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.

This investigation presents a new design and analysis of a fourteen elements massive multiple input multiple output (MIMO) compact low specific absorption rate (SAR) antenna system for 5G and beyond terminals. The MIMO antennas are realized for sub-6 GHz LTE-band 46 (5.1-5.8 GHz) long-term evolution 5G wireless communication services. The proposed antenna system is designed on an FR-4 dielectric constant 4.3 substrate consisting of the main board and four sideboards. The radiating elements are developed on the sideboards consisting of a monopole antenna and two metamaterial unit cells, as well as the feed line is designed on the principal board. The overall volume of the proposed structure is 150×80×7.5 mm. Through the use of the metamaterial unit cells, the proposed antennas have been arranged in close proximity without developing a strong mutual coupling. The results obtained were very important in terms of impedance matching, isolation among the antennas, and good gain. Even, the SAR analysis at the operating frequency band of 5.5 GHz proves that the proposed MIMO antenna system satisfies safety standards set for human exposure at the international level. Therefore, the proposed massive MIMO antenna structure is a potential solution for future communication applications.

The early and accurate detection of plant diseases is essential for crop management and agricultural loss control, especially under resource limitations. We propose an optimized YOLO11n architecture, designated as YOLO11_Opt, targeting real-time inference on low-cost embedded systems. The model is computationally efficient through the selective narrowing of its width and depth, while performing competitively in two-class object recognition tasks. Pepper leaves were chosen as the materials for study. Three methods of quantization (FP32, FP16, and INT8) were investigated. After running the experiments, the results showed that YOLO11_Opt greatly reduces the computational complexity: the complexity decreased from 6.3 GFLOPS and 2.58 million parameters in the typical YOLO11n model to a very small 0.5 GFLOPS and 0.33 million parameters, while maintaining competitive detection capabilities. The improved FP32 model has a mAP (0.5:0.95) of 0.913 and a precision of 0.991, while the old version has 0.961 mAP and 0.996 precision. Lastly, implementations on embedded hardware prove that the method is feasible: the detection accuracy of the system in live classification is around 92% with Raspberry Pi 4 and 94% with NVIDIA Jetson Nano, with inference times of as little as 1.9 ms on NVIDIA Jetson Nano and 8.3 ms on Raspberry Pi 4. Thus, YOLO11_Opt demonstrates significant potential as a reliable, high-performance, low-cost solution to identifying plant diseases on devices in precision agriculture.