@article{Santoso:340616,
      recid = {340616},
      author = {Santoso, I and Yuanita, EA and Karomah, RS},
      title = {Application of digital image processing method for roasted  coffee bean quality identification: a systematic literature  review},
      journal = {African Journal of Food, Agriculture, Nutrition and  Development},
      address = {2024-01},
      number = {2490-2024-974},
      year = {2024},
      abstract = {In coffee processing, there are several important stages,  one of which is roasting. The roasting process is an  important determinant of coffee quality. Determination of  coffee quality can be done using digital image processing  methods to produce parameters and quality classifications  precisely, make images of better quality so that photos and  moving images can be easily understood. This analysis uses  a Systematic Literature Review (SLR) for the  identification, evaluation, and interpretation of all  available research results on the topics discussed. The  purpose of this study was to identify and analyze the main  quality parameters and the best digital image processing  methods used in classifying the quality of roasted coffee  beans. From the results of the analysis of 31 journals, it  is known that the parameters for evaluating the quality of  roasted coffee are color parameters, texture parameters,  and shape parameters. The color parameters consist of Red  Green Blue (RGB), Grayscale, Hue Saturation Intensity  (HSI), and L*a*b* features. The texture parameters consist  of energy, entropy, homogeneity, and contrast. As for the  feature shape parameters, they are area, circumference,  diameter, and percentage of roundness. Results of the  analysis show that the main parameter that plays an  important role in assessing the quality of roasting coffee  is the color parameter. This can be seen from the function  of the color parameter in quality identification based on  the image of the roasted coffee beans. The quality  parameters used are image capture, image resolution,  training data, testing data, iterations, and accuracy  values. In addition, the resulting image processing methods  used for quality classification include Backpropagation  (BP), Learning Vector Quantization (LVQ), and K-Nearest  Neighbor (KNN). Based on results of the analysis, the best  method for classifying the quality of roasting results is  Backpropagation, and it is known that the accuracy value of  this method has a high range of values.},
      url = {http://ageconsearch.umn.edu/record/340616},
      doi = {https://doi.org/10.22004/ag.econ.340616},
}