In order to realize the intelligent online yield estimation of tomato in the plant factory with artificial lighting (PFAL), a recognition method of tomato red fruit and green fruit based on improved yolov3 deep learning model was proposed to count and estimate tomato fruit yield under natural growth state. According to the planting environment and facility conditions of tomato plants, a computer vision system for fruit counting and yield estimation was designed and the new position loss function was based on the generalized intersection over union (GIoU), which improved the traditional YOLO algorithm loss function. Meanwhile, the scale invariant feature could promote the description precision of the different shapes of fruits. Based on the construction and labeling of the sample image data, the K-means clustering algorithm was used to obtain nine prior boxes of different specifications which were assigned according to the hierarchical level of the feature map. The experimental results of model training and evaluation showed that the mean average precision (mAP) of the improved detection model reached 99.3%, which was 2.7% higher than that of the traditional YOLOv3 model, and the processing time for a single image declined to 15 ms. Moreover, the improved YOLOv3 model had better identification effects for dense and shaded fruits. The research results can provide yield estimation methods and technical support for the research and development of intelligent control system for planting fruits and vegetables in plant factories, greenhouses and fields.
Winter wheat is one of the widespread crops in Ukraine. The search for methods to increase the yield and consumer properties of wheat, without compromising environmental safety, is one of the important scientific problems. The principles of precision agriculture point to the proper positioning of the seeds, recommending the method of “upward germination” (positioning the wheat germ vertically). The main objective of this study was to develop a new geometric model of wheat grain with a displaced centre of mass, as well as to conduct the theoretical research and numerical experiments on the orientation of grains using their multiple impact interaction with inclined surfaces. A new model of germ consisting of two different end semispheres and amid-line truncated cone was proposed, with a displaced centre of mass. Taking into account the physical properties of the objects, the concept of arrangement of gravity orientation of seeds in a stream was applied. This concept was based on various ratios of kinetic parameters of bodies with a displaced centre of gravity following an impact. The results showed that the orientation process can be controlled by changing the inclination angles and the length of the walls of the tray orientator within the working velocity range. This must be done before impact interaction of 0.2-0.3 m s−1 when the inclination angles of the impact interaction planes are 24-32°C.
The object of research in this work was cast iron for machine-building parts, alloyed with Al. The possibility of improving the mechanical properties of cast iron by choosing the optimal Mn – Al combinations, depending on the carbon content in the cast iron, was determined. The study was carried out on the basis of available retrospective data of serial industrial melts by constructing the regression equation for the ultimate strength of cast iron in the three-factor space of the input variables C – Mn – Al. The optimization problem was solved by the ridge analysis method after reducing the dimension of the factor space by fixing the carbon content at three levels: C = 3 %, C = 3.3 %, and C = 3.6 %. It was found that the maximum values of the ultimate strength are achieved at the minimum level of carbon content (C = 3%) and are in the range of values close to 300 MPa. In this case, the Al content is in the range (2.4–2.6) %, and the Mn content is about 0.82 %. With an increase in the carbon content, there is a tendency to a decrease in the content of Mn and Al in the alloy, which is necessary to ensure the ultimate strength close to 300 MPa. The results of the ridge analysis of the response surface also showed that at the upper limit of the carbon content (C = 3.6%), it is not possible to reach the ultimate strength of 300 MPa in the existing range of Mn and Al variation. All solutions are verified for the following ranges of input variables C = (2.94–3.66) %, Mn = (0.5–1.1) %, Al = (1.7–2.9) %. Graphical-analytical descriptions of the optimal Mn – Al ratios are obtained, depending on the actual content of carbon in the alloy, which make it possible to purposefully select the optimal melting modes by controlling the tensile strength of the alloy
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