Developing an Automatic Tomato Ripeness Sorting Prototype to Enhance Post Harvest Efficiency

Abstract The tomato sorting process is essential to post-harvest management, which aims to classify tomatoes according to their ripeness level. Currently, the majority of traders in traditional markets sort the tomatoes manually. This manual sorting process has drawbacks, as human judgment can vary and be subjective, leading to errors in sorting. Moreover, the longer duration of the sorting process results in a decline in tomato quality. This research proposes an automatic sorting solution by designing a prototype for sorting tomato ripeness levels based on color. The prototype integrates the technology of a TCS34725 color sensor and a TCRT5000 infrared sensor. The system shows the ripeness levels of the detected tomatoes on the LCD screen. Afterward, it sorts the tomatoes into separate containers based on their ripeness levels: raw, half-ripe, and ripe. This method ensures that tomato ripeness information is displayed and allows for organized sorting to manage post-processing efficiently. The prototype test results show that the average time needed to detect a tomato is 50 milliseconds (ms). Then, the accuracy of the prototype to classify the ripeness level is 98.67%. However, the sorting accuracy depends on factors like the distance between the object and the sensor, as well as the lighting conditions. These variables notably impact sensor readings in the current system, posing a limitation in this research. Future studies should incorporate adaptive algorithms to improve the system’s performance under varying environmental conditions.