A Review of Deep Learning-Based Defect Detection and Panel Localization for Photovoltaic Panel Surveillance System
(1) * Muhammad Irshat Mohamed Ameerdin Mail (Universiti Teknikal Malaysia Melaka, Malaysia)
(2) Muhammad Herman Jamaluddin Mail (Universiti Teknikal Malaysia Melaka, Malaysia)
(3) Ahmad Zaki Shukor Mail (Universiti Teknikal Malaysia Melaka, Malaysia)
(4) Syazwani Mohamad Mail (Politeknik Kota Kinabalu, Malaysia)
*corresponding author

Abstract


As the photovoltaic (PV) systems expands globally, robust defect detection and precise localization technologies becomes crucial to ensure their operational efficiency. This review introduces an integrated deep learning framework that leverages Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and You Only Look Once (YOLO) algorithms to enhance defect detection in solar panels. By integrating these technologies with Global Positioning System (GPS) and Real-Time Kinematic (RTK) GPS, the framework achieves unprecedented accuracy in defect localization, facilitating efficient maintenance and monitoring of expansive solar farms. Specifically, CNNs are employed for their superior feature detection capabilities in identifying defects such as microcracks and delamination. RNNs enhance the framework by analyzing time-series data from panel sensors, predicting potential failure points before they manifest. YOLO algorithms are utilized for their real-time detection capabilities, allowing for immediate identification and categorization of defects during routine inspections. This review's novel contribution lies in its use of an integrated approach that combines these advanced technologies to not only detect but also accurately localize defects, significantly impacting the maintenance strategies for PV systems. The findings demonstrate an improvement in detection speed and localization accuracy, suggesting a promising direction for future research in solar panel diagnostics. The review provided aims to refine surveillance systems and improve the maintenance protocols for photovoltaic installations, ensuring longevity, durability and efficiency in energy production.

Keywords


Solar Defects; Deep Learning; Photovoltaic; Localization; RTK GPS

   

DOI

https://doi.org/10.31763/ijrcs.v4i4.1579 		      

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References


[1] S. K. Ramu, G. C. R. Irudayaraj, and R. Elango, “An IoT‐Based Smart Monitoring Scheme for Solar PV Applications,” Electrical and Electronic Devices, Circuits, and Materials, pp. 211–233, 2021, https://doi.org/10.1002/9781119755104.ch12.

[2] T. Berghout, M. Benbouzid, T. Bentrcia, X. Ma, S. Djurović, and L.-H. Mouss, “Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects,” Energies, vol. 14, no. 19, p. 6316, 2021, https://doi.org/10.3390/en14196316.

[3] S. Ansari, A. Ayob, M. S. H. Lipu, M. H. M. Saad, and A. Hussain, “A Review of Monitoring Technologies for Solar PV Systems Using Data Processing Modules and Transmission Protocols: Progress, Challenges and Prospects,” Sustainability, vol. 13, no. 15, p. 8120, 2021, https://doi.org/10.3390/su13158120.

[4] S. Naser Kabour, R. Adnan Almalki, L. A. Alghamdi, W. Alharthi, and N. B. Alshagi, “Fault classification and detection for photovoltaic plants using machine learning algorithms,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 32, no. 1, pp. 353-362, 2023, https://doi.org/10.11591/ijeecs.v32.i1.pp353-362.

[5] W. Yin, S. Lingxin, L. Maohuan, S. Qianlai, and L. Xiaosong, “PV-YOLO: Lightweight YOLO for Photovoltaic Panel Fault Detection,” IEEE Access, vol. 11, pp. 10966-10976, 2023, https://doi.org/10.1109/ACCESS.2023.3240894.

[6] A. P. Singh and I. Yadav, “A Review on The Axis Tracking Used for Solar PV Application,” IOP Conference Series: Materials Science and Engineering, vol. 1116, no. 1, p. 012063, 2021, https://doi.org/10.1088/1757-899X/1116/1/012063.

[7] M. A. Saldamli, Y. A. Kaplan, and G. G. Ünaldi, “Comparison of the Performance of Solar Panels Designed in Three Different Ways under the Same Conditions,” Gazi University Journal of Science, vol. 36, no. 3, pp. 1231-1245, 2023, https://doi.org/10.35378/gujs.1037777.

[8] Q. Fang, C. Ibarra-Castanedo, I. Garrido, Y. Duan, and X. Maldague, “Automatic Detection and Identification of Defects by Deep Learning Algorithms from Pulsed Thermography Data,” Sensors, vol. 23, no. 9, p. 4444, 2023, https://doi.org/10.3390/s23094444.

[9] X. Lu and P. Li, “Research on gearbox temperature field image fault diagnosis method based on transfer learning and deep belief network,” Scientific Reports, vol. 13, 2023, https://doi.org/10.1038/s41598-023-33858-w.

[10] A. Binomairah, A. Abdullah, B. Khoo, Z. Mahdavipour, T. Teo, N. M. Noor, and M. Abdullah, “Detection of microcracks and dark spots in monocrystalline PERC cells using photoluminescene imaging and YOLO-based CNN with spatial pyramid pooling,” EPJ Photovoltaics, vol. 13, no. 27, 2022, https://doi.org/10.1051/epjpv/2022025.

[11] J. Gu and S. Unjoh, “Image processing methodology for detecting delaminations using infrared thermography in CFRP-jacketed concrete members by infrared thermography,” Composite Structure, vol. 270, p. 114040, 2021, https://doi.org/10.1016/j.compstruct.2021.114040.

[12] S. N. Venkatesh and V. Sugumaran, “A combined approach of convolutional neural networks and machine learning for visual fault classification in photovoltaic modules,” Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability, vol. 236, no. 1, pp. 148-159, 2022, https://doi.org/10.1177/1748006X211020305.

[13] K. Liao and J. Lu, “Using UAV to Detect Solar Module Fault Conditions of a Solar Power Farm with IR and Visual Image Analysis,” Applied Sciences, vol. 11, no. 4, p. 1835, 2021, https://doi.org/10.3390/app11041835.

[14] M. K. Hasan, T. S. T. Tanaka, M. R. Ali, C. K. Saha, and M. M. Alam, “Harvester Evaluation Using Real-Time Kinematic GNSS and Hiring Service Model,” AgriEngineering, vol. 3, no. 2, pp. 363–382, 2021, https://doi.org/10.3390/agriengineering3020024.

[15] T. Kanwal, S. Rehman, T. Ali, K. Mahmood, S. Villar, L. Lopez, I. Ashraf, “An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field,” Agriculture, vol. 13, no. 8, p. 1600, 2023, https://doi.org/10.3390/agriculture13081600.

[16] P. B. Quater, F. Grimaccia, S. Leva, M. Mussetta, and M. Aghaei, “Light Unmanned Aerial Vehicles (UAVs) for Cooperative Inspection of PV Plants,” IEEE Journal of Photovoltaics, vol. 4, no. 4, pp. 1107–1113, 2014, https://doi.org/10.1109/JPHOTOV.2014.2323714.

[17] M. Aghaei, A. Gandelli, F. Grimaccia, S. Leva and R. E. Zich, "IR real-time analyses for PV system monitoring by digital image processing techniques," 2015 International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP), pp. 1-6, 2015, https://doi.org/10.1109/EBCCSP.2015.7300708.

[18] M. Aghaei, F. Grimaccia, C. A. Gonano and S. Leva, "Innovative Automated Control System for PV Fields Inspection and Remote Control," IEEE Transactions on Industrial Electronics, vol. 62, no. 11, pp. 7287-7296, 2015, https://doi.org/10.1109/TIE.2015.2475235.

[19] M. Aghaei, A. Dolara, S. Leva and F. Grimaccia, "Image resolution and defects detection in PV inspection by unmanned technologies," 2016 IEEE Power and Energy Society General Meeting (PESGM), pp. 1-5, 2016, https://doi.org/10.1109/PESGM.2016.7741605.

[20] S. Lee, K. E. An, B. D. Jeon, K. Y. Cho, S. J. Lee and D. Seo, "Detecting faulty solar panels based on thermal image processing," 2018 IEEE International Conference on Consumer Electronics (ICCE), pp. 1-2, 2018, https://doi.org/10.1109/ICCE.2018.8326228.

[21] V. V. C, M. R. S, P. S. V., and A. R, “Deep Learning Approach for Crack Detection in Solar Panels using Convolutional Neural Networks,” International Research Journal on Advanced Science Hub, vol. 5, no. 5S, pp. 321-328, 2023, https://doi.org/10.47392/irjash.2023.S043.

[22] S. Hassan and M. Dhimish, “Dual spin max pooling convolutional neural network for solar cell crack detection,” Scientific Reports, vol. 13, 2023, https://doi.org/10.1038/s41598-023-38177-8.

[23] C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electronic Markets, vol. 31, pp. 685-695, 2021, https://doi.org/10.1007/s12525-021-00475-2.

[24] S. C. Mesbahi, M. A. Mahraz, J. Riffi, and H. Tairi, “Hand Gesture Recognition Based on Various Deep Learning YOLO Models,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 4, 2023, https://doi.org/10.14569/ijacsa.2023.0140435.

[25] Y. Cao, D. Pang, Y. Yan, Y. Jiang, and C. Tian, “A photovoltaic surface defect detection method for building based on deep learning,” Journal of Building Engineering, vol. 70, p. 106375, 2023, https://doi.org/10.1016/j.jobe.2023.106375.

[26] L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” Journal of Big Data, vol. 8, 2021, https://doi.org/10.1186/s40537-021-00444-8.

[27] A. Adadi and M. Berrada, "Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)," IEEE Access, vol. 6, pp. 52138-52160, 2018, https://doi.org/10.1109/ACCESS.2018.2870052.

[28] S. Albawi, T. A. Mohammed and S. Al-Zawi, "Understanding of a convolutional neural network," 2017 International Conference on Engineering and Technology (ICET), pp. 1-6, 2017, https://doi.org/10.1109/ICEngTechnol.2017.8308186.

[29] P. Purwono, A. Ma’arif, W. Rahmaniar, H. I. K. Fathurrahman, A. Z. K. Frisky, and Q. M. U. Haq, “Understanding of Convolutional Neural Network (CNN): A Review,” International Journal of Robotics and Control Systems (IJRCS), vol. 2, no. 4, pp. 739-748, 2023, https://doi.org/10.31763/ijrcs.v2i4.888.

[30] F. Yang, J. Liu, R. Zhang, and Y. Yao, “Diffusion characteristics classification framework for identification of diffusion source in complex networks,” PLoS One, vol. 18, no. 5, p. e0285563, 2023, https://doi.org/10.1371/journal.pone.0285563.

[31] Safwan Mahmood Al-Selwi, Mohd Fadzil Hassan, Said Jadid Abdulkadir, and Amgad Muneer, “LSTM Inefficiency in Long-Term Dependencies Regression Problems,” Journal of Advanced Research in Applied Sciences and Engineering Technology, vol. 30, no. 3, pp. 16-31, 2023, https://doi.org/10.37934/araset.30.3.1631.

[32] M. Bansal, A. Goyal, and A. Choudhary, “A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning,” Decision Analytics Journal, vol. 3, p. 100071, 2022, https://doi.org/10.1016/j.dajour.2022.100071.

[33] X. Cong, S. Li, F. Chen, C. Liu, and Y. Meng, “A Review of YOLO Object Detection Algorithms based on Deep Learning,” Frontiers in Computing and Intelligent Systems, vol. 4, no. 2, pp. 17-20, 2023, https://doi.org/10.54097/fcis.v4i2.9730.

[34] H. Chen et al., “MC-YOLOv5: A Multi-Class Small Object Detection Algorithm,” Biomimetics, vol. 8, no. 4, p. 342, 2023, https://doi.org/10.3390/biomimetics8040342.

[35] P. Jiang, D. Ergu, F. Liu, Y. Cai, and B. Ma, “A Review of Yolo Algorithm Developments,” Procedia Computer Science, vol. 199, pp. 1066-1073, 2022, https://doi.org/10.1016/j.procs.2022.01.135.

[36] M. Hussain, “YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection,” Machines, vol. 11, no. 7, p. 677, 2023, https://doi.org/10.3390/machines11070677.

[37] P. Gao, “Development of YOLO-based Model for Fall Detection in IoT Smart Home Applications,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 10, 2023, https://doi.org/10.14569/ijacsa.2023.0141.

[38] H. T. Ngoc, K. H. Nguyen, H. K. Hua, H. V. N. Nguyen, and L. Quach, “Optimizing YOLO Performance for Traffic Light Detection and End-to-End Steering Control for Autonomous Vehicles in Gazebo-ROS2,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 7, 2023, https://doi.org/10.14569/ijacsa.2023.01407.

[39] Z. Mahboob, A. Zeb and U. S. Khan, "YOLO v5, v7 and v8: A Performance Comparison for Tobacco Detection in Field," 2023 3rd International Conference on Digital Futures and Transformative Technologies (ICoDT2), pp. 1-6, 2023, https://doi.org/10.1109/ICoDT259378.2023.10325705.

[40] J. -H. Kim, N. Kim and C. S. Won, "High-Speed Drone Detection Based On Yolo-V8," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-2, 2023, https://doi.org/10.1109/ICASSP49357.2023.10095516.

[41] M. I. Ameerdin, M. H. Jamaluddin, A. Z. Shukor, L. Al Hakim Kamaruzaman and S. Mohamad, "Towards Efficient Solar Panel Inspection: A YOLO-based Method for Hotspot Detection," 2024 IEEE 14th Symposium on Computer Applications & Industrial Electronics (ISCAIE), pp. 367-372, 2024, https://doi.org/10.1109/ISCAIE61308.2024.10576312.

[42] I. Zyout and A. Oatawneh, "Detection of PV Solar Panel Surface Defects using Transfer Learning of the Deep Convolutional Neural Networks," 2020 Advances in Science and Engineering Technology International Conferences (ASET), pp. 1-4, 2020, https://doi.org/10.1109/ASET48392.2020.9118382.

[43] Y. Guo, Z. Pang, J. Du, F. Jiang and Q. Hu, "An Improved AlexNet for Power Edge Transmission Line Anomaly Detection," IEEE Access, vol. 8, pp. 97830-97838, 2020, https://doi.org/10.1109/ACCESS.2020.2995910.

[44] O. Ozturk, B. Hangun and O. Eyecioglu, "Detecting Snow Layer on Solar Panels using Deep Learning," 2021 10th International Conference on Renewable Energy Research and Application (ICRERA), pp. 434-438, 2021, https://doi.org/10.1109/ICRERA52334.2021.9598700.

[45] C. Molfese, D. Scaccabarozzi, B. Saggin, C. Popa and M. G. Naón, "Smart Solar Panels project, the design outline," 2022 IEEE 9th International Workshop on Metrology for AeroSpace (MetroAeroSpace), pp. 23-27, 2022, https://doi.org/10.1109/MetroAeroSpace54187.2022.9856089.

[46] K. Lee, H. Lee, R. Lee, D. Kim, and J. Yoon, “The Impact of Cracks in BIPV Modules on Power Outputs: A Case Study Based on Measured and Simulated Data,” Energies, vol. 14, no. 4, p. 836, 2021, https://doi.org/10.3390/en14040836.

[47] P. A. A. Pramana and R. Dalimi, "Photovoltaic (PV) Thermal Fault Monitoring Using the Catadioptric Device," IEEE Access, vol. 11, pp. 75546-75554, 2023, https://doi.org/10.1109/ACCESS.2022.3203814.

[48] M. J. A. Ahmad Faudzi et al., “Detection of Crack on Asphalt Pavement using Deep Convolutional Neural Network,” Journal of Physics Conference Series, vol. 1755, no. 1, p. 012048, 2021, https://doi.org/10.1088/1742-6596/1755/1/012048.

[49] H. S. Munawar, A. W. A. Hammad, S. T. Waller, and M. R. Islam, “Modern Crack Detection for Bridge Infrastructure Maintenance Using Machine Learning,” Human-Centric Intelligent Systems, vol. 2, pp. 95-112, 2022, https://doi.org/10.1007/s44230-022-00009-9.

[50] S. Nylund and Z. Barbari, “Study of defects in PV modules: UV fluorescence and Thermographic photography for Photovoltaics (PV) Field Application,” Mälardalen University Sweden, 2019, http://www.diva-portal.org/smash/record.jsf?pid=diva2:1325520.

[51] N. Padmavathi and A. Chilambuchelvan, "Fault detection and identification of solar panels using Bluetooth," 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 3420-3426, 2017, https://doi.org/10.1109/ICECDS.2017.8390096.

[52] M. Dhimish and P. I. Lazaridis, “An empirical investigation on the correlation between solar cell cracks and hotspots,” Scientific Reports, vol. 11, no. 1, pp. 1-11, 2021, https://doi.org/10.1038/s41598-021-03498-z.

[53] M. A. Rasyidi, R. Handayani, and F. Aziz, “Identification of batik making method from images using convolutional neural network with limited amount of data,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 3, pp. 1300-1307, 2021, https://doi.org/10.11591/eei.v10i3.3035.

[54] A. Gupta, Yongmei Liu, N. Zamora and T. Paddock, "Thermal imaging for detecting thermal interface issues in assembly and reliability stressing," Thermal and Thermomechanical Proceedings 10th Intersociety Conference on Phenomena in Electronics Systems, 2006. ITHERM 2006., pp. 942-945, 2006, https://doi.org/10.1109/ITHERM.2006.1645447.

[55] J. Ahmad, A. Akula, R. Mulaveesala and H. K. Sardana, "Probability of Detecting the Deep Defects in Steel Sample Using Frequency Modulated Independent Component Thermography," IEEE Sensors Journal, vol. 21, no. 10, pp. 11244-11252, 2021, https://doi.org/10.1109/JSEN.2020.3021118.

[56] M. Li, T. Ma, D. Yan, H. Liu and H. Wang, "Experimental Research on Welding Defect Detection Based on Thermal Imaging," 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 836-840, 2022, https://doi.org/10.1109/IMCEC55388.2022.10019863.

[57] H. Jeong, G. Kwon, and S. Lee, “Deterioration Diagnosis of Solar Module Using Thermal and Visible Image Processing,” Energies, vol. 13, no. 11, p. 2856, 2020, https://doi.org/10.3390/en13112856.

[58] D. Lee and J. Park, “Development of Solar-Panel Monitoring Method Using Unmanned Aerial Vehicle and Thermal Infrared Sensor,” IOP Conference Series: Materials Science and Engineering, vol. 611, no. 1, p. 012085, 2019, https://doi.org/10.1088/1757-899X/611/1/012085.

[59] M. Waqar Akram, G. Li, Y. Jin, and X. Chen, “Failures of Photovoltaic modules and their Detection: A Review,” Applied Energy, vol. 313, pp. 1-45, 2022, https://doi.org/10.1016/j.apenergy.2022.118822.

[60] G. Yao and X. Wu, "Halcon-Based Solar Panel Crack Detection," 2019 2nd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM), pp. 733-736, 2019, https://doi.org/10.1109/WCMEIM48965.2019.00154.

[61] J. Huang, K. Zeng, Z. Zhang, and W. Zhong, “Solar panel defect detection design based on YOLO v5 algorithm,” Heliyon, vol. 9, no. 8, p. e18826, 2023, https://doi.org/10.1016/j.heliyon.2023.e18826.

[62] H. Lu, X. Huang, H. Shi and H. He, "Defect Detection of Photovoltaic Panels Based on Deep Learning," 2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE), pp. 99-103, 2023, https://doi.org/10.1109/NNICE58320.2023.10105789.

[63] G. Terzoglou, M. Loufakis, P. Symeonidis, D. Ioannidis and D. Tzovaras, "Employing deep learning framework for improving solar panel defects using drone imagery," 2023 24th International Conference on Digital Signal Processing (DSP), pp. 1-5, 2023, https://doi.org/10.1109/DSP58604.2023.10167960.

[64] W. Javed, G. Parveen, and S. Bilal, “Algorithmic as well as Space and Time comparison of various Deep Learning Algorithms,” Lahore Garrison University Research Journal of Computer Science and Information Technology, vol. 7, no. 01, pp. 7-13, 2023, https://doi.org/10.54692/lgurjcsit.2023.0701361.

[65] J. Kim, M. Park, N. T. Huynh, C. Shim, and J. Park, “Detection and Length Measurement of Cracks Captured in Low Definitions Using Convolutional Neural Networks,” Sensors, vol. 23, no. 8, p. 3990, 2023, https://doi.org/10.3390/s23083990.

[66] M. Zhang, D. Wang, N. Amaitik and Y. Xu, "A Distributional Perspective on Remaining Useful Life Prediction With Deep Learning and Quantile Regression," IEEE Open Journal of Instrumentation and Measurement, vol. 1, pp. 1-13, 2022, https://doi.org/10.1109/OJIM.2022.3205649.

[67] S. R. Kaminwar, J. Goschenhofer, J. Thomas, I. Thon, and B. Bischl, “Structured Verification of Machine Learning Models in Industrial Settings,” Big Data, vol. 11, no. 3, pp. 181-198, 2023, https://doi.org/10.1089/big.2021.0112.

[68] Y. Du, G. Huang, Q. Zhang, Y. Gao, and Y. Gao, “A New Asynchronous RTK Method to Mitigate Base Station Observation Outages,” Sensors, vol. 19, no. 15, p. 3376, 2019, https://doi.org/10.3390/s19153376.

[69] A. Rankin, A. Holloway, J. Carsten and M. Maimone, "Integration of an Arm Kinematics Hot Patch Onboard the Curiosity Rover," 2021 IEEE Aerospace Conference (50100), pp. 1-8, 2021, https://doi.org/10.1109/AERO50100.2021.9438501.

[70] T. Baybura, İ. Tiryakioğlu, M. A. Uğur, H. İ. Solak, and Ş. Şafak, “Examining the Accuracy of Network RTK and Long Base RTK Methods with Repetitive Measurements,” Journal of Sensors, vol. 2019, no. 1, pp. 1-12, 2019, https://doi.org/10.1155/2019/3572605.

[71] H. Zheng, M. Atia and H. Yanikomeroglu, "High Altitude Platform Station (HAPS)-Aided GNSS for Urban Areas," 2022 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE), pp. 29-34, 2022, https://doi.org/10.1109/WiSEE49342.2022.9926812.

[72] I. Um, S. Park, H. T. Kim and H. Kim, "Configuring RTK-GPS Architecture for System Redundancy in Multi-Drone Operations," IEEE Access, vol. 8, pp. 76228-76242, 2020, https://doi.org/10.1109/ACCESS.2020.2989276.

[73] Y. Kunisada and C. Premachandra, “High Precision Location Estimation in Mountainous Areas Using GPS,” Sensors, vol. 22, no. 3, p. 1149, 2022, https://doi.org/10.3390/s22031149.

[74] F. Radicioni, A. Stoppini, G. Tosi and L. Marconi, "Multi-constellation Network RTK for Automatic Guidance in Precision Agriculture," 2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 260-265, 2022, https://doi.org/10.1109/MetroAgriFor55389.2022.9965046.

[75] A. Marucci, A. Colantoni, I. Zambon, and G. Egidi, “Precision Farming in Hilly Areas: The Use of Network RTK in GNSS Technology,” Agriculture, vol. 7, no. 7, p. 60, 2017, https://doi.org/10.3390/agriculture7070060.

[76] J. Fulton, E. Hawkins, R. Taylor, and A. Franzen, “Yield monitoring and mapping,” ASSA, CSSA and SSSA, pp. 63-77, 2018, https://doi.org/10.2134/precisionagbasics.2016.0089.

[77] A. Anbumozhi and S. A, “Adoption of Novel Technologies to Boost Precision Agriculture (BPA) using Internet of Things (IOT),” ITM Web of Conferences, vol. 56, pp. 1-12, 2023, https://doi.org/10.1051/itmconf/20235605019.

[78] R. K. Jones and S. Kurtz, "Optimizing the Configuration of Photovoltaic Plants to Minimize the Need for Storage," IEEE Journal of Photovoltaics, vol. 12, no. 3, pp. 860-870, 2022, https://doi.org/10.1109/JPHOTOV.2022.3157565.

[79] L. Yang et al., “Generation of a high-precision digital elevationmodel for fields in mountain regions using RTK-GPS,” International Journal of Automation Technology, vol. 13, no. 5, pp. 671-678, 2019, https://doi.org/10.20965/ijat.2019.p0671.

[80] F. Radicioni, A. Stoppini, R. Brigante, A. Brozzi, and G. Tosi, “GNSS Network RTK for Automatic Guidance in Agriculture: Testing and Performance Evaluation,” Computational Science and Its Applications – ICCSA 2020, pp. 19-35, 2020, https://doi.org/10.1007/978-3-030-58814-4_2.

[81] R. M. Alkan, S. Erol, V. İlçi, and İ. M. Ozulu, “Comparative analysis of real-time kinematic and PPP techniques in dynamic environment,” Elsevier, vol. 163, p. 107995, 2020, https://doi.org/10.1016/j.measurement.2020.107995.

[82] M. Nap et al., “Non-Destructive Measurements for 3D Modeling and Monitoring of Large Buildings Using Terrestrial Laser Scanning and Unmanned Aerial Systems,” Sensors, vol. 23, no. 12, p. 5678, 2023, https://doi.org/10.3390/s23125678.

[83] A. Siemuri, K. Selvan, H. Kuusniemi, P. Valisuo and M. S. Elmusrati, "A Systematic Review of Machine Learning Techniques for GNSS Use Cases," IEEE Transactions on Aerospace and Electronic Systems, vol. 58, no. 6, pp. 5043-5077, 2022, https://doi.org/10.1109/TAES.2022.3219366.

[84] D. Langan, R. Vraa and C. Xu, "Machine-Vision Based Location Detection Solutions for Autonomous Horticulture Rover During Early Growth Season," 2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR), pp. 429-434, 2018, https://doi.org/10.1109/IISR.2018.8535837.

[85] S. J. LeVoir, P. A. Farley, T. Sun and C. Xu, "High-Accuracy Adaptive Low-Cost Location Sensing Subsystems for Autonomous Rover in Precision Agriculture," IEEE Open Journal of Industry Applications, vol. 1, pp. 74-94, 2020, https://doi.org/10.1109/OJIA.2020.3015253.

[86] J. Canny, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pp. 679-698, 1986, https://doi.org/10.1109/TPAMI.1986.4767851.

[87] F. Ghallabi, F. Nashashibi, G. El-Haj-Shhade and M. -A. Mittet, "LIDAR-Based Lane Marking Detection For Vehicle Positioning in an HD Map," 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2209-2214, 2018, https://doi.org/10.1109/ITSC.2018.8569951.

[88] B. Wang and Q. Xiang, “Fast Median Filter Image Processing Algorithm and Its FPGA Implementation,” Frontiers in Signal Processing, vol. 4, no. 4, pp. 88-94, 2020, https://doi.org/10.22606/fsp.2020.44002.

[89] S. Li, Y. Deng, Z. Zhu, H. Hua, and Z. Tao, “A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging,” Diagnostics, vol. 11, no. 9, p. 1523, 2021, https://doi.org/10.3390/diagnostics11091523.

[90] H. Ismail, A. Rahmani, N. Aljasmi and J. Quadir, "Stitching Approach for PV Panel Detection," 2020 Advances in Science and Engineering Technology International Conferences (ASET), pp. 1-4, 2020, https://doi.org/10.1109/ASET48392.2020.9118386.

[91] M. Zhang and L. Yin, "Solar Cell Surface Defect Detection Based on Improved YOLO v5," IEEE Access, vol. 10, pp. 80804-80815, 2022, https://doi.org/10.1109/ACCESS.2022.3195901.

[92] H. Wang et al., “Scientific discovery in the age of artificial intelligence,” Nature, vol. 620, no. 7972, pp. 47-60, 2023, https://doi.org/10.1038/s41586-023-06221-2.

[93] A. Shewalkar, D. Nyavanandi, and S. A. Ludwig, “Performance Evaluation of Deep Neural Networks Applied to Speech Recognition: RNN, LSTM and GRU,” Journal of Artificial Intelligence and Soft Computing Research, vol. 9, no. 4, pp. 235-245, 2019, https://doi.org/10.2478/jaiscr-2019-0006.

[94] A. Mehrish, N. Majumder, R. Bharadwaj, R. Mihalcea, and S. Poria, “A review of deep learning techniques for speech processing,” Information Fusion, vol. 99, p. 101869, 2023, https://doi.org/10.1016/j.inffus.2023.101869.

[95] R. F. Pamungkas, I. B. K. Y. Utama, and Y. M. Jang, “A Novel Approach for Efficient Solar Panel Fault Classification Using Coupled UDenseNet,” Sensors, vol. 23, no. 10, p. 4918, 2023, https://doi.org/10.3390/s23104918.


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