In recent years, with the rise of environmental awareness worldwide, the number of solar power plants has significantly increased. However, the maintenance of solar power plants is not an easy job, especially the detection of malfunctioning photovoltaic (PV) cells in large-scale or remote power plants. Therefore, finding these cells and replacing them in time before severe events occur is increasingly important. In this paper, we propose a hybrid scheme with three embedded learning methods to enhance the detection of malfunctioning PV modules with validated efficiencies. For the first method, we combine the improved gamma correction function (preprocess) with a convolutional neural network (CNN). Infrared (IR) thermographic images of solar modules are used to train the abovementioned improved algorithm. For the second method, we train a CNN model using the IR temperatures of PV modules with the preprocessing of a threshold function. A compression procedure is then designed to cut the time-consuming preprocesses. The third method is to replace the CNN with the eXtreme Gradient Boosting (XGBoost) algorithm and the selected temperature statistics. The experimental results show that all three methods can be implemented with high detection accuracy and low time consumption, and furthermore, the hybrid scheme provides an even better accuracy.