The Use of Image Processing and Sensor in Tomato Sorting Machine by Color, Size, and Weight

Abstract

Tomatoes are a popular vegetable in Indonesia, where production increases every year according to market demand. The large production requires proper post-harvest handling both in quality and time. It has been well-known that sorting and grading are the first and foremost processes in the post-harvest process of tomatoes. Sorting tomatoes can be conducted by color and adjusted to the target market. The automation process in the post-sorting and grading process can save time and resources. This research proposes a sorting system that sorts tomatoes based on color, size, and weight. Tomatoes are sorted by red, yellow, and green colors. The detection of color and size was carried out by image processing with the OpenCV library. The color detection was carried out by HSV's red, yellow, and green values. In comparison, the dimensional measurement was carried out by determining the outermost point of the detected object both vertically and horizontally. At the same time, tomatoes' weight was measured by a weight sensor. This system was implemented into a prototype sorting system with a webcam, Arduino, a conveyor, and motors. The final part was a storage box used to accommodate tomatoes based on grading. The implementation has the maximum results for detecting color with 100% accuracy and measuring weight with 95% accuracy. However, it still needs development for dimensional measurements. In this research, it has only 5% accuracy. This proposed system can be used both in software and hardware design as an inline tomato sorting tool.

Designing, Optimizing, and Validating a Low-Cost, Multi-Purpose, Automatic System-Based RGB Color Sensor for Sorting Fruits

“…The initial phase in the post-harvest procedure is the sorting of the product according to color and quality. This process is crucial to maintaining freshness during the harvesting process and distribution to marketing places [3,4]. Manual sorting of fruits is regarded as being quite easy [5], but it is inconsistent, time-consuming, variable, subjective, onerous, expensive, and easily influenced by the surrounding environment.…”

“…Manual sorting of fruits is regarded as being quite easy [5], but it is inconsistent, time-consuming, variable, subjective, onerous, expensive, and easily influenced by the surrounding environment. Therefore, the automatic systems are expected to reduce the post-harvest processing time to speed up the marketing process and therefore enhance the shelf-life of the produce [1,4,6].…”

“…Risdin et al [25] used CNN to classify fruits based on their variety. Sari et al [4] used a procedure based on both image processing and a sensor in a tomato sorting machine to classify tomatoes by color, size, and weight.…”