Evaluating droplet distribution of spray-nozzles for dust reduction in livestock buildings using machine vision

Previous studies have demonstrated the negative effects of sub-optimal air quality on profitability, production efficiency, environmental sustainability and animal welfare. Experiments were conducted to assess potential environmental improvement techniques such as installing oil-spraying systems in piggery buildings. The developed spray system worked very well and it was easy to assemble and operate. However, before selecting the most suitable spray heads, their capacity to uniformly distribute the oily mixture and the area covered by the spray heads had to be assessed. Machine vision techniques were used to evaluate the ability of different spray heads to evenly distribute the oil/water mixture. The results indicated that the best coverage was achieved by spray head No.4 and spray head No.1 which covered 79% and 67% of the target area, respectively. Spray distribution uniformity (variance) value was the lowest for spray head No.4 (0.015). Spray head No.3 had the highest variance value (0.064). As the lowest variance means higher uniformity, nozzle No.4 was identified as the most suitable spray head for dust reduction in livestock buildings

Young broiler feeding kinematic analysis as a function of the feed type

The present study aims to compare the kinematic feeding variables of 3-4 days old broiler chickens using three different feed types: fine mash (F1), coarse mash (F2), and crumbled (F3); size was 476 mu m, 638 mu m, and 1243 mu m, respectively. The head displacement and the maximum beak gape were automatically calculated by computational image analysis to find the feeding behavior of broilers. The results did not show strong correlations between birds' weight, beak size (length and width), and the kinematic variables. The "catch-and-throw" movements in F1 (the smallest feed particle) generally occurred in the first mandibulation, while in F3 (the largest feed particle) occurred in the latest mandibulation. It can be suggested that the adoption of "catch-and-throw" in the latest mandibulations increases with larger particles. Abstract Past publications describe the various impact of feeding behavior of broilers on productivity and physiology. However, very few publications have considered the impact of biomechanics associated with the feeding process in birds. The present study aims at comparing the kinematic variables of young broiler chicks (3-4 days old; 19 specimens) while feeding them with three different feed types, such as fine mash (F1), coarse mash (F2), and crumbled feed (F3). The feeding behavior of the birds was recorded using a high-speed camera. Frames sequences of each mandibulation were selected manually and classified according to the temporal order that occurred (first, second, third, or fourth, and further). The head displacement and the maximum beak gape were automatically calculated by image analysis. The results did not indicate strong correlations between birds' weight, beak size (length and width), and the kinematic variables of feeding. The differences between the tested feed were found mostly in the first and second mandibulations, probably explained by the higher incidence of "catch-and-throw" movements in F3 (33%) and F1 (26%) than F2 (20%). The "catch-and-throw" movements in F1 (the smallest feed particle) mostly occurred in the first mandibulation, as in F3 (the largest feed particle) also occurred in the latest mandibulations. It might be suggested that the adoption of "catch-and-throw" in the latest mandibulations increases with larger particles. The kinematic variables in the latest mandibulations (from the third one on) seem to be similar for all feed types, which represent the swallowing phase. It might be inferred that the temporal sequence of the mandibulations should be essential to describe the kinematics of a feeding scene of broiler chickens, and the first and second mandibulations are potentially the key factors for the differences accounted by the diverse feed particle sizes912CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQsem informaçã

The effect of dynamic loading on abrasion of mulberry fruit using digital image analysis

The quality of fruits can be reduced due to some damages and impacts which occur during harvesting. One of the most important mechanical damages that can reduce the quality of ripe fruit quality is abrasion damage. This study focused on the effect of dynamic loading based on customary harvesting method on mulberry fruit properties. To this end, different maturity stage and storage regimes were considered. Color quality parameters, firmness, total soluble solid (TSS), total anthocyanins content (TAC) and abrasion area were the measured factors. The results revealed that none of the surveyed factors were stable during the experiment. The lightness (Lâ), redness (aâ), yellowness (bâ), Câ value, firmness, TSS and TAC of immature and mature mulberry decreased during storage. The value of aâ, bâ and Câ increased as dropping height increased. However, Lâ value, firmness, TSS and TAC of mulberry fruit decreased at both maturity stages (immature and mature mulberry). Moreover, abrasion area increased at immature and mature mulberries by increasing the dropping height and storage time. Keywords: Abrasion, Dynamic loading, Postharvest quality, Mulberr

Development of a smart spectral analysis method for the determination of mulberry (Morus alba var. nigra L.) juice quality parameters using FT‐IR spectroscopy

Abstract Recently, the application of Fourier transform infrared (FT‐IR) spectroscopy as a noninvasive technique combined with chemometric methods has been widely noted for quality evaluation of agricultural products. Mulberry (Morus alba var. nigra L.) is a native fruit of Iran and there is limited information about its quality characteristics. The present study aims at assessing a nondestructive optical method for determining the internal quality of mulberry juice. To do so, first, FT‐IR spectra were acquired in the spectral range 1000–8333 nm. Then, the principal component analysis (PCA) was used to extract the principal components (PCs) which were given as inputs to three predictive models (support vector regression (SVR), partial least square (PLS), and artificial neural network (ANN)) to predict the internal parameters of the mulberry juice. The performance of predictive models showed that SVR got better results for the prediction of ascorbic acid (R2 = .84, RMSE = 0.29), acidity (R2 = .71, RMSE = 0.0004), phenol (R2 = .35, RMSE = 0.19), total anthocyanin (R2 = .93, RMSE = 5.85), and browning (R2 = .89, RMSE = 0.062) compared to PLS and ANN. However, the ANN predicted the parameters TSS (R2 = .98, RMSE = 0.003) and pH (R2 = .99, RMSE = 0.0009) better than the other two models. The results indicated that a good prediction performance was obtained using the FT‐IR technique along with SVR and this method could be easily adapted to detect the quality parameters of mulberry juice

Prediction and classification of sugar content of sugarcane based on skin scanning using visible and shortwave near infrared

The potential application of a visible and shortwave near infrared (Vis/SWNIR) spectroscopic technique as a low cost alternative to predict sugar content based on skin scanning was evaluated. Two hundred and ninety one internode samples representing three different commercial sugarcane varieties were used. Each sample was scanned at four scanning points to obtain the spectra data which was later correlated with its Brix (soluble solids content) values. Partial least square (PLS) model was developed and applied to both calibration and prediction samples. Using reflectance spectra data, the model had a coefficient of determination (R2) of 0.91 and root means square error of predictions (RMSEP) of 0.721 Brix. The artificial neural network (ANN) was also applied to classify spectra data into five Brix categories. The ANN has yielded good classification performance, ranging from 50 to 100% accuracy with an average accuracy of 83.1%. These results demonstrated that the Vis/SWNIR spectroscopy technique could be applied to predict sugarcane Brix in the field based skin scanning method

An intelligent system for egg quality classification based on visible-infrared transmittance spectroscopy

The potential of the visible infrared (Vis–IR) (400–1100 nm) transmittance method to assess the internal quality (freshness) of intact chicken egg during storage at a temperature of 30 ± 7 °C and 25 ± 4% relative humidity was investigated. Two hundred chicken egg samples were used for measuring freshness and spectra collection during egg storage (up to 25 days). Two correlation models, firstly between Haugh unit (HU) and storage time, and secondly between the yolk coefficient (YC) and storage time, were developed and yielded correlation coefficients (R2) of 0.86 and 0.96, respectively. These models spanned the period for which egg quality decreased dramatically and are statistically significant (P < 0.05). In addition, to reduce the dimensionality of the spectra and extract effective wavelengths, two methods were developed based on principal component analysis (PCA) and a genetic algorithm (GA). The output of PCA and GA were also used comparatively to design an egg quality intelligent system. The result of the analyses indicated that identification ratio of GA with fast Fourier transform (FFT) preprocessing was superior to other methods, and that the quality classification rates of this method for one-day-old eggs are 100%. This study shows that identification of an egg’s freshness using NIR spectroscopy with GA and artificial neural network (ANN) is reliable

Applied machine learning in greenhouse simulation; new application and analysis

Prediction the inside environment variables in greenhouses is very important because they play a vital role in greenhouse cultivation and energy lost especially in cold and hot regions. The greenhouse environment is an uncertain nonlinear system which classical modeling methods have some problems to solve it. So the main goal of this study is to select the best method between Artificial Neural Network (ANN) and Support Vector Machine (SVM) to estimate three different variables include inside air, soil and plant temperatures (Ta, Ts, Tp) and also energy exchange in a polyethylene greenhouse in Shahreza city, Isfahan province, Iran. The environmental factors which influencing all the inside temperatures such as outside air temperature, wind speed and outside solar radiation were collected as data samples. In this research, 13 different training algorithms were used for ANN models (MLP-RBF). Based on K-fold cross validation and Randomized Complete Block (RCB) methodology, the best model was selected. The results showed that the type of training algorithm and kernel function are very important factors in ANN (RBF and MLP) and SVM models performance, respectively. Comparing RBF, MLP and SVM models showed that the performance of RBF to predict Ta, Tp and Ts variables is better according to small values of RMSE and MAPE and large value of R2 indices. The range of RMSE and MAPE factors for RBF model to predict Ta, Tp and Ts were between 0.07 and 0.12 °C and 0.28–0.50%, respectively. Generalizability and stability of the RBF model with 5-fold cross validation analysis showed that this method can use with small size of data groups. The performance of best model (RBF) to estimate the energy lost and exchange in the greenhouse with heat transfer models showed that this method can estimate the real data in greenhouse and then predict the energy lost and exchange with high accuracy. Keywords: Black box method, Energy lost, Environmental situation, Energy exchang