Fruit quality is instrumental in agricultural pursuit, and the evaluation of fruit quality and disease detection is a highly challenging task; hence, it creates huge gaps in efficiency and errors. Recently however, the DL, along with other advanced techniques such as the CNN, has shown considerable enhancement of the general methodology in fruit quality assessment. This article presents recent convolution-based fruit quality assessment developments: ripeness detection, disease classification, and grading of other fruits, such as apples, oranges, guava, and tomatoes. The performance of various types of studies showed accuracy in classification ranging from 90 to 99%, demonstrating the power of a CNN. The use of hyperspectral imaging and transfer learning has been noted to enhance the power of detection. However, challenges such as limited datasets, real-time application fields, and model interpretability still exist. This paper discusses future directions for research aimed at enhancing deep learning models, focusing on fruit quality assessment in terms of more accuracy, scalability, and practical application.