Enterprise AI Analysis
Smart Greenhouses in the Era of IoT and AI
A Comprehensive Review for Strategic Leaders in Agriculture 4.0
Authors: Wiam El Ouaham, Mohamed Sadik, Abdelhadi Ennajih, Youssef Mouzouna, Houda Orchi and Samir Elouaham
Abstract: Smart greenhouses (SGHs) are controlled-environment agricultural systems that leverage digital technologies to optimize crop production and resource management. In particular, recent advances in artificial intelligence (AI) and the Internet of Things (IoT) have enabled the development of intelligent monitoring, predictive modeling, and automated decision-support systems within these environments. Against this backdrop, this comprehensive review synthesizes over 130 studies published between 2020 and 2025, with a focus on AI-driven monitoring, predictive modeling, and decision-support frameworks in SGH environments. More specifically, key application domains include microclimate regulation, crop growth assessment, disease and pest detection, yield estimation, and robotic harvesting. Moreover, particular attention is given to the interplay between AI methodologies and their data sources, encompassing IoT sensor networks, RGB, multispectral, and hyperspectral imaging, as well as multimodal data-fusion approaches. In addition, publicly available datasets, model architectures, and performance metrics are consolidated to support reproducibility and cross-study comparison. Nevertheless, persistent challenges are critically discussed, including data heterogeneity, limited model generalization across sites, interpretability constraints, and practical barriers to deployment. Finally, emerging research directions are identified, notably multimodal learning, edge-AI integration, standardized benchmarks, and scalable system architectures, with the overarching objective of guiding the development of robust, sustainable, and operationally feasible AI-enabled SGH systems.
Executive Impact: Driving Efficiency & Sustainability
Leveraging AI and IoT in smart greenhouses offers significant opportunities for operational optimization and sustainable growth in the face of global agricultural challenges.
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Studies Analyzed (2020-2025)
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Estimated Annual Publication Growth
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AI Application Domains Covered
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Key Data Modalities Integrated
Strategic Insights for Leaders
✓ AI and IoT drive intelligent monitoring, predictive modeling, and automated decision-support in SGHs.
✓ Key applications include microclimate regulation, crop growth, disease/pest detection, yield estimation, and robotic harvesting.
✓ Multimodal data fusion (IoT, RGB, MSI, HSI) is critical for robust and generalized AI models.
✓ Challenges persist in data heterogeneity, model generalization, interpretability, and practical deployment.
✓ Future directions emphasize multimodal learning, edge-AI, standardized benchmarks, and scalable architectures for sustainable SGHs.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Microclimate prediction in SGHs is primarily a time-series regression problem, forecasting variables like air temperature, relative humidity, and CO2 concentration from historical sensor data. Statistical methods, machine learning (ML), and deep sequence architectures are extensively used. Gated recurrent models (GRU, LSTM) are particularly effective for longer temporal dependencies and non-stationary dynamics, often enhanced with attention mechanisms for multi-step forecasting across horizons up to 48 hours. Ensemble learning and hybrid approaches combining mechanistic models with data-driven corrections further improve accuracy and robustness.
AI-based approaches have become crucial for crop disease detection in SGHs, moving beyond traditional manual or laboratory-based methods. Deep learning, especially transfer learning with RGB-based detection methods (e.g., EfficientNet, YOLO variants), is widely adopted for improved accuracy and precision, particularly with limited labeled data. Lightweight backbones (MobileNetV2) and advanced loss functions (GIoU, CIoU) enhance real-time localization and multi-scale symptom capture. Severity estimation is also increasingly addressed via two-stage designs that segment diseased tissue to compute a diseased-area ratio.
Computer-vision-based pest monitoring in SGHs replaces manual scouting by quantifying pest presence, species, and population density from image data, supporting timely interventions. Detection on sticky traps, plant surfaces, and trap baseplates presents distinct challenges due to small target size, reflections, and complex backgrounds. Deep learning models, particularly those with improved anchor design (TPest-RCNN) and lightweight architectures (SSD-Lite), enhance detection performance. Resolution-preserving inference (YOLOv3 cascade) and data augmentation (copy-paste) address small pest features and crowded scenes.
Plant growth monitoring and yield estimation in SGHs leverage complementary data streams, fusing vision-based phenotyping (fruit presence, maturity, size) with time-series forecasting of historical production records and environmental variables. Lightweight detection models are integrated into IoT pipelines for continuous observation. Dense segmentation is used for organ-level pixel measurements, providing growth curves and stable trend estimation. Deep temporal models, including CNN-RNN predictors, improve short-horizon yield predictions, and multi-step crop growth state is addressed with SVR Seq2Seq formulations.
Robotic harvesting automates labor-intensive tasks like fruit picking and maturity monitoring in SGHs, addressing workforce shortages and improving efficiency. Platforms integrate autonomous navigation, environmental mapping, automatic control, and machine vision. Deep learning-based detectors (YOLOv4, YOLOv5, Mask R-CNN), often enhanced with attention mechanisms and multi-scale feature fusion, are central for perception tasks such as fruit detection, ripeness assessment, key-point localization, and 3D pose estimation. Stereo depth cameras and RGB-D sensors enable real-time 3D fruit localization and successful harvesting across various crop types.
1.42 °C ± 0.12 °C
Above Pre-Industrial Levels (Jan-Aug 2025)
Climate change is driving significant temperature increases, impacting agriculture.
40%
Global Crop Losses Annually
Pests and diseases cause substantial economic damage, estimated at $220 billion USD.
10.3 Billion
Projected Global Population Peak (Mid-2080s)
Increasing global population demands more efficient and sustainable food production.
133
Peer-Reviewed Studies (2020-2025)
This review synthesizes recent advancements in AI-driven Smart Greenhouses.
PRISMA-based Review Methodology
Identification (327 records)
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Duplicates Removed (120)
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Abstract Screening (60 removed)
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Full-text Eligibility (14 removed)
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133 Studies Included
| Category | This Review (2026) | [23] | [18] | [19] | [20] | [22] | [21] | [24] | [25] |
|---|---|---|---|---|---|---|---|---|---|
| Microclimate Modeling (MMO) | ✓ | ✓ | ✓ | ✓ | |||||
| Control Strategies (CTRL) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||
| Time-Series Forecasting (TSF) | ✓ | ✓ | ✓ | ||||||
| Computer Vision (CV) | ✓ | ✓ | |||||||
| Spectral Sensing (SPEC) | ✓ | ✓ | |||||||
| IoT/WSN Integration (IOT) | ✓ | ✓ | ✓ | ||||||
| Multimodal Data Fusion (FUS) | ✓ | ✓ | |||||||
| Edge/Embedded AI (EDGE) | ✓ | ||||||||
| Robotics Harvesting (ROB) | ✓ |
Our comprehensive review addresses the fragmentation of prior studies by unifying sensing, multimodal data fusion, microclimate modeling, control, and robotic harvesting, providing a holistic overview.
Microclimate Prediction: GRU for Temperature Forecasting
Gated Recurrent Units (GRU) demonstrated superior performance over classical machine learning methods (RF, SVM, MLR) in forecasting minimum temperature within greenhouses. This highlights the effectiveness of recurrent architectures in capturing delayed and regime-dependent greenhouse dynamics, crucial for operational scheduling and control. For longer horizons and sequence-to-sequence configurations, attention mechanisms further alleviate information bottlenecks.
- Outperforms RF, SVM, MLR
- Improved accuracy for delayed dynamics
- Supports multi-step forecasting
Disease Detection: YOLO-Dense for Tomato Leaf Diseases
YOLO-Dense achieved a mAP of 96.41% for tomato leaf disease detection. By substituting dense connections for residual blocks, refining anchors with improved K-means clustering, and adopting multi-scale training, it significantly raised mean Average Precision (mAP) while maintaining low latency (20.28 ms). This approach is robust in cluttered greenhouse backgrounds and effective for small, occluded lesions.
- mAP: 96.41%
- Low Latency: 20.28 ms
- Effective for small/occluded lesions
Robotic Harvesting: DHN-YOLO for Strawberries
Strawberry harvesting robots leveraging DHN-YOLO models achieved an mAP50 of 91.8% for fruit detection and processed images at 71.6 FPS. This system incorporates advanced deep learning for precise fruit detection, ripeness assessment, and key-point localization, demonstrating high efficiency and accuracy for automated harvesting operations in SGHs, particularly for complex scenarios like fruit occlusion.
- mAP50: 91.8%
- Processing Speed: 71.6 FPS
- Robust for fruit detection
Multimodal Fusion
Key Enabler for SGH Robustness
Integrating RGB imagery, spectral data, and environmental sensor data enhances model resilience to variability, improves early detection, and supports accurate monitoring.
Advanced ROI Calculator
Our AI solutions optimize resource management (water, energy, labor) and enhance crop yields by providing precise, real-time insights and automation capabilities. This translates into significant operational savings and increased profitability.
Estimated Annual Savings
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Productive Hours Reclaimed Annually
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Implementation Timeline & Roadmap
A phased approach to integrating AI and IoT into your Smart Greenhouse operations, ensuring a smooth transition and measurable results.
Phase 1: Data Infrastructure & Sensor Integration
Establish robust IoT sensor networks for microclimate, soil, and imaging data collection. Implement secure data pipelines for real-time aggregation and cloud storage. Calibrate and validate sensor readings across the greenhouse environment.
Phase 2: AI Model Development & Initial Deployment
Develop and fine-tune AI/ML models for key applications (e.g., disease detection, yield prediction, climate control). Train models using collected multimodal data, leveraging transfer learning where appropriate. Deploy initial models on edge devices for real-time inference and monitoring.
Phase 3: Autonomous Control & Robotic Integration
Implement automated actuation systems based on AI model outputs for climate control (heating, ventilation, irrigation). Integrate robotic platforms for tasks like harvesting, spraying, and phenotyping, ensuring seamless navigation and manipulation within the SGH environment.
Phase 4: Continuous Optimization & Scalable Expansion
Establish feedback loops for continuous model re-training and performance optimization. Implement federated learning across multiple SGHs to improve generalization and adaptability. Develop scalable system architectures to support expansion to larger operations and diverse crop types, ensuring long-term sustainability and operational efficiency.
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