On-line weight estimation of broiler carcass and cuts by a computer vision system.

In a broiler carcass production conveyor system, inspection, monitoring, and grading carcass and cuts based on computer vision techniques are challenging due to cuts segmentation and ambient light conditions issues. This study presents a depth image-based broiler carcass weight prediction system. An Active Shape Model was developed to segment the carcass into 4 cuts (drumsticks, breasts, wings, and head and neck). Five regression models were developed based on the image features for each weight estimation (carcass and its cuts). The Bayesian-ANN model outperformed all other regression models at 0.9981 R2 and 0.9847 R2 in the whole carcass and head and neck weight estimation. The RBF-SVR model surpassed all the other drumstick, breast, and wings weight prediction models at 0.9129 R2, 0.9352 R2, and 0.9896 R2, respectively. This proposed technique can be applied as a nondestructive, nonintrusive, and accurate on-line broiler carcass production system in the automation of chicken carcass and cuts weight estimation.

Prediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin.

Prediction of a precise subsoiling using an analytical model (AM) and Discrete Element Method (DEM) was conducted to explain cutting forces and the soil profile induced changes by a subsoiler. Although sensors, AMs and DEM exist, there are still cases of soil structure deformation during deep tillage. Therefore, this study aimed to provide a clear understanding of the deep tillage using prediction models. Experimental data obtained in the soil bin trolley with force sensors were used for verification of the models. Experiments were designed using Taguchi method. In the AM, the modified-McKyes and Willat and Willis equations were used to determine cutting forces and soil furrow profile respectively. Calculations were done using MATLAB software. The elastoplastic behavior of soil was incorporated into the DEM. The DEM predicted results with the best regression of 0.984 [Formula: see text] at a [Formula: see text] of 1.936 while the AM had the lowest [Formula: see text] of 0.957, at a [Formula: see text] of 6.008. All regression results were obtained at p < 0.05. The ANOVA test showed that the p-values for the horizontal and vertical forces were 0.9396 and 0.9696, respectively. The DEM predicted better than the AM. DEM is easy to use and is effective in developing models for precision subsoiling.

Weight and volume estimation of poultry and products based on computer vision systems: a review.

The appearance, size, and weight of poultry meat and eggs are essential for production economics and vital in the poultry sector. These external characteristics influence their market price and consumers' preference and choice. With technological developments, there is an increase in the application and importance of vision systems in the agricultural sector. Computer vision has become a promising tool in the real-time automation of poultry weighing and processing systems. Owing to its noninvasive and nonintrusive nature and its capacity to present a wide range of information, computer vision systems can be applied in the size, mass, volume determination, and sorting and grading of poultry products. This review article gives a detailed summary of the current advances in measuring poultry products' external characteristics based on computer vision systems. An overview of computer vision systems is discussed and summarized. A comprehensive presentation of the application of computer vision-based systems for assessing poultry meat and eggs was provided, that is, weight and volume estimation, sorting, and classification. Finally, the challenges and potential future trends in size, weight, and volume estimation of poultry products are reported.