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In most pig barns, intensive breeding is being carried out, and social problems such as manure disposal and large-scale infectious diseases have emerged due to this livestock farming method. In particular, organic matter from the manure is decomposed and odorous gases such as ammonia and hydrogen sulfide are emitted. The emitted harmful gases cause problems such as causing stress in pig barns, reducing immunity, and causing various diseases. To solve these problems, cleaning robots for handling manure in pig barns have been developed, and algorithms for planning optimal routes for autonomous driving or methods for cost reduction due to efficient cleaning are being studied. Most cleaning robots currently in use, employ a method of treating accumulated manure by spraying high-pressure water from a stored water tank. If water can be sprayed only on areas that need cleaning, water consumption can be reduced, so we propose a technology that can determine this through a camera. If it is possible to determine whether cleaning is necessary by segmenting only manure from the image into objects, it is expected to significantly reduce unnecessary water consumption. The purpose of this study is to determine whether cleaning is necessary by dividing excrement into objects from images taken during autonomous driving with a camera mounted on the front of a cleaning robot. The final goal is to obtain a segmented image capable of recognizing only the manure part requiring cleaning from the captured image. The first step acquires image information using an RGB camera. At the beginning of the study, when the number of images is insufficient, the segmentation performance is checked through semantic segmentation using the difference between manure and other objects or backgrounds using image information. Depending on the segmentation performance and the number of acquired images, we plan to use a deep learning-based algorithm in the future. The ultimate goal of this study is to develop a technology for obtaining segmented images capable of recognizing excreta by utilizing image information from captured images. In addition, if only the manure part can be accurately recognized through this study, we plan to apply the technology of spraying water only to the necessary part by using it as information to determine whether cleaning is necessary from the segmented image. Through this, the company plans to apply the technology to self-driving cleaning robots to develop high-efficiency intelligent cleaning robots capable of efficiently handling manure. This achievement was made by the support of the Ministry of Agriculture, Food and Rural Affairs, R&D project(421044043HD030). [ABSTRACT FROM AUTHOR]

Livestock farming generates a significant amount of manure, which must be effectively managed to prevent environmental pollution and ensure the health and well-being of animals. The cleaning machine is a commonly used tool for removing manure from pig barns: however, it often leaves behind residual waste that is difficult to remove. To address this issue, a new equipment design that combines a high-pressure jet and a translational scraper has been proposed. This design aims to improve the efficiency of manure treatment by weakening the adhesion and removing stubborn waste particles, making it easier for the cleaning machine to collect the manure. The highpressure jet provides a powerful stream of water that can dislodge even the most stubborn waste particles. For high-pressure washing, a DC 10V/10A standard water pump and a 70L water tank are included. The translational scraper, on the other hand, can move left and right across the surface of the slatted floor, effectively scraping away any remaining waste. For this work, an actuator was installed to move the blade part and the hook type work part. These two components work together to improve the efficiency of manure treatment and reduce the amount of waste left behind. By improving the efficiency of manure treatment, this new equipment design can help to reduce environmental pollution and improve the overall health and well-being of pigs. In addition, it can also reduce labor costs and improve the profitability of pig farming operations. In conclusion, the design of equipment that combines a high-pressure jet and a translational scraper has the potential to significantly improve the manure treatment efficiency of a pig barn cleaning machine. This new design can help to address the challenges associated with manure management in pig farming and promote a more sustainable and efficient approach to livestock farming. This achievement was made by the support of the Ministry of Agriculture, Food and Rural Affairs, R&D project(421044043HD030). [ABSTRACT FROM AUTHOR]

Members of the public are always somewhat aware of foodborne and other zoonotic pathogens; however, recent illnesses traced to produce and the emergence of pandemic H1N1 influenza virus have increased the scrutiny on all areas of food production. The Council for Agricultural Science and Technology has recently published a comprehensive review of the fate and transport of zoonotic pathogens that can be associated with swine manure. The majority of microbes in swine manure are not zoonotic, but several bacterial, viral, and parasitic pathogens have been detected. Awareness of the potential zoonotic pathogens in swine manure and how treatment, storage, and handling affect their survival and their potential to persist in the environment is critical to ensure that producers and consumers are not at risk. This review discusses the primary zoonotic pathogens associated with swine manure, including bacteria, viruses, and parasites, as well as their fate and transport. Because the ecology of microbes in swine waste is still poorly described, several recommendations for future research are made to better understand and reduce human health risks. These recommendations include examination of environmental and ecological conditions that contribute to off-farm transport and development of quantitative risk assessments. [ABSTRACT FROM AUTHOR]