Abstract
The Kingdom of Saudi Arabia is the world’s largest producer of date fruit. It produces almost 400 date varieties in bulk. During the harvesting season the date grading and sorting pose problems for date growers. Since it is a labor intensive and time consuming process, it delays the post harvesting operations which costs them dearly.
The date grading and sorting is a repetitive process. In practice, it is carried out by humans manually through visual inspection. The manual inspection poses further problems in maintaining consistency in grading and uniformity in sorting. To speed up the process as well as maintain the consistency and uniformity we have designed and implemented a prototypical computer vision based date grading and sorting system. We have defined a set of external quality features. The system uses RGB images of the date fruits. From these images, it automatically extracts the aforementioned external date quality features. Based on the extracted features it classifies dates into three quality categories (grades 1, 2 and 3) defined by experts. We have studied the performance of a back propagation neural network classifier and tested the accuracy of the system on preselected date samples. The test results show that the system can sort 80% dates accurately.