###### Abstract  
Monitoring food processing is mandatory for controlling and ensuring product quality. Most of the used techniques are destructive, arduous, and time-consuming. Non-destructive analyses are convenient for rapid and conservative food quality assessment. Color images of apple slices during the manufacturing of healthy snacks were used for monitoring the drying processing. The implementation of the image-based analysis was straightforward, feasible, and low-cost. The parameters analyzed during imagen acquisition for normalizing were: contrast enhancement, binarization, and morphologic processing, varying the illumination and reference between the positions of the camera and object under analysis. Several apple features related to color, texture, and shape were extracted with computer vision techniques and also analyzed. During image analysis, the entropy was one of the most relevant computed features according to principal component analysis, and it was also relevant in terms of physical interpretation. The average percentage of entropy increase was 19.81% in the green and blue channels, while it was 16.82% in the red channel. Other relevant visual features were the skewness and kurtosis in the RGB channels; and textural information such as contrast, correlation, and variance. f +  
The use of computer vision during monitoring tasks has been successfully applied in different fields (health care, vigilance, and environment). For instance, in the analysis of sequences of medical images to follow the evolution of lesions or [4], and recognition and tracking of smoke within video signals for detection and monitoring of forest fires for environmental protection [5], and machine vision to address the quality assessment of emulsions in the pharmaceutical industry [6]. In addition, computer vision has been exploited in the food industry to track changes in food properties during processing. For example, computer vision (CV) has been previously used as a non-destructive method for monitoring the drying behavior of organic apple cylinders subjected to different antibrowning treatments [7]. Also, drying processing of turmeric slices has been monitored by imaging features by extracting attributes such as color, morphology, texture, browning, and shrinkage [8]. Computer vision has also been used during the salting process of ham to monitor the salt diffusion within lean and fatty tissue areas [9]. Besides human vision, other sensory properties such as the auditory, tactile, olfactory, and gustatory senses have also been artificially implemented to convert, for instance, deterioration reactions into electrical signals processed by a neural network. The design of electronic tongues and noses combines materials' physical and chemical properties as sensors for monitoring specific characteristic compounds of aroma and taste present in the gaseous, liquid, and solid materials in food processing and control quality. An electronic nose was a successful method to detect and recognize fresh and moldy apples and the different molds used for their inoculation [10]. Quality assessment of delicious royal apple fresh and contaminated using bacterial cultures was determined using an in-line electronic nose system. This study's principal component analysis and [PERSON]'s analyses showed a correlation for differentiation of fresh, half, and total contaminated apples [11]. [PERSON] et al. (2015) have reported the use of electronic nose devices (gas sensors) for the identification of fruit volatile organic compounds for identifying the type of fruit, ripeness, and quality [12]. Applying different data-recognition algorithms to signals detected from metal oxide semiconductor (MOS) sensors was a non-invasive and rapid method to identify apple pesticide residues [13].