Quality control of fruits is important for agriculture to ensure product quality and meet customer needs. Traditional quality reviews often rely on time-consuming and content-intensive book reviews. To overcome these limitations, this article presents a hybrid model that combines EfficientNet architecture with a support vector machine (SVM) to accurately and efficiently determine the quality of fruit by just visual inspection. This work also discusses issues and considerations associated with the use of hybrid models for fruit quality research, including data collection, model training, and combination. After extracting fruit image features using a convolutional neural network, intermediate feature maps are passed through several convolution-average-pool layers in InceptionV3 architecture. Finally, SVM layer applies better discrimination to classify images. Simulation results have shown that the proposed hybrid CNN SVM model can distinguish between rotten and fresh fruits with more accuracy.