The spatial adaptation of farming systems to the heterogeneity of plots

The full exploitation of the resource potential of arable lands was analyzed in the research. The problem of completeness of the use in different farming systems was considered. It was found that at the current stage of development of farming systems, the diversity of soils and lands and, accordingly, their resource potential were most successfully incorporated in adaptive-landscape farming systems and precision farming systems. Undoubtedly, the cost of precision farming systems will decrease in the future due to the cheapening of technical means. But without introducing scientific and methodological justification for accounting of diversity of soils and land plots (as in adaptive-landscape farming systems) and heterogeneity of crops into precision farming systems, increasing the completeness of land resource potential cannot be achieved. Another important direction to improve the full use of the land resource potential is the development of a new scientific direction - Econics, and the development of technologies for leveling the heterogeneity of fields. But these directions are at the very beginning of their development.

Analysis of the Content Values of Sweet Maize (Zea mays L. Convar Saccharata Koern) in Precision Farming

The global precision farming area is constantly increasing, and precision sweet maize production developed the most. Sweet maize yield is above average in precision farming. Additionally, its role in healthy nutrition is becoming increasingly important due to new hybrids with high carotenoid content. Precision farming techniques are needed to produce healthy food. In particular, nutrient supply and irrigation, sowing, crop management and harvesting need to be carried out with precision techniques. These factors are all prerequisites for effective and healthy growing and processing. The aim was to use the yields of the four sweet maize hybrids grown on the largest area to examine their nutritional values and concentrations (mg kg−1 dry matter) and to analyse their yield per hectare. Concentration is important for the consumer because K, P, Mg, Ca, Fe, Zn, and Na play an important role in metabolism, skin protection, and bone and tooth health. The new results obtained show that the amount of lutein and zeaxanthin per hectare is important for the processing industry, especially for use in food supplements. Their anti-inflammatory effects and their role in disease prevention (cardiovascular diseases, Age-Related Macular Degeneration (AMD)) have been demonstrated. Consumers choose sweet maize mainly on the basis of its palatability, which is why the sugar content of the hybrids was also studied. We assumed that the element concentration in the yield of new hybrids with higher yield per hectare does not decrease with increasing yield. The concentrations of zeaxanthin, β-cryptoxanthin and β-carotene appear in one principal component and they are in close positive correlation with each other. The lutein concentration was independent of the former three compounds. The independence of the lutein concentration means that it is not possible to estimate its amount based on the other three components. For yield per unit area, the correlation is one-dimensional. Yield determines the lutein, zeaxanthin, β-cryptoxanthin and β-carotene concentrations per hectare.

A Non-Invasive Millimetre-Wave Radar Sensor for Automated Behavioural Tracking in Precision Farming—Application to Sheep Husbandry

The automated quantification of the behaviour of freely moving animals is increasingly needed in applied ethology. State-of-the-art approaches often require tags to identify animals, high computational power for data collection and processing, and are sensitive to environmental conditions, which limits their large-scale utilization, for instance in genetic selection programs of animal breeding. Here we introduce a new automated tracking system based on millimetre-wave radars for real time robust and high precision monitoring of untagged animals. In contrast to conventional video tracking systems, radar tracking requires low processing power, is independent on light variations and has more accurate estimations of animal positions due to a lower misdetection rate. To validate our approach, we monitored the movements of 58 sheep in a standard indoor behavioural test used for assessing social motivation. We derived new estimators from the radar data that can be used to improve the behavioural phenotyping of the sheep. We then showed how radars can be used for movement tracking at larger spatial scales, in the field, by adjusting operating frequency and radiated electromagnetic power. Millimetre-wave radars thus hold considerable promises precision farming through high-throughput recording of the behaviour of untagged animals in different types of environments.

Augmented Reality in Precision Farming: Concepts and Applications

The amount of arable land is limited, yet the demand for agricultural food products is increasing. This issue has led to the notion of precision farming, where smart city-based technologies (e.g., Internet of Things, digital twins, artificial intelligence) are employed in combination to cater for increased production with fewer resources. Widely used in manufacturing, augmented reality has demonstrated impactful solutions for information communication, remote monitoring and increased interaction. Yet, the technology has only recently begun to find a footing alongside precision farming solutions, despite the many benefits possible to farmers through augmenting the physical world with digital objects. Therefore, this article reflects on literature discussing current applied solutions within agriculture, where augmented realty has demonstrated a significant impact for monitoring and production. The findings discuss that augmented reality must be coupled with other technologies (e.g., simultaneous localization and mapping algorithms, global positioning systems, and sensors), specifically 9 are identified across 2 application domains (livestock and crop farming) to be beneficial. Attention is also provided on how augmented reality should be employed within agriculture, where related-work examples are drawn from in order to discuss suitable hardware approaches and constraints (e.g., mobility).

Application of precision farming technologies for chemical protection of grain crops

The current trend of development of agricultural production brings heightened requirements to the quality and safety of products forcing the producers to change the technologies profoundly with providing balance between gross output of production and its safety, and maintaining the stability of agro-ecosystem. The main directions of implementation of this trend is application of technologies of precision farming which constitutes in pre-dosed and addressed fertilizing and application of chemical materials to protect plants. One of the directions of efficiency improvement in the sphere of plant protection is the application of GPS-navigation which monitors the quantity of applied liquid and moving aggregates used for herbicide application. While carrying out the study of efficiency of application of the parallel driving system with the use of GPS-navigation during the chemical weeding of barley seeds of the sort “Otra” in conditions of the Vologda region it was found out that application of the system of parallel driving allowed reducing the amount of sections with the repeated herbicide treatment. This leads to the lowering of quantity of applied herbicides and positively affects the grain crop yield providing the reduction of costs for dilution to treat more than 1, 2 times.

To the question about remote sensing of the earth for precision farming tasks and assessment of the consequences of techno-environmental events

Some issues of the use of unmanned aircraft and space vehicles in monitoring the consequences of technical and environmental events and precision farming are considered. The proposed technology is aimed at improving the recognition accuracy of infrastructure objects with obtaining the numerical values of their 3D coordinates. The aim of the research is to improve the quality of monitoring using neural network identification and classification of objects in multi-zone satellite images obtained from unmanned aerial vehicles (UAV). Research includes both theoretical research and applied problem solving. The mathematical basis of image processing is the image recognition computer. Practical research is based on experimentation, software implementation, testing of algorithms and technology. An effective method of video surveillance of the territory has been improved. The task of the authors' research is to improve the accuracy of objects recognition on the earth's surface (specific infrastructure objects, the sky, the state of vegetation of agricultural land). The authors have experience in this area. The solution to this problem occurs simultaneously in two directions. The first direction: the technical result is ensured by the fact that the technology offers the use of a UAV equipped with two video cameras. The second direction is the use of scientific idea consisting in the development of a method for joint computer processing of digital and analog images obtained from UAVs, as well as quasi-simultaneous and reusable multi-zone satellite images. A new result of the research is the developed data structure for storing the model of the recognition process, which allows to jointly save dissimilar characteristics and membership functions of different types in the same tables