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%).
Measures of percent severity of visible symptoms or injuries caused by diseases or insect pests on plant organs are essential in plant health research. Current color thresholding digital imaging-methods are generally more accurate and reliable than visual estimates. However, these methods perform poorly when scene illumination and background are not uniform, conditions that can be overcome by convolutional neural networks (CNN) for semantic segmentation. In this study, we trained five CNN models for pixel level predictions in images of individual leaves exhibiting necrotic lesions and/or yellowing caused by the insect pest coffee leaf miner (CLM), and two fungal diseases: soybean rust (SBR) and wheat tan spot (WTS). Training was performed in 80% of images annotated for three classes: leaf background (B), healthy leaf (H) and injured leaf (I). Precision, recall, and Intersection over Union (IoU) metrics in the test image set were highest for B, followed by H and I classes, irrespective of the model. When the pixel-level predictions were used to estimate percent severity, Feature Pyramid Network (FPN), Unet and DeepLabv3+ (Xception) performed the best: concordance coefficients were greater than 0.95, 0.96 and 0.98 for CLM, SBR and WTS datasets, respectively. The other three models tended to misclassify healthy pixels as injured, leading to overestimation of percent severity. The accuracy of the predictions by CNN models were comparable with those obtained using a standard commercial software which requires manual adjustments that slows the process.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.