Quality assessment of coffee beans through computer vision and machine learning algorithms

Abstract

The increasing market interest in coffee beverage, lead coffee growers around the world to adopt more efficient methods to select the best-quality coffee beans. Currently, coffee beans selection is carried out either manually, which is a costly and unreliable process, or using electronic sorting machines, which are often inefficient because some coffee beans defects, such as sour and immature beans, have similar spectral response patterns. In this sense, the present work aimed to analyze the importance of shape and color features for different machine learning techniques, such as Support Vector Machine (SVM), Deep Neural Network (DNN) and Random Forest (RF), to assess coffee beans' defects. For this purpose, an algorithm written in Python language was used to extract shape and color features from coffee beans images. The dataset obtained was then used as input to the machine learning algorithms, developed using Python and R programing languages. The data reported in this study pointed to the importance of color descriptors for classifying coffee beans defects. Among the variables used, the components G mean from RGB (Red, Green and Blue) color space and V mean from HSV (Hue, Saturation and Value) color space were some of the most relevant features for the classification models. The results reported in this study indicate that all the classifier models presented similar performance. In addition, computer vision along with machine learning algorithms can be used to classify coffee beans with a very high accuracy (> 88%).

Productivity and Quality Sugarcane Broth at Different Soil Management

“…Lima et al In layers below 25 cm, there was a reduction in SPR, a fact relating to the effect to implements applied in soil preparation; emphasis on traffic machines did not affect soil physical structure in depth, except in the no-tillage system (NT); this becomes evident when comparing Figures 2 and 3 at a 35 cm depth downwards; in this stratum the values are very similar. Lima et al [38] reported increased SPR with below-layer soil tillage. Otto et al [39] found lower SPR results than those obtained in our study, but this was demonstrated by a severely affected location, with machine traffic in the inter rows of sugarcane.…”

Analisa Fitur Ekstraksi Ciri Dan Warna Dalam Proses Klasifikasi Kematangan Buah Rambutan Berbasis K-Nearest Neighbor

“…Model HSV dimotivasi oleh sistem visual manusia. Dalam model HSV, komponen luminous (kecerahan) dipisahkan dari informasi pembawa warna (hue dan saturation) [11], [20]- [22]. Model warna HSV didefinisikan pada persamaan (11) berikut.…”

Convolutional Neural Network for Ground Coffee Particle Size Classification

“…Pinto et al (2017) used the Convolutional Neural Network (CNN) to classify defects in raw coffee beans, achieving accuracies ranging from 72.4 to 98.7% based on the types of defects. Santos et al (2020) and García et al (2019) discussed quality control of ground coffee beans by detecting defects. Santos et al (2020) used Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN) to classify coffee beans defects according to the shape and color features, achieving high accuracy in their results.…”

“…Santos et al (2020) and García et al (2019) discussed quality control of ground coffee beans by detecting defects. Santos et al (2020) used Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN) to classify coffee beans defects according to the shape and color features, achieving high accuracy in their results.…”