This study investigates the implementation of deep learning (DL) approaches to the fertile egg-recognition problem, based on incubator images. In this study, we aimed to classify chicken eggs according to both segmentation and fertility status with a Mask R-CNN-based approach. In this manner, images can be handled by a single DL model to successfully perform detection, classification and segmentation of fertile and infertile eggs. Two different test processes were used in this study. In the first test application, a data set containing five fertile eggs was used. In the second, testing was carried out on the data set containing 18 fertile eggs. For evaluating this study, we used AP, one of the most important metrics for evaluating object detection and segmentation models in computer vision. When the results obtained were examined, the optimum threshold value (IoU) value was determined as 0.7. According to the IoU of 0.7, it was observed that all fertile eggs in the incubator were determined correctly on the third day of both test periods. Considering the methods used and the ease of the designed system, it can be said that a very successful system has been designed according to the studies in the literature. In order to increase the segmentation performance, it is necessary to carry out an experimental study to improve the camera and lighting setup prepared for taking the images.

In this study, calf in Turkey live cattle stock in an important position in terms of the Kars dairy cattle-feeding operation, maintenance practices and differences in the level of knowledge and is intended to determine the economic losses due to calf mortalities. The material of the study was constituted by the data obtained from the interviews conducted with 108 dairy cattle business owners in the central villages of Kars. In the interviews, data about 0-180 days old patients and deceased calves were collected from livestock enterprise owners in 2016-2017. In this study, economic losses due to calf mortality were determined by taking into account the calculation methods in the literatüre. In the study, it was determined that 281 (24.65%) of 1140 calves had various diseases in 2017 and 63 (5.52%) of them died. It was calculated that an average of 156.32 TRY ($43.95) was spent per animal and the economic loss due to calves that died was estimated as 4.597 TRY ($1.293). As a result, it has been shown that training studies aiming to increase producer knowledge levels in minimizing calf diseases and deaths are important.

Deep learning algorithms can now be used to identify, locate, and count items in an image thanks to advancements in image processing technology. The successful application of image processing technology in different fields has attracted much attention in the field of agriculture in recent years. This research was done to ascertain the number of indigestible cereal grains in animal feces using an image processing method. In this study, a regression-based way of object counting was used to predict the number of cereal grains in the feces. For this purpose, we have developed two different neural network architectures based upon Fully Convolutional Regression Networks (FCRN) and U-Net. The images used in the study were obtained from three different dairy cows enterprises operating in Nigde Province. The dataset consists of the 277 distinct dropping images of dairy cows in the farm. According to findings of the study, both models yielded quite acceptable prediction accuracy with U-Net providing slightly better prediction with a MAE value of 16.69 in the best case, compared to 23.65 MAE value of FCRN with the same batch.

Green fodder plants have an important place in animal nutrition in terms of meeting the nutritional needs of animals and increasing appetite. Especially in dairy cattle breeding, green feeds are needed for milk yield and quality. In order to meet the green feed needs of ruminant animals, the scarcity of agricultural areas, water use, environmental and climate factors can cause negative effects. The increase in the prices of green feeds, which cannot be sustained throughout the year, increases the tendency to soilless agriculture. The increase in feed prices due to many reasons in soil-dependent agriculture may cause the breeder to ignore the nutritional needs of the animal and cause the feeding not to be done correctly. For this purpose, it is thought that with hydroponic production, which is one of the soilless farming systems, the negative conditions related to the environment and soil can be eliminated and the feed costs can be reduced by ensuring the continuity of green feed throughout the year. Although there is a disease-free growing environment with hydroponic production, the digestibility of the products to be obtained will increase and feed efficiency will increase. In addition, with hydroponic production, the digestibility, crude protein values, vitamin and mineral contents of feeds with high cellulose content (such as barley, wheat, maize) increase. Meat, milk yield and quality, animal performance and health will also be positively affected by the increase in feed utilization. In addition to all these, it also allows the plants with a long growing period to benefit in a short time. Especially the feeds obtained by hydroponic production are used to obtain green feed of 18-20 cm in 6-7 days, and to feed animals with the obtained feed root.

No abstract