Table 5.

Early and current authors are conducting research on the infrared detection of PV panels.

Authors	Year	Citations	Title	Remarks
Dincer et al. [121]	2014	29	Polarization Angle Independent Perfect Metamaterial Absorbers for Solar Cell Applications in the Microwave, Infrared, and Visible Regime.	The proposed metamaterial-based solar cell demonstrates high absorption in both the infrared and visible spectra, enhancing the potential for more efficient next-gen solar cells.
Chandel et al. [122]	2015	33	Degradation analysis of 28 year field exposed mono-c-Si photovoltaic modules of a direct coupled solar water pumping system in western Himalayan region of India.	Utilizing thermal imaging technology to identify hotspots and quantifying degradation by measuring PV parameters under indoor and outdoor conditions.
Adams et al. [123]	2015	42	Water Ingress in Encapsulated Inverted Organic Solar Cells: Correlating Infrared Imaging and Photovoltaic Performance.	Utilizing infrared imaging for local, in-situ tracking of humidity-induced performance degradation to predict the lifespan of organic solar cells and modules.
Du et al. [124]	2017	38	Nondestructive inspection, testing and evaluation for Si-based, thin film and multi junction solar cells: An overview.	Non-destructive inspection, testing, and assessment of solar cells and modules.
Addabbo et al. [125]	2017	55	A UAV Infrared Measurement Approach for Defect Detection in Photovoltaic Plants.	Drones can swiftly inspect solar farms, employing this positioning technology for detecting, labeling anomalies, and identifying faulty panels.
He et al. [126]	2018	36	Noncontact Electromagnetic Induction Excited Infrared Thermography for Photovoltaic Cells and Modules Inspection.	The active electromagnetic induction infrared thermal imaging defect detection method has enabled the visual detection of defects in PV cells and modules.
Zefri et al. [127]	2018	48	Thermal Infrared and Visual Inspection of Photovoltaic Installations by UAV Photogrammetry-Application Case: Morocco.	Visual defects, such as cracks, contamination, and hotspots, have been identified in both visual RGB and thermographic inspections.
Akram et al. [128]	2020	80	Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning.	CNN are used to train an isolation learning model, achieving an average accuracy of 98.67%. Fine-tuning the pre-trained base model through transfer learning on an infrared image dataset increased accuracy to 99.23%.
Du et al. [99]	2020	43	Intelligent Classification of Silicon Photovoltaic Cell Defects Based on Eddy Current Thermography and Convolution Neural Network.	IRT and CNN demonstrate significant potential for defect detection and automatic recognition in Si-PV cells, providing a reliable approach for the research, testing, manufacturing, servicing, and maintenance of Si-PV cells.
Alves et al. [129]	2021	40	Automatic fault classification in photovoltaic modules using Convolutional Neural Networks.	Using cross-validation methods, CNN achieve an estimated accuracy of 92.5% in detecting anomalies in PV modules.