# Assessing the Homogeneity of Forage Mixtures Using an RGB Camera as Exemplified by Cattle Ratios
## 1 Introduction  
Despite the rapid trend of focusing on products of plant origin in the food industry, meat and dairy livestock and cattle breeding still remains a priority agricultural activity in most countries of the world, thus forming the basis of food security. Modern cattle farms specializing in dairy and beef production are highly automated enterprises using digitalization elements and IoT tools. Some processes, however, cannot be carried out 100% efficiently without human intervention. One of them is feeding, which is formed by a long chain of successive technological operations performed by mechanized tools, robots, and also directly by humans. First, human influence includes quality control of naturally-grown feeds (green mass of freshly cut grass, haylage, corn silage, etc.). Today, conventional \"wet chemistry\" methods are reduced to the preliminary selection and preparation of samples (crushing, drying, etc.) and laboratory analysis to determine protein, fat, carbohydrate, dry matter content, KDK and NDK values. They are accompanied by methods based on scanning the spectral characteristics of prepared feedstuff. In the past three years, the portable nutrition analyzers Aurora NIR or Dinamica Generale have become the most widespread commercial solutions, enabling measuring various raw forages in the field thanks to the autonomous accumulator power system. The instrumentation based on near-infrared reflectance spectrometry is quite functional. Similar methods are relatively common in other industries, where it is necessary to monitor the efficiency of conveyor production, control the flow of powdered mixes in pharmaceutical production, etc. [1; 2; 3; 4]. The disadvantages of using NIR analyzers include their limited use in specially prepared rooms. Additionally, farmers have to make decisions and conduct nutritional analyses of various crops directly in the field when the intensity of natural light, especially insummer, can hinder spectral analysis. Therefore, to make a reliable analysis, it is necessary to provide complete shielding from external light. This condition requires a specially prepared room. Several studies have suggested alternative solutions using RGB cameras with software capable of detecting some essential features [4,5]. Regarding feeding efficiency in livestock farms, it is worth noting that nutritional analysis of ration components is only a preliminary stage to make total mixed rations (TMR). It is still much more important to know what kind of feed an animal gets directly from the feed table. It is vital to ensure the adequate mixing of multi-component rations intended for livestock feeding. The diet of cattle, goats, sheep, and other animals consists of naturally grown components (green mass of freshly cut grass, haylage, corn silage, etc.) and concentrated feed (including grain milling, sunflower, and soybean meal, and other components), obtained in processing plants. The problem is the lack of automated systems to monitor the efficiency of real-time TMR preparation and give recommendations on the mixing mode, thereby ensuring the energy efficiency of feed mixers, including battery-operated robotic ones [6]. The relevance of the proposed research implies that feeding is a key factor influencing animal productivity and health by 70%. The remaining share belongs mainly to the quality of the premises and sanitary standards, so the result depends on the farmer's ability to mix the feed raw material correctly [7,8]. A key indicator of the nutritional value of the ration is the dry matter/moisture content. The optimum level of dry matter content in TMR is estimated to be around 50%. In naturally-grown feeds, the moisture content is higher and amounts to 70%. For balancing the dry matter content and increasing the energy value of TMR, the green feed is enriched with various compound feeds with a high dry matter content and moisture levels up to 14%. Thus, when mixed, the total dry matter content of the feed is about 50%.