Welcome to my website - Masaya Homepage

My research

I am currently in a PhD program and am conducting research in the broad areas of near-infrared light, deep learning, sound, image processing, feed nutrition, and statistics in cattle. On this web site, I try to keep up with my research, conferences, and provide programming code.The following is my research.

Livestock Production and Issues to be addressed

　Livestock have not only provided valuable nutrition to people, but have also improved crop production through their use as a source of power in rice paddies and through the use of manure as compost. Furthermore, as the world population increases, the demand for livestock products from livestock is expected to grow. That demand is projected to increase 35% from 2012 levels by 2030 and 50% by 2050 (FAO, 2018). While it is important to combat climate change through practices in sustainable livestock agrifood systems, goals related to eradicating poverty and achieving zero hunger by 2030, as stated in the Sustainable Development Goals (SDGs), are also important issues The SDGs (Sustainable Development Goals) are also important. Therefore, a balance must be struck between animal-derived foods containing essential nutrients such as meat, milk, eggs, cheese, and yogurt that feed hungry and malnourished people and are produced in a way that minimizes greenhouse gas emissions into the atmosphere. Methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) are the three main greenhouse gases emitted from livestock production systems and should be prioritized for reduction. emissions are urgently needed to prevent global warming. The Food and Agriculture Organization of the United Nations (FAO) estimates that improved livestock management practices alone could reduce net emissions from livestock production systems, particularly CH4, by about 30% (FAO, 2013). To shape sustainable livestock production, it is important to understand livestock production from different perspectives.

Beef cattle production system

　In Japan, there are four main breeds of beef cattle. Of these, the Japanese Black breed accounts for more than 95% of beef cattle in Japan and is characterized by its superiority in terms of fat crossbreeding (Ministry of Agriculture, Forestry and Fisheries, 2021). In general, beef calves are weaned by the age of 5 months (or 150 kg) at the latest, and are then moved to breeding and fattening. The average age, starting weight, finishing age, and finishing weight of Japanese black cattle calves is 9.1 months of age and 285 kg, and they are shipped at 28.7 months of age and 714 kg. The fattening period from the start of fattening to shipment is usually divided into two to three stages: pre- and post-fattening or pre-, mid- and post-fattening.

Current status of starchy feeds

　Starch is a useful resource that can be used as food, feed, and energy. The starch content of grains varies depending on variety and growing conditions, but the highest starch content is found in wheat (77%), corn (72%), and barley (58%) (Huntington, 1997). Among these, corn is the main source of energy for Japanese livestock, accounting for 40-50% of the total amount of corn in formula feed fed to livestock (Ministry of Land, Infrastructure, Transport and Tourism, 2014). However, corn is also not produced domestically (except for green corn) and is almost entirely dependent on imports (Statistics Bureau, Ministry of Internal Affairs and Communications, 2020). Thus, while corn supports livestock production as feed, it is a grain that competes with human food, so efficient use of corn as feed is desirable.

Use of computer vision systems

　In the livestock industry, digital technologies such as wearable sensors (Neethirajan, 2017; Rutten et al., 2013), robotic milking systems (Rodenburg, 2017), and infrared spectrometry (Bresolin & Dórea, 2020) have been developed and utilized in the field. These digital technologies can be used to obtain biometric information on livestock that is difficult to measure due to labor, cost, and animal stress. Among the digital technologies for livestock, computer vision systems using deep learning are beginning to attract attention as an indispensable technology for optimal farm management decision making.