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In this study, the capability of an electronic nose machine to identify chilled and frozen-thawed chicken meat was investigated. According to the importance of fresh chicken meat consumption in people’s daily diet, recognition of fresh chicken meat from frozen-thawed one is an essential issue. In the mentioned system, eight sensors (MOS type) were applied. The outputs of sensors were preprocessed, then features were extracted, and fuzzy K-nearest neighbors (F-KNN) algorithm was applied for classification. Chicken meats were classified in two classes (fresh and frozen-thawed). Also, each group of fresh and frozen-thawed chicken meat was classified in five classes according to their shelf life (elapsed day after slaughtering/thawing). The obtained results showed that the average amount of accuracy for fresh-chilled chicken meat classification and frozen-thawed one were 95.2 and 94.67%, respectively. Finally, the accuracy of fresh chicken meat with 95.83% demonstrated the high accuracy of it compare with frozen-thawed one. According to the results, high performance of F-KNN algorithm demonstrated that e-nose system can be applied as a rapid, accurate, and nondestructive method for online and automated identification of fresh and frozen-thawed chicken meat.

The potential of combining artificial neural networks (ANNs) and image processing for assessing leaf relative water content (RWC) and water content (WC) was addressed. Spathiphyllum wallisii was employed as model species, because it has broad leaves and very responsive stomata. In the course of desiccation, leaves were periodically weighted (to calculate RWC and WC conventionally) and imaged. Image acquisition was performed by a scanner, and was, thus, independent of ambient light environment. Color feature extraction was performed in three color spaces (RGB, HSI, and CIELAB), while six texture statistical features were calculated for each of the (nine) computed color channels. Prior to model development via ANNs, the obtained feature vector underwent feature reduction using principal component analysis. The presented methodology yielded very precise estimations of leaf RWC and WC (correlation coefficient > 0.95). Therefore, the technique under study was proven to be very promising for non-invasive in situ measurements of leaf water status.

Deep Convolutional Neural Network (CNN) with a unique structure for combining the feature extraction and classification stages, has been considered to be a state-of-the-art computer vision technique for classification tasks. This study presents a novel and accurate method for discriminating healthy date fruit (cv. Shahani), from defective ones. Furthermore, owing to the use of deep CNN, this method is able to predict the ripening stage of the healthy dates. The proposed CNN model was constructed from VGG-16 architecture which was followed by max-pooling, dropout, batch normalization, and dense layers. This model was trained and tested on an image dataset containing four classes, namely Khalal, Rutab, Tamar, and defective date. This dataset was collected by a smartphone under uncontrolled conditions with respect to illumination and camera parameters such as focus and camera stabilization. The CNN model was able to achieve an overall classification accuracy of 96.98%. The experimental results on the suggested model demonstrated that the CNN model outperforms the traditional classification methods that rely on feature engineering for discrimination of date fruit images.

In the current research, the potential of a novel method based on the artificial neural network was investigated to diagnose the freshness of common carp (Cyprinus carpio) during ice storage. Fish as an aquaculture product has high nutrients and low-fat content. So, people have consumed it as a safe and high-value foodstuff in their daily diet. Investigation of fish freshness is proposed as a significant issue in the aquaculture industry since fish spoils rapidly. The applied system of this study is comprised of the following steps: First, images of samples were captured and the pre-processing operation was done on the images. Then, particular channels including R, G, B, H, S, I, L*, a*, and b* were computed. Next, feature extraction was performed to obtain 6 types of texture features from each channel. Afterward, the hybrid Artificial Bee Colony-Artificial Neural Network (ABC-ANN) algorithm was applied to select the best features. Finally, the Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) and Artificial Neural Network (ANN) algorithms as the most common methods were used to classify fish images. The best performance of the K-NN classifier was calculated in the k = 8 neighborhood size with the accuracy of 90.48. The best kernel function for the SVM algorithm was polynomial with C, sigma, and accuracy of 1, 2 and 91.52 percent, respectively. In this system, the input layer has consisted of 22 neurons based on the feature selection operation and 4 classes including most fresh, fresh, fairly fresh and spoiled have been used as the number of output layer. At the end, the best results of the MLP networks were achieved by LM learning algorithm and 6 neurons in the hidden layer with the 22–10–4 topology and accuracy of 93.01 percent. The achieved results demonstrate the high performance of the ANN classifier for evaluation of common carp freshness during ice storage as a rapid, accurate, non-destructive, real-time and automated method. It shows the potential of computer vision method in combination with artificial neural networks as an intelligent technique for evaluation of fish freshness.

In this study, the application of a versatile approach for modeling and prediction of the moisture content of dried savory leaves using hybrid artificial neural network-genetic algorithm has been presented. Genetic Algorithm was used in order to find the best Feed Forward Neural Network (FFNN) structure for modeling and estimation of moisture content in the drying process of savory leaves. The experiments were performed at three air temperatures of 40, 60 and 80 °C and at three levels of relative humidity 20%, 30% and 40% and air velocity of 1, 1.5 and 2.0 m/s for drying the savory leaves in the forced conductive dryer. Optimized neural network by GA had two hidden layers with 9 and 17 neurons in first and second hidden layers, respectively. Mean Square Error (MSE) value (0.000094606) and correlation coefficient (0.9992) of FFNN-GA experiments showed that moisture content can be accurately predicted from the input variables: air temperature, airflow velocity, relative humidity and drying time. Moreover, results showed that the optimized neural network topology could denote the superior ability of this intelligent model for on-line prediction of the moisture content of Savory leaves in different drying conditions.

This study focused on developing a one-dimensional convolutional neural network (CNN) model for sorting pistachio nuts using acoustic emissions signals. The mentioned system detects open and closed-shell pistachio nuts by dropping them onto a steel plate and analyzing the acoustic signals. A total of 1600 pistachio nuts from two pistachio nut varieties were used. Proposed CNN-based model learned features directly from raw time-domain data. In addition, the performance of feature learning from frequency spectrum and combined time-frequency data also were tested. Moreover, the proposed CNN model results were compared with random forest (RF) and multilayer perceptron (MLP) neural networks with manual features extracting from the time-domain, frequency domain, and wavelet domain data. The overall accuracy (OAC) and mean squared error (MSE) of the CNN classifier with raw time data were 98.75 % and 0.01, respectively. The corresponding performance of RF with manual time domain features was OAC = 50 % and MSE = 1.56, and for MLP was OAC = 31.46 % and MSE = 2.57, respectively. The results demonstrate that the proposed deep learning method outperforms the other comparative methods. The proposed protocol consists of a smart, non-invasive, and reliable technique for the online pistachio nuts sorting systems.

The potential of artificial neural networks (ANNs) for assessing key herb quality aspects (color features, moisture ratio, essential oil content and major constituents) by considering two drying processing factors (temperature and time) was evaluated. Water mint was employed as model species owing to global popularity and economic importance. Variation in herb quality was induced by employing different periods (0–450 min) and temperatures (50–70 °C) of drying processing. Samples were occasionally imaged (400–700 nm), and then quality features were invasively determined. Green color attenuation was triggered by drying processing duration and temperature. Essential oil content peaked at a shorter time period and at a lower value as drying processing temperature increased. In all drying processing temperature levels, three main components (linalool, linalyl acetate, 1,8-cineole) accounted for more than 77 % of the total essential oil, while five components (α-terpineol, geraniol acetate, hedycaryol, myrcene, neryl acetate) for approximately 15 %. These eight constituents were thus considered in the model. An ANN model was obtained, including an input layer (drying processing temperature and time), 10 hidden layers, and an output layer (12 quality features). This structure corresponds to a 2−10-12 topology. The presented methodology yielded precise estimations of the quality features under study (correlation coefficients in the range of 0.79 and 0.99). Therefore, the technique under study was proven to be very promising for non-invasive in situ estimations of several critical herb quality features.

On-time seed variety recognition is critical to limit qualitative and quantitative yield loss and asynchronous crop production. The conventional method is a subjective and error-prone process, since it relies on human experts and usually requires accredited seed material. This paper presents a convolutional neural network (CNN) framework for automatic identification of chickpea varieties by using seed images in the visible spectrum (400–700 nm). Two low-cost devices were employed for image acquisition. Lighting and imaging (background, focus, angle, and camera-to-sample distance) conditions were variable. The VGG16 architecture was modified by a global average pooling layer, dense layers, a batch normalization layer, and a dropout layer. Distinguishing the intricate visual features of the diverse chickpea varieties and recognizing them according to these features was conceivable by the obtained model. A five-fold cross-validation was performed to evaluate the uncertainty and predictive efficiency of the CNN model. The modified deep learning model was able to recognize different chickpea seed varieties with an average classification accuracy of over 94%. In addition, the proposed vision-based model was very robust in seed variety identification, and independent of image acquisition device, light environment, and imaging settings. This opens the avenue for the extension into novel applications using mobile phones to acquire and process information in situ. The proposed procedure derives possibilities for deployment in the seed industry and mobile applications for fast and robust automated seed identification practices.

Nowadays people tend to include more meat in their diet thanks to the improvement in standards of living as well as an increase in awareness of meat nutritive values. To ensure public health, therefore, there is a need for a rise in worldwide meat production and consumption. Further attention is also required as to how the safety and the quality of meat production process should be assessed. Classical methods of meat quality assessment, however, have some disadvantages; expensive and time-consuming. This study intends to introduce an alternative method known as Computer Vision (CV) for the assessment of various quality parameters of muscle foods. CV has several advantages over the traditional methods. It is non-destructive, easy, and quick, hence, more efficient in meat quality assessments. This study aims to investigate different quality characteristics of some muscle foods using CV. It closes with a discussion on the future challenges and expected opportunities of the practical application of CV in the meat industry.

Accurate laboratory measurement of geo-engineering properties of intact rock including uniaxial compressive strength (UCS) and modulus of elasticity (E) involves high costs and a substantial amount of time. For this reason, it is of great necessity to develop some relationships and models for estimating these parameters in rock engineering. The present study was conducted to forecast UCS and E in the sedimentary rocks using artificial neural networks (ANNs) and multivariable regression analysis (MLR). For this purpose, a total of 196 rock samples from four rock types (i.e., sandstone, conglomerate, limestone, and marl) were cored and subjected to comprehensive laboratory tests. To develop the predictive models, physical properties of studied rocks such as P wave velocity (Vp), dry density (γd), porosity, and water absorption (Ab) were considered as model inputs, while UCS and E were the output parameters. We evaluated the performance of MLR and ANN models by calculating correlation coefficient (R), mean absolute error (MAE), and root-mean-square error (RMSE) indices. The comparison of the obtained results revealed that ANN outperforms MLR when predicting the UCS and E.

Unconventional biodiesel characterization techniques using thermophysical and transport properties have been receiving increasing attention due to its advantages over fundamental combustion and simulation of heat transfer in solving heat transfer, chemical, and bioenergy characteristics of biodiesel combustion. In this study, the optimum production yield of Food Industrial Waste Oil Methyl Ester (FIWOME, B100, FIWOB) was modelled using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques. The basic properties of the fuel types were determined using ASTM test methods, while specific heat capacity ( C p ), thermal diffusivity (TD), thermal conductivity (TC) and Prandtl number (Pr) were determined using standard methods. Diesel engine performance indicators such as Engine Torque (ET), Brake Power (BP), Brake Specific Fuel Consumption (BSFC) and Brake Thermal Efficiency (BTE) were determined for different fuel types using a Perkins diesel engine. The estimated Coefficient of Determination (R2) of 0.9820, Root Mean-Square-Error (RMSE) of 1.7403, Standard Error of Prediction (SEP) of 0.0215, Mean Average Error (MAE) of 1.3790, and Average Absolute Deviation (AAD) of 1.6389 for RSM compared to those of R2 (0.9847), RMSE (1.6071), SEP (0.0199), MAE (1.1425), and AAD (1.2583) for ANN exhibited the robustness of the ANN tool over the RSM technique. Optimal biodiesel-- diesel fuel (B0) blend was 22.5% volume ratio called as B22.5. The optimum yield of FIWOME of 92.5% was achieved at the methanol/oil molar ratio of 5.99, KOH of 1.1 wt.%, and a reaction time of 77.6 min. The basic properties of FIWOME determined complied with both ASTM D6751 and EN 14214 specifications. The values of C p and TD increased and decreased respectively with biodiesel percent in fuel in a quadratic manner. The TC and Pr were correlated with the biodiesel fraction through linear regressions. The brake power, brake torque, and brake specific fuel consumption were observed to increase with an increase in biodiesel percentage level. The determined performance characteristics of B22.5, which is above B20 as recommended for powered diesel engine gave a good improvement in terms of BTE and Pr making FIWOME based biodiesel a viable substitute for fossil diesel.