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A high-quality dataset is a basic requirement to ensure the training quality and prediction accuracy of a deep learning network model (DLNM). To explore the influence of label image accuracy on the performance of a concrete crack segmentation network model in a semantic segmentation dataset, this study uses three labelling strategies, namely pixel-level fine labelling, outer contour widening labelling and topological structure widening labelling, respectively, to generate crack label images and construct three sets of crack semantic segmentation datasets with different accuracy. Four semantic segmentation network models (SSNMs), U-Net, High-Resolution Net (HRNet)V2, Pyramid Scene Parsing Network (PSPNet) and DeepLabV3+, were used for learning and training. The results show that the datasets constructed from the crack label images with pix-el-level fine labelling are more conducive to improving the accuracy of the network model for crack image segmentation. The U-Net had the best performance among the four SSNMs. The Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA) and Accuracy reached 85.47%, 90.86% and 98.66%, respectively. The average difference between the quantized width of the crack image segmentation obtained by U-Net and the real crack width was 0.734 pixels, the maximum difference was 1.997 pixels, and the minimum difference was 0.141 pixels. Therefore, to improve the segmentation accuracy of crack images, the pixel-level fine labelling strategy and U-Net are the best choices.

Site-specific nutrient management (SSNM) for a rice ( Oryza sativa L.)-growing domain is typically developed by first using the nutrient omission plot technique (NOPT) to determine yield responses with added nitrogen (N), with added phosphorus (P), and with added potassium (K) and then conducting an evaluation experiment to verify an SSNM practice developed from the NOPT results. We hypothesized that an effective SSNM practice could be more rapidly developed through concurrent NOPT experiments and evaluation of a preliminary best-bet SSNM practice derived from experiences in other rice-growing domains. Our study domain was rainfed lowland rice in an Indonesian tidal swampland without past NOPT experiments and without a verified SSNM practice. Concurrent NOPT and evaluation experiments were conducted in the wet season (WS) and dry season across 20–25 farmers’ fields either directly or not directly influenced by tides. Rice yields and gross return above fertilizer cost for a preliminary SSNM practice were higher than for farmers’ fertilizer practice in both seasons and for two fertilizer practices based on soil tests in WS. The inherent ability of SSNM to adjust fertilizer rates for an attainable target yield and time fertilizer applications to match crop demand contributed to the superiority of SSNM. In NOPT experiments, relative yields without added N, without added P, and without added K respectively averaged 0.70, 0.89, and 0.88 across seasons and tidal locations. These relative yields enabled preliminary SSNM fertilizer rates to be further adjusted for the rice-growing domain. Rice growers in domains without a verified SSNM practice can benefit, before NOPT experiments are completed, by using generic SSNM guidelines for timing fertilizer applications and adjusting fertilizer rates for a target yield. After completion of NOPT experiments, the fertilizer rates can be further adjusted based on measured yield responses with added N, P, or K.

In eastern India, zinc (Zn) has emerged as the most critical micronutrient impacting the yield of rice. Experiments were conducted for 2 years during the Rabi and Kharif rice seasons at 339 on-farm locations in five districts and four agroclimatic zones of Odisha state in eastern India to study the management of Zn in rice nurseries and the transplanted crop. At each location, five treatment plots were established in which nitrogen (N), phosphorus (P) and potassium (K) were applied to rice following site-specific nutrient management as guided by Rice Crop Manager (RCM), a web-based tool. In the three treatments, the rice nursery was treated with compost (4 t ha -1 ) or 50 or 100 kg Zn sulfate ha −1 (on a nursery basis), while the transplanted crop was supplied with only N, P, and K. In the remaining two treatments, no compost or Zn was applied to the rice nursery, but 12.5 or 25 kg Zn sulfate ha −1 was applied along with N, P, and K to the transplanted crop. Rice grain yield, system yield, and gross return above fertilizer cost (GRF) were significantly greater (p < 0.05) with the application of 50 kg Zn sulfate ha −1 than with the application of compost (farmer practices) to rice nurseries. Applying 100 kg Zn sulfate ha⁻ 1 to the nursery or 12.5–25 kg Zn sulfate ha⁻ 1 to transplanted rice did not increase yield or GRF. Higher yield, grain Zn content, and GHG emissions occurred in the Rabi season, with the lowest GHG emissions recorded when the nursery received 100 kg Zn sulfate ha⁻ 1 in both seasons. The results of this study convincingly prove the usefulness of applying Zn along with site specific nutrient management (SSNM) in rice in eastern India to produce high yields and GRFs and reduce GHG emissions.

The crack detection of concrete bridges is an important link in the safety evaluation of bridge structures, and the rapid and accurate identification and detection of bridge cracks is a prerequisite for ensuring the safety and long-term stable use of bridges. To solve the incomplete crack detection and segmentation caused by the complex background and small proportion in the actual bridge crack images, this paper proposes a coarse–fine combined bridge crack detection method of “double detection + single segmentation” based on deep learning. To validate the effect and practicality of fine crack detection, images of old civil bridges and viaduct bridges against a complex background and images of a bridge crack against a simple background are used as datasets. You Only Look Once V5(x) (YOLOV5(x)) was preferred as the object detection network model (ODNM) to perform initial and fine detection of bridge cracks, respectively. Using U-Net as the optimal semantic segmentation network model (SSNM), the crack detection results are accurately segmented for fine crack detection. The test results showed that the initial crack detection using YOLOV5(x) was more comprehensive and preserved the original shape of bridge cracks. Second, based on the initial detection, YOLOV5(x) was adopted for fine crack detection, which can determine the location and shape of cracks more carefully and accurately. Finally, the U-Net model was used to segment the accurately detected cracks and achieved a maximum accuracy (AC) value of 98.37%. The experiment verifies the effectiveness and accuracy of this method, which not only provides a faster and more accurate method for fine detection of bridge cracks but also provides technical support for future automated detection and preventive maintenance of bridge structures and has practical value for bridge crack detection engineering.

This paper proposes relaxed Simultaneous Signal and Noise Matching (SSNM) conditions to address limitations in selecting source degeneration inductors for multistage LNA design, achieved by introducing controlled mismatches at the external ports. Additionally, a novel frequency-bounded mismatch envelope is introduced to guide load termination selection based on desired IM-OM (input mismatch-output mismatch) characteristics across the operating band. Building on these concepts, a systematic, easy-to-follow strategy is presented for implementing wideband multistage low-noise amplifiers (LNAs), significantly reducing reliance on blind CAD-based optimisation. This approach is validated through a three-stage MMIC LNA prototype, fabricated using a 0.15 μm GaAs process and operating from 28 to 34 GHz. The measured results closely match the simulation, demonstrating a stable gain of 23 ± 1 dB and a noise figure of 2–2.5 dB, confirming the practical effectiveness of the proposed design approach for wideband amplifiers.

Fertilizer K and P requirements for rice (Oryza sativa L.) can be determined with site-specific nutrient management (SSNM) using estimated target yield, nutrient balances, and yield gains from added nutrient. We used the QUEFTS (QUantitative Evaluation of the Fertility of Tropical Soils) model with >8000 plot-level observations to estimate the relationship between grain yield and nutrient accumulation in above-ground dry matter of irrigated rice with harvest index ≥ 0.4. Predicted reciprocal internal efficiencies (RIEs) at 60-70% of yield potential corresponded to plant accumulation of 14.6 kg N, 2.7 kg P, and 15.9 kg K per tonne of grain yield. These RIEs enable determination of plant requirements for K and P and net output of K and P in harvested grain and removed crop residues at a target yield. Yield gains for nutrient applied to irrigated rice averaged 12% for K and 9% for P for 525 to 531 observations. For fields without certain yield gain, fertilizer K and P requirements can be determined by a partial maintenance approach (i.e., fertilizer input < output in nutrient balance), which considers nutrient supply mediated through soil processes and balances trade-offs between financial loss with full maintenance rates and risk of excessive nutrient depletion without nutrient application. When yield gains to an added nutrient are certain, partial maintenance plus yield gain can be used to determine fertilizer requirements. The SSNM-based approach and algorithms enable rapid development of field-specific K and P management.

Resource-conserving irrigation and fertilizer management practices have been developed for rice systems which may help address water quality concerns by reducing N and P losses via surface runoff. Field experiments under three treatments, i.e., farmers’ conventional practice (FCP), alternate wetting and drying (AWD), and AWD integrated with site-specific nutrient management (AWD + SSNM) were carried out during two rice seasons at two sites in the southwest Yangtze River delta region. Across site years, results indicated that under AWD irrigation (i.e., AWD and AWD + SSNM), water inputs were reduced by 13.4 ~ 27.5 % and surface runoff was reduced by 30.2 ~ 36.7 % compared to FCP. When AWD was implemented alone, total N and P loss masses via surface runoff were reduced by 23.3 ~ 30.4 % and 26.9 ~ 31.7 %, respectively, compared to FCP. However, nutrient concentrations of surface runoff did not decrease under AWD alone. Under AWD + SSNM, total N and P loss masses via surface runoff were reduced to a greater extent than AWD alone (39.4 ~ 47.6 % and 46.1 ~ 48.3 % compared to FCP, respectively), while fertilizer inputs and N surpluses significantly decreased and rice grain yields increased relative to FCP. Therefore, by more closely matching nutrient supply with crop demand and reducing both surface runoff and nutrient concentrations of surface runoff, our results demonstrate that integration of AWD and SSNM practices can mitigate N and P losses via surface runoff from rice fields while maintaining high yields.

Rice growth model of ORYZA2000 was introduced, we used field datas to verify the parameters. The aim is to check the adaptability of model, when it is applied to simulate dry matter, leaf area and yield under water and nitrogen managements. The results showed that, the model can simulate dry matter accumulation preferable, in which the observed dry matter of aboveground, leaf and sheath fit well with simulated results. But the simulated yield is higher then observed, may be the reason due to the rice lodging at later stages. While the simulated leaf area is lower than observed, the reason is that we have the dead leaves are counted in measuring. ORYZA2000 provides a powerful tool to explore the impact on the environment as well as water-saving irrigation of rice under optimal water and fertilizer managements.

The applicability of five nitrogen (N) dressing methods to rice cultivation was examined using the canopy spectrum-based nitrogen optimization algorithm (CSNOA), leaf area index (LAI), site-specific N management (SSNM), N nutrition index (NNI), and N fertilizer optimization algorithm (NFOA). After base-tiller N dressing (basal dressing and top dressing at the tillering stage) at low and normal levels, rice plants were grown by the above five N dressing methods. The effects of different N dressing methods on plant dry weight, plant N accumulation, grain yield, N use efficiency, and economic benefit were analyzed. Compared with the standard method, under the low base-tiller N dressing level, the optimum N dressing rate was decreased, and the economic benefit was increased by adapting the N dressing methods of CSNOA and SSNM, whereas the optimum N dressing rate was increased, and the economic benefit was decreased by the other three N dressing methods. Under the general base-tiller N dressing level, the optimum N rate, N-use efficiency and economic benefit were increased by all N dressing methods except the NFOA. These results indicated that the CSNOA and SSNM were two good techniques for quantifying N dressing in rice, with higher economic benefit, less N input, and better applicability under different base-tiller N dressing levels.