Kumar, A.Kashyap, Y.Sharma, A.2026-02-062025Lecture Notes in Electrical Engineering, 2025, Vol.1402 LNEE, , p. 69-7718761100https://doi.org/10.1007/978-981-96-5115-3_6https://idr.nitk.ac.in/handle/123456789/28620The increasing popularity of photovoltaic (PV) setups stems from their capacity to generate clean and cost-effective electricity. However, various factors can either totally or partially disrupt the production of PV panels. To address this challenge, this study proposes a ResNet-50 model with dynamically adjusted hyperparameters to classify real-time captured images of PV panels into efficient and non-efficient categories. The hyperparameter tuning within the ResNet-50 model is conducted across three distinct cases, revealing that the most optimal classification results are achieved with the following settings: 50 epochs, a learning rate of 0.001, and a batch size of 32. The highest weighted average metrics, including accuracy (96%), recall (96%), precision (97%), and F1-score (96%), were obtained under these settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.ClassificationHyperparameterResidual blockResNet-50