Climate change-induced environmental stresses and limited agricultural land demanding intensification of sustainable agriculture over degraded land via crop diversification strategies. Dragon fruit is one of the potential options and popularising in resource-poor degraded lands apart from its several nutraceutical advantages. Hence, understanding of facts related to its consumer acceptability and maintaining high quality for marketing and processing is highly essential. Therefore in this study, we have developed grading and sorting techniques for dragon fruit using machine learning algorithms (CNN, ANN, and SVM) based on a thorough review of techniques or algorithms available to detect and classify fruit quality using various features of fruits and vegetables. Working of these algorithms is based on the, shape, size, weight, color, and diseases of dragon fruits. Raspberry functionality counts the total number of fruits that are available in the fruit bucket and these are separated by their maturity level using machine learning algorithms. Display omitted •Depth Camera – With help of camera device we can collect the dragon fruit images which is present in the fruit bucket. Functionality of camera device is interfaced with raspberry pi device.•Machine Learning Algorithms - Working of machine learning algorithms such as Convolutional Neural Network, Support Vector Machine and Artificial Neural Network are based on the, shape, size, weight, color, and diseases of dragon fruits.•Raspberry Pi Device - Raspberry functionality counts the total number of fruits that are available in the fruit bucket and these are separated by their maturity level using machine learning algorithms.•Grading and Sorting of Dragon Fruits – This process works on the data set of dragon fruit images which contains the features of dragon fruits as well as camera captured images of dragon fruit and classified it their maturity levels.