The presence of ongoing climate change and their increasing scale have now become apparent. Climate models provide a glimpse of the future. Disappointing forecasts pushing to develop models that would allow rapidly develop measures for adapting to unavoidable environmental conditions and to mitigate impacts, including those related to the deterioration of water quality in natural water bodies and reservoirs. The most simple and effective models for fast and fairly accurate forecasts regarding waters are the zero-dimensional models. Such models widely used in engineering practice owing to their considerable simplicity which is correct for conservative substances. At the same time, additional limits should be taken into account while forecasting water quality involving nonconservative components which could change its chemical characteristics in particular. The period during which the concentration of a substance becomes the same over the entire area of a reservoir could be considered as a time scale in which zero-dimensional equations are applicable, the average concentration of substances for the entire body of water can be considered only as of the average over some time not less than determined by the scope of consideration. The expression which is given in the paper allows identifying limits of applicability of zero-dimensional equations for a non-conservative substance concentration as a function of time scale and coefficient of non-conservativeness.
Turning to the question of those engineering problems, the solution of which can be used the results of this work, let us first of all select from the wide range of design cases related to pressureless channels, the main design case, which we will keep in mind in the future (as, so to speak, «starting»). Concerning the indicated main design case, we agree to consider the non-pressure movement of water in the prismatic channel (operating in summer conditions) along which uniform turbulent movement of water occurs, which is almost pure in the absence of waves and other phenomena that violate the uniform motion regime, assuming that if such phenomena are taking place, then they should be taken into account the introduction into the calculations of the relevant adjustments. The statement of the above problems in most cases boils down to the following, it is necessary to find a water slope such that its cross-sectional shape is stable (indelible) and the living cross-sectional area is smallest. It is known that such a problem, until recently, was solved by using the concept of “maximum permissible speed” V max (related to the uniform movement of water). The magnitude of this speed was assigned (and is currently assigned) based on reference data on the type of soil (and in some cases depending on the depth of the water in the channel). Knowing V max and the flow rate, one can easily find the cross-sectional area, as well as the channel slope (using formulas to determine the Shezi coefficient «C» or hydraulic friction coefficient λ and following the accepted value of the roughness coefficient). In engineering practice, when hydraulic calculations of the channels under consideration, we usually use the Shezy coefficient «C». Meanwhile, there is a shared opinion by us that, when performing the above calculations, it is more advisable to use the coefficient of hydraulic friction λ.
The article shows the results of creation of a system aimed at reducing the negative impact on the ecological situation and biological systems while eliminating weeds in beetroot production. Methods of controlling weeds using machine vision technology are examined. For machine vision algorithms a library of images has been prepared. An algorithm and technical device have been developed for cultivating the soil on the early stages of beet growth.
In this paper, the use of electronic components for automating the care of agricultural crops is studied. A microprocessor control system has been developed, components of a robotic platform have been selected to ensure stable operation of the complex in the field. This platform combines ease of use and high performance. At the moment, there are few analogous foreign complexes with similar characteristics, therefore, the considered robotic platform is a promising development.
The existing range of plant identification methods and tools is considered limited in real agrotechnical tasks. The image parameters tend to differ significantly in applied solutions. (Research purpose) To develop an algorithm for crop plant recognition by a robotic device using a state-of-the-art convolutional neural network (R-CNN) and deep learning technology. (Materials and methods) A robotic device has been developed for variable rate application of plant protection products able to recognize both useful crops and weeds, determine the area of processing, namely the coordinates of the processing center and the processing radius. Mask R-CNN and Deeplabv3 plus segmenting neural networks were chosen for crop (white head cabbage) detection. The network-based algorithm detects, segments, and positions plants based on a dataset collected in the image-mask and COCO dataset formats. The data set was formed by aerial photography using an unmanned aircraft. The original images are taken by Xiaovv HD Web USB 150 degree Full HD 1080P webcam and Logitech C270 HD 720p webcam. The trained neural network for the robotic device was installed on the Nvidia Jetson AGX Xavier platform. (Results and discussion) As a result of assessing the accuracy of the model on the test data, the following values were obtained: the number of plants detected is 98 percent, the accuracy of contour detection is 94 percent. (Conclusions) It is proved that the trained neural network can be applied to any cultivated crops, taking into account the heterogeneity of their location in the field, soil types, and the percentage of weeds. As a result, the model is trained to extract the bounding box coordinates and the object (cabbage) location by pixels with the required accuracy for both synthetic and real data.
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