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Optimized deep learning model as a basis for fast UAV mapping of weed species in winter wheat crops
(2021)
Weed maps should be available quickly, reliably, and with high detail to be useful for site-specific management in crop protection and to promote more sustainable agriculture by reducing pesticide use. Here, the optimization of a deep residual convolutional neural network (ResNet-18) for the classification of weed and crop plants in UAV imagery is proposed. The target was to reach sufficient performance on an embedded system by maintaining the same features of the ResNet-18 model as a basis for fast UAV mapping. This would enable online recognition and subsequent mapping of weeds during UAV flying operation. Optimization was achieved mainly by avoiding redundant computations that arise when a classification model is applied on overlapping tiles in a larger input image. The model was trained and tested with imagery obtained from a UAV flight campaign at low altitude over a winter wheat field, and classification was performed on species level with the weed species Matricaria chamomilla L., Papaver rhoeas L., Veronica hederifolia L., and Viola arvensis ssp. arvensis observed in that field. The ResNet-18 model with the optimized image-level prediction pipeline reached a performance of 2.2 frames per second with an NVIDIA Jetson AGX Xavier on the full resolution UAV image, which would amount to about 1.78 ha h(-1) area output for continuous field mapping. The overall accuracy for determining crop, soil, and weed species was 94%. There were some limitations in the detection of species unknown to the model. When shifting from 16-bit to 32-bit model precision, no improvement in classification accuracy was observed, but a strong decline in speed performance, especially when a higher number of filters was used in the ResNet-18 model. Future work should be directed towards the integration of the mapping process on UAV platforms, guiding UAVs autonomously for mapping purpose, and ensuring the transferability of the models to other crop fields.
The aim of this study is to monitor short-term seasonal development of young Olympic weightlifters’ anthropometry, body composition, physical fitness, and sport-specific performance. Fifteen male weightlifters aged 13.2 ± 1.3 years participated in this study. Tests for the assessment of anthropometry (e.g., body-height, body-mass), body-composition (e.g., lean-body-mass, relative fat-mass), muscle strength (grip-strength), jump performance (drop-jump (DJ) height, countermovement-jump (CMJ) height, DJ contact time, DJ reactive-strength-index (RSI)), dynamic balance (Y-balance-test), and sport-specific performance (i.e., snatch and clean-and-jerk) were conducted at different time-points (i.e., T1 (baseline), T2 (9 weeks), T3 (20 weeks)). Strength tests (i.e., grip strength, clean-and-jerk and snatch) and training volume were normalized to body mass. Results showed small-to-large increases in body-height, body-mass, lean-body-mass, and lower-limbs lean-mass from T1-to-T2 and T2-to-T3 (∆0.7–6.7%; 0.1 ≤ d ≤ 1.2). For fat-mass, a significant small-sized decrease was found from T1-to-T2 (∆13.1%; d = 0.4) and a significant increase from T2-to-T3 (∆9.1%; d = 0.3). A significant main effect of time was observed for DJ contact time (d = 1.3) with a trend toward a significant decrease from T1-to-T2 (∆–15.3%; d = 0.66; p = 0.06). For RSI, significant small increases from T1-to-T2 (∆9.9%, d = 0.5) were noted. Additionally, a significant main effect of time was found for snatch (d = 2.7) and clean-and-jerk (d = 3.1) with significant small-to-moderate increases for both tests from T1-to-T2 and T2-to-T3 (∆4.6–11.3%, d = 0.33 to 0.64). The other tests did not change significantly over time (0.1 ≤ d ≤ 0.8). Results showed significantly higher training volume for sport-specific training during the second period compared with the first period (d = 2.2). Five months of Olympic weightlifting contributed to significant changes in anthropometry, body-composition, and sport-specific performance. However, hardly any significant gains were observed for measures of physical fitness. Coaches are advised to design training programs that target a variety of fitness components to lay an appropriate foundation for later performance as an elite athlete.
The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.
There is controversy in the literature in regards of the link between training load and injury rate. Thus, the aims of this non-interventional study were to evaluate relationships between pre-season training load with biochemical markers, injury incidence and performance during the first month of the competitive period in professional soccer players.
There is controversy in the literature in regards of the link between training load and injury rate. Thus, the aims of this non-interventional study were to evaluate relationships between pre-season training load with biochemical markers, injury incidence and performance during the first month of the competitive period in professional soccer players.
This study aimed to compare the training load of a professional under-19 soccer team (U-19) to that of an elite adult team (EAT), from the same club, during the in-season period. Thirty-nine healthy soccer players were involved (EAT [n = 20]; U-19 [n = 19]) in the study which spanned four weeks. Training load (TL) was monitored as external TL, using a global positioning system (GPS), and internal TL, using a rating of perceived exertion (RPE). TL data were recorded after each training session. During soccer matches, players’ RPEs were recorded. The internal TL was quantified daily by means of the session rating of perceived exertion (session-RPE) using Borg’s 0–10 scale. For GPS data, the selected running speed intensities (over 0.5 s time intervals) were 12–15.9 km/h; 16–19.9 km/h; 20–24.9 km/h; >25 km/h (sprint). Distances covered between 16 and 19.9 km/h, > 20 km/h and >25 km/h were significantly higher in U-19 compared to EAT over the course of the study (p = 0.023, d = 0.243, small; p = 0.016, d = 0.298, small; and p = 0.001, d = 0.564, small, respectively). EAT players performed significantly fewer sprints per week compared to U-19 players (p = 0.002, d = 0.526, small). RPE was significantly higher in U-19 compared to EAT (p = 0.001, d = 0.188, trivial). The external and internal measures of TL were significantly higher in the U-19 group compared to the EAT soccer players. In conclusion, the results obtained show that the training load is greater in U19 compared to EAT.
This study aimed to compare the training load of a professional under-19 soccer team (U-19) to that of an elite adult team (EAT), from the same club, during the in-season period. Thirty-nine healthy soccer players were involved (EAT [n = 20]; U-19 [n = 19]) in the study which spanned four weeks. Training load (TL) was monitored as external TL, using a global positioning system (GPS), and internal TL, using a rating of perceived exertion (RPE). TL data were recorded after each training session. During soccer matches, players’ RPEs were recorded. The internal TL was quantified daily by means of the session rating of perceived exertion (session-RPE) using Borg’s 0–10 scale. For GPS data, the selected running speed intensities (over 0.5 s time intervals) were 12–15.9 km/h; 16–19.9 km/h; 20–24.9 km/h; >25 km/h (sprint). Distances covered between 16 and 19.9 km/h, > 20 km/h and >25 km/h were significantly higher in U-19 compared to EAT over the course of the study (p = 0.023, d = 0.243, small; p = 0.016, d = 0.298, small; and p = 0.001, d = 0.564, small, respectively). EAT players performed significantly fewer sprints per week compared to U-19 players (p = 0.002, d = 0.526, small). RPE was significantly higher in U-19 compared to EAT (p = 0.001, d = 0.188, trivial). The external and internal measures of TL were significantly higher in the U-19 group compared to the EAT soccer players. In conclusion, the results obtained show that the training load is greater in U19 compared to EAT.
We and AI
(2021)
Large areas in the humid tropics are currently undergoing land-use change. The decrease of tropical rainforest, which is felled for land clearing and timber production, is countered by increasing areas of tree plantations and secondary forests. These changes are known to affect the regional water cycle as a result of plant-specific water demand and by influencing key soil properties which determine hydrological flow paths. One of these key properties sensitive to land-use change is the saturated hydraulic conductivity (Ks) as it governs vertical percolation of water within the soil profile. Low values of Ks in a certain soil depth can form an impeding layer and lead to perched water tables and the development of predominantly lateral flow paths such as overland flow. These processes can induce nutrient redistribution, erosion and soil degradation and thus affect ecosystem services and human livelihoods. Due to its sensitivity to land-use change, Ks is commonly used to assess the associated changes in hydrological flow paths. The objective of this dissertation was to assess the effect of land-use change on hydrological flow paths by analysing Ks as indicator variable. Sources of Ks variability, their implications for Ks monitoring and the relationship between Ks and near-surface hydrological flow paths in the context of land-use change were studied. The research area was located in central Panama, a country widely experiencing the abovementioned changes in land use. Ks is dependent on both static, soil-inherent properties such as particle size and clay mineralogy and dynamic, land use-dependent properties such as organic carbon content. By conducting a pair of studies with one of these influences held constant in each, the importance of static and dynamic properties for Ks was assessed. Applying a space-for-time approach to sample Ks under secondary forests of different age classes on comparable soils, a recovery of Ks from the former pasture use was shown to require more than eight years. The process was limited to the 0−6 cm sampling depth and showed large variability among replicates. A wavelet analysis of a Ks transect crossing different soil map units under comparable land cover, old-growth tropical rainforest, showed large small-scale variability, which was attributed to biotic influences, as well as a possible but non-conclusive influence of soil types. The two results highlight the importance of dynamic, land use-dependent influences on Ks. Monitoring studies can help to quantify land use-induced change of Ks, but there is a variety of sampling designs which differ in efficiency of estimating mean Ks. A comparative study of four designs and their suitability for Ks monitoring is used to give recommendations about designing a Ks monitoring scheme. Quantifying changes in spatial means of Ks for small catchments with a rotational stratified sampling design did not prove to be more efficient than Simple Random Sampling. The lack of large-scale spatial structure prevented benefits of stratification, and large small-scale variability resulting from local biotic processes and artificial effects of destructive sampling caused a lack of temporal consistency in the re-sampling of locations, which is part of the rotational design. The relationship between Ks and near-surface hydrological flow paths is of critical importance when assessing the consequences of land-use change in the humid tropics. The last part of this dissertation aimed at disclosing spatial relationships between Ks and overland flow as influenced by different land cover types. The effects of Ks on overland-flow generation were spatially variable, different between planar plots and incised flowlines and strongly influenced by land-cover characteristics. A simple comparison of Ks values and rainfall intensities was insufficient to describe the observed pattern of overland flow. Likewise, event flow in the stream was apparently not directly related to overland flow response patterns within the catchments. The study emphasises the importance of combining pedological, hydrological, meteorological and botanical measurements to comprehensively understand the land use-driven change in hydrological flow paths. In summary, Ks proved to be a suitable parameter for assessing the influence of land-use change on soils and hydrological processes. The results illustrated the importance of land cover and spatial variability of Ks for decisions on sampling designs and for interpreting overland-flow generation. As relationships between Ks and overland flow were shown to be complex and dependent on land cover, an interdisciplinary approach is required to comprehensively understand the effects of land-use change on soils and near-surface hydrological flow paths in the humid tropics.