Segmentation is the first and most important part in the development of any machine vision system with specific goals. Segmentation is especially important when the machine vision system works under environmental conditions, which means under natural light with natural backgrounds. In this case, segmentation will face many challenges, including the presence of various natural and artificial objects in the background and the lack of uniformity of light intensity in different parts of the camera's field of view. However, today, we must use different machine vision systems for outdoor use. For this reason, in this study, a segmentation algorithm was proposed for use in environmental conditions without the need for light control and the creation of artificial background using video processing with emphasizing the recognition of apple fruits on trees. Therefore, a video with more than 12 minutes duration containing more than 22,000 frames was studied under natural light and background conditions. Generally, in the proposed segmentation algorithm, five segmentation steps were used. These steps include: 1. Using a suitable color model; 2. Using the appropriate texture feature; 3. Using the intensity transformation method; 4. Using morphological operators; and 5. Using different color thresholds. The results showed that the segmentation algorithm had the total correct detection percentage of 99.013%. The highest sensitivity and specificity of segmentation algorithm were 99.224 and 99.458%, respectively. Finally, the results showed that the processor speed was about 0.825 seconds for segmentation of a frame.
Nondestructive estimation of the various physicochemical features of food such as fruits and vegetables will create a dramatic development in the food industry. The reason for this development is that the estimation is non-destructive, online, and most importantly fast. Regarding the advantages, various researchers have focused on how to undertake non-destructive estimation of the physicochemical features of various nutrients. Three main goals were pursued in this article. These are: 1. Nondestructive estimation of the chlorophyll b content of red delicious apple using color features and hybrid artificial neural network-cultural algorithm (ANN-CA), 2. Nondestructive estimation of chlorophyll b content of red delicious apple using spectral data (around a range of 680 nm) and hybrid Artificial Neural Network-biogeography-based algorithm (ANN-BBO), 3. Nondestructive estimation of the chlorophyll b content of red delicious apple using different groups of selective spectra by the hybrid artificial neural network-differential evolution algorithm (ANN-DA). In each of these methods, 1000 replications were performed to evaluate the reliability of various hybrids of the artificial neural network. Finally, the results indicated that the average determination coefficient in 1000 replications for the hybrid artificial neural network, the cultural algorithm, and the hybrid artificial neural network, the biogeography-based optimization algorithm, was 0.882 and 0.932, respectively. Also, the results showed that the highest value of the coefficient of determination among the different groups of effective features is related to the group of features with 10 spectra. The coefficient of determination in this case was 0.93.
Non-destructive estimation of the chemical properties of fruit is an important goal of researchers in the food industry, since online operations, such as fruit packaging based on the amount of different chemical properties and determining different stages of handling, are done based on these estimations. In this study, chlorophyll a content in Red Delicious apple cultivar is predicted as a chemical property that is altered by apple ripening stage, using nondestructive spectral and color methods combined. Two artificial intelligence methods based on hybrid Multilayer Perceptron Neural Network-Artificial Bee Colony Algorithm (ANN-ABC) and Partial least squares regression (PLSR) were used in order to obtain a non-destructive estimation of chlorophyll a content. In application of the PLSR method, various pre-processing algorithms were used. In order to statistically properly validate the hybrid ANN-ABC predictive method, 20 runs were performed. Results showed that the best regression coefficient of the PLSR method in predicting chlorophyll a content using spectral data alone was 0.918. At the same time, the average determination coefficient over 20 repetitions in hybrid ANN-ABC in the estimation of chlorophyll a content, using spectral data and color features were higher than 0.92±0.040 and 0.89±0.045, respectively, which to our knowledge is a remarkable non-intrusive estimation result.
Lycopene and vitamin C are two vital compositions of tomatoes. Analytical quantification of these components using common destructive methods is expensive and time consuming. In this study, the feasibility of using near-infrared (NIR) spectroscopy at the range of 930-1650nm was assessed to measure the lycopene and vitamin C contents of intact cv. Newton tomatoes. To develop prediction models based on spectral data and analytical measurements achieved using common destructive methods, partial least squares (PLS) regression was utilized. Before modeling, some pre-processing techniques were also used to reduce the irrelevant data from the spectra. The prediction accuracy of the models was evaluated using root mean square error of cross validation (RMSECV), correlation coefficient (rcv) and residual predictive deviation (RPD). The best prediction models had RMSECV of 2.256 µg g-1 and 1.087 mg 100g-1 , r cv of 0.840 and 0.818, and RPD of 1.835 and 1.701 for lycopene and vitamin C contents, respectively. Results indicated that NIR spectroscopy can predict the lycopene and vitamin C contents of tomato non-destructively with good and fair accuracy, respectively.
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