systems for comprehensive crop growth status assessment;
  • Edge computing solutions for real-time plant physiology status processing and control.

Dr. Xanthoula Eirini Pantazi
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture 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

  • multisensory data fusion
  • crop monitoring
  • crop stress detection
  • smart sensors
  • microcontrollers
  • machine learning
  • edge computing
  • precision agriculture
  • decision support systems

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Published Papers (2 papers)

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Research

25 pages, 9499 KB  
Article
Integration of Machine Learning and Remote Sensing to Evaluate the Effects of Soil Salinity, Nitrate, and Moisture on Crop Yields and Economic Returns in the Semi-Arid Region of Ethiopia
by Gezimu Gelu Otoro and Katsuaki Komai
Agriculture 2025, 15(22), 2378; https://doi.org/10.3390/agriculture15222378 - 18 Nov 2025
Viewed by 510
Abstract
Soil salinity, soil moisture, and nutrient loss significantly reduce agricultural productivity and economic benefits in the semi-arid regions of Ethiopia. However, knowledge of how to mitigate these risks remains limited. This study examined the combined effects of salinity (EC), soil moisture (Sm), and [...] Read more.
Soil salinity, soil moisture, and nutrient loss significantly reduce agricultural productivity and economic benefits in the semi-arid regions of Ethiopia. However, knowledge of how to mitigate these risks remains limited. This study examined the combined effects of salinity (EC), soil moisture (Sm), and nitrate (N) on the yield and profitability of banana, cotton, and maize using field-based and satellite data with seven machine learning algorithms. Our results showed that a higher EC level reduced crop yields, whereas sufficient Sm and N improved productivity and income. Among the models, Random Forest (RF) performed the best, achieving high accuracy (e.g., R2 = 0.998 for cotton, 0.869 for banana, and 0.793 for maize). SHapley Additive exPlanations (SHAP) analysis further identified EC as the most critical determinant, highlighting the priority of salinity mitigation, alongside water and nutrient management. These findings provide farmers and decision-makers with practical insights into how to sustain crop productivity, improve livelihoods, and strengthen food security in semi-arid regions. Full article
(This article belongs to the Special Issue Smart Sensor-Based Systems for Crop Monitoring)
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31 pages, 2147 KB  
Article
Plant-Driven Precision Irrigation in Aeroponics: Real-Time Turgor Sensing for Sustainable Lettuce Cultivation
by Panagiotis Karnoutsos, Dimitrios Katsantonis, Anna Gkotzamani, Athanasios Koukounaras, Thomas Kotsopoulos, Xanthoula Eirini Pantazi and Vassilios P. Fragos
Agriculture 2025, 15(18), 1948; https://doi.org/10.3390/agriculture15181948 - 14 Sep 2025
Viewed by 1601
Abstract
The narrow margin for irrigation error in aeroponics necessitates advanced control strategies beyond fixed timer-based approaches. This study evaluates a plant-driven irrigation method based on real-time leaf turgor feedback in aeroponic romaine lettuce (Lactuca sativa L. var. longifolia) cultivation. A leaf [...] Read more.
The narrow margin for irrigation error in aeroponics necessitates advanced control strategies beyond fixed timer-based approaches. This study evaluates a plant-driven irrigation method based on real-time leaf turgor feedback in aeroponic romaine lettuce (Lactuca sativa L. var. longifolia) cultivation. A leaf thickness–turgor sensor was interfaced with an Arduino Mega 2560 to activate misting events dynamically. Two identical aeroponic systems were operated in a fully controlled environment: a conventional timer-based control (TC) system applying mist every 10 min and an Arduino-controlled (AC) system triggered by turgor changes. Over two independent 37-day cultivation cycles, the AC strategy reduced total water use by an average of 15.9% and pump activations by 17.2% while improving water use efficiency by 17.8% and nutrient use efficiency for N, P, and K by an average of 17.8%, with no statistically significant differences in shoot biomass, height, or yield. Although root dry weight was significantly higher under TC, the AC treatment led to a 45.0% reduction in leaf nitrate accumulation and non-significant increases in phenolic content. These findings demonstrate the potential of turgor-responsive irrigation for enhancing sustainability, resource use efficiency, and the quality of produce in aeroponic systems, thereby supporting its broader integration into controlled-environment agriculture (CEA). Full article
(This article belongs to the Special Issue Smart Sensor-Based Systems for Crop Monitoring)
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