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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 8179

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Dr. Xanthoula Eirini Pantazi

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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
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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 (4 papers)

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Research

33 pages, 2275 KB

Open AccessArticle

SymbioMamba: An Efficient Dual-Stream State-Space Framework for Real-Time Maize Disease and Yield Analysis on UAV Platforms

by Zihuan Wang, Yuru Wang, Bocheng Zhou, Xu Yan, Peijiang Guo, Hanyu Yang and Yihong Song

Agriculture 2026, 16(7), 801; https://doi.org/10.3390/agriculture16070801 - 3 Apr 2026

Viewed by 435

Abstract

In UAV (unmanned aerial vehicle)-enabled precision agriculture, achieving high-accuracy disease diagnosis and yield estimation simultaneously on resource-constrained edge devices remains a significant challenge. Existing solutions are commonly hindered by conflicts in visual feature scales, the absence of explicit agronomic causal logic, and the trade-off between lightweight design and global modeling capability. To address these challenges, a heterogeneous dual-stream state-space framework termed SymbioMamba is proposed. The proposed framework incorporates three key innovations: first, a heterogeneous dual-stream encoder is constructed, in which a micro-texture stream captures high-frequency disease details while a macro-context-scan stream models field-scale biomass continuity; second, a pathology–biomass collaborative interaction (PBCI) module is designed to explicitly inject the biological prior—disease stress leading to yield reduction—into the feature space. Third, a topology-aligning cross-architecture distillation (TACAD) paradigm is introduced to transfer global knowledge from a heavyweight teacher to a lightweight student. Experimental results from a maize UAV dataset comprising 12,074 annotated image patches demonstrate that SymbioMamba achieves 89.4% mAP@0.5 and an

R^{2}

of 0.915. Compared to the industry-standard YOLOv11, the framework improves mAP@0.5:0.95 by 2.4% while reducing the parameter count to 6.2 M—a 50% decrease relative to monolithic state-space baselines. Furthermore, yield prediction error is significantly reduced to an RMSE of 485.6 kg/ha. With a compact model size of 6.2 M parameters and 2.4 G FLOPs, SymbioMamba attains an inference speed of 38.2 FPS on the NVIDIA Jetson AGX Orin platform, providing a high-performance, real-time solution for intelligent agricultural phenotypic analysis. Full article

(This article belongs to the Special Issue Smart Sensor-Based Systems for Crop Monitoring)

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22 pages, 2873 KB

Open AccessArticle

Resource-Constrained Edge AI Solution for Real-Time Pest and Disease Detection in Chili Pepper Fields

by Hoyoung Chung, Jin-Hwi Kim, Junseong Ahn, Yoona Chung, Eunchan Kim and Wookjae Heo

Agriculture 2026, 16(2), 223; https://doi.org/10.3390/agriculture16020223 - 15 Jan 2026

Viewed by 2103

Abstract

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops. Full article

(This article belongs to the Special Issue Smart Sensor-Based Systems for Crop Monitoring)

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25 pages, 9499 KB

Open AccessArticle

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

Cited by 2 | Viewed by 1655

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 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

Open AccessArticle

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

Cited by 3 | Viewed by 3160

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 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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