In this study, we investigated the influence of soil cultivation method on the productivity and quality of pasture grass fodder. We found that increasing the depth of cultivation from 8–10 cm to 20–22 cm (using surface tillage with disk implements) improved the productivity of all the grass species studied – <em>Phleum pratense</em>, <em>Lolium perenne</em>, <em>Festuca </em><em>orientalis</em>, <em>Dactylis glomerata</em>, <em>Bromus </em><em>inermis</em>, <em>Phalaris arundinacea</em>, <em>Festuca</em><em> </em><em>rubra </em>– by an average of 2%–3% at an LSD<sub>05</sub> of 0.30 t ha<sup>−1</sup>, over a period of 3 years. On average, the most important factor influencing the production of 1 ha of dry mass appeared to be the species of grass, accounting for 57% of the variation. The depth of soil tillage was also important, accounting for 43% of the variation. Of all the species studied, the highest productivity was exhibited by <em>Lolium perenne </em>(0.35 t ha<sup>−1</sup> of dry weight). Increasing the soil cultivation depth led to an increase in the content of crude protein and albumen (0.9%–1.1%). According to the analysis of organic matter content and digestibility of the fodder, across the different depths of soil cultivation, the early ripening species <em>Dactylis glomerata</em>, and the average ripening species <em>Festuca </em><em>orientalis</em>, <em>Lolium perenne</em>, and <em>Bromus </em><em>inermis</em>, performed best. Considering the different depths of soil cultivation, <em>Lolium perenne </em>[154 g; surface tillage (disking) 8–10 cm] and <em>Festuca</em><em> </em><em>orientalis </em>(152 g; ploughing 20–22 cm) provided the most fodder units with digestible protein.
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.
The influence of different agriculture systems and measures of basic tillage on nutrient regime, its relation to the number of microorganisms involved in transformation of organic matter in typical black soils was studied. Direction of the formation processes of some physiological indices in the field of wheat winter was investigated. It was found that the most favorable conditions of nutrition and soil microflora development, as well as indices of crop growth and development are created when using ecological system of agriculture.
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.
Analysis of changes in hydrothermal conditions of growing crops in the forest steppe zone of Ukraine over a period of 2004–2016 showed that by the average monthly air temperature more than a half of the years under study and by rainfall nearly a third of the researched period differed significantly from the average long-term value and were close to extreme weather. Statistical analysis of long-term indicators of the air temperature regime is evidence of a steady trend towards an increase in average annual air temperature with significant fluctuations in indices in separate periods from 7.9 ± 2.9 to 10.0 ± 2.5oС and a decrease in the amount and instability of natural moisture entry. The influence of weather conditions on the formation of productivity of spiked cereals (winter and spring wheat, spring barley) and maize was assessed at the current agrometeorological risks in the forest steppe of Ukraine. Based on the correlation-regression analysis, mathematical models were created that reproduce the dependence of grain yields upon the complex weather conditions of the growing season, the impact of which reached 60–70%. The conditions of eight years (2006–2008, 2011–2014 and 2016), when the hydrothermal index for the vegetation period was 1.13–1.76, turned out to be optimal by hydrothermal indicators to harvest maize yield at 5.83–9.47 t/ha. However, the years of 2005, 2009–2010 and 2015 were unfavorable as they received precipitation by 120 mm lower than a norm or 36% of the norm. The rainfall by 37–61% lower than a norm in June–July and grain yield 3.12–6.51 t/ha were also characteristic of the years mentioned above.
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