地方文化资源数字化保护与文化创意品牌塑造

以惠州龙门农民画产业化为例,惠州龙门农民画是以“南蛮文化”作为独特视角,展现南国传统民俗风情的民间绘画,被誉为世界民间艺术奇葩。分析了产业化发展进程中龙门农民画的艺术特色和文化价值,阐述了基于知识管理的龙门农民画数字化保护思路,并从文化创意角度对龙门农民画的文化品牌塑造进行了探讨。     

日本包膜缓释肥料养分释放及微结构研究

采用恒温水培试验,研究了日本生产的五种包膜肥料(LP40、LPS40、SC60、LPSS和LPSS100)氮素释放特性,用一级反应动力学方程进行拟合,并用扫描电镜观察了包膜肥料水培前后膜的微观结构。结果表明,包膜肥料的膜材料不同,氮素释放量也不同;包膜肥料的氮素释放率随着时间的延长而增加;这五种包膜肥料的氮素释放特征均可用一级反应动力学方程来描述,氮素释放速率常数与氮素释放率成正比,与氮素溶出的半时值成反比;扫描电镜的图像分析结果显示,水培后LPSS、SC60的膜外表面完整性要好于LP40,LPSS的膜剖面结构很紧实,阻水性能好。     

便携式农作物茎秆剪切力测试仪的设计与试验

为了能方便准确地测定农作物茎秆的剪切力学特性,设计了一种手动与电动两用便携式农作物茎秆剪切仪。该仪器以交流电动机或发条驱动装置作驱动机构,以STM32单片机为核心组成嵌入式控制平台,由拉/压力传感器采集压力信号,压力曲线等试验结果可以实时显示和SD卡存储,同时还能完成做功、最大压力等参数的计算。试验结果表明,该剪切力测试仪精度达到0.84%,工作稳定可靠。该仪器可满足作物茎秆剪切力检测的要求,为进一步研究农作物茎秆的力学性能提供了方便。     

基于深度学习的玉米拔节期冠层识别

为了满足田间玉米植株快速识别与检测的需求,针对玉米拔节期提出了基于深度学习的冠层识别方法,比较并选取了适于玉米植株精准识别和定位的网络模型,并研制了玉米植株快速识别和定位检测装置.首先拍摄玉米苗期和拔节期图像共计3000张用于训练深度学习模型,针对拔节期玉米叶片交叉严重的问题,提出了以玉米株心取代玉米整株对象的标记策略...     

基于SDAE-BP的联合收割机作业故障监测

为了解决联合收割机作业故障的非线性特征信号难以提取的问题,该研究提出了一种基于堆叠去噪自动编码器（Stack Denoising Auto Encoder, SDAE）和BP神经网络（Back Propagation,BP）融合的联合收割机作业故障监测及诊断的方法（SDAE-BP）。以转速传感器采集联合收割机脱粒滚筒转速、籽粒搅龙转速、喂入搅龙转速、杂余搅龙转速、风机转速、输送链耙转速、割刀频率以及逐稿器振动频率,并将采集的数据集作为系统的输入。利用SDAE提取输入信号的深层次特征,并由BP神经网络辨识收割机作业状态,实现联合收割机故障监测。在SDAE-BP模型训练过程中,去噪自动编码器(Denoising Auto Encode, DAE)依次经带有不同分布中心噪声的原始数据进行训练,然后将其堆叠,并通过误差反向传播算法对模型参数进行优化,以提升模型识别故障性能和泛化能力。试验结果表明,对于2018年联合收割机田间试验数据,模型的故障诊断准确率达到99.00%,与SDAE和BP神经网络相比,分别提高了1.5和4.5个百分点。将SDAE－BP故障诊断模型用2019年的试验数据进行更新,并用2018年和2019年试验数据进行测试,结果表明,更新后的模型对2018年试验数据的故障识别准确率为99.25%,对2019年试验数据的故障识别准确率为98.74%,更新后模型在2019试验数据集上的故障识别准确率较未更新模型提高了6.52个百分点。该文所建模型能够准确识别联合收割机的故障类型,且具有较好的鲁棒性,对旋转型机械故障监测及预警具有参考价值。     

Cultivation and nitrogen requirements for continuous winter barley on a gleysol and a cambisol

Long- and short-term direct-drilling and seed broadcasting plus rotovation were examined as possible quick and cheap alternatives to conventional mouldboard ploughing and drilling. The experiment was the continuation of an existing long-term tillage experiment for spring barley. The conventional ploughing and long-term direct-drilling treatments continued on the same plots. The broadcasting and the short-term direct-drilling treatments were applied to previously chisel-ploughed and deep mouldboard-ploughed treatments, respectively. Autumn nitrogen treatments of 30 or 60 kg ha⁻¹ and spring nitrogen treatments of 150 or 225 kg ha⁻¹ were applied. The experiment is located on a cambisol (15% clay in topsoil) and on a gleysol (17% clay in topsoil) in south-east Scotland. Straw was removed by baling and the stubble remained when the treatments were applied.

Long-term direct-drilling yielded most over the 3 years of the experiment and was particularly successful after the unusually wet autumn and winter of the third season. Short-term direct-drilling was the lowest yielding treatment in the first season only. The success of the long-term direct-drilling treatment was associated with the development of a stable, protective surface tilth as a result of organic-matter accumulation. This was associated with some soil structural improvement deeper in the profile in the long-term direct-drilled gleysol as shown by measurements of air permeability. Crop performance apparently was not related to soil compactness or cone resistance. The relatively high rates of nitrogen applied, both autumn and spring, gave worthwhile crop responses except for the third season, when the crop lodged. Short-term direct-drilling, broadcasting with rotovation and ploughing with drilling gave similar average yields on the gleysol, but on the cambisol broadcasting with rotovation outyielded the other two treatments by an average of 0.3 t ha⁻¹.     

Jack beans and vetiver grass growth on iron ore tailing sediments from the Doce River dam disaster in Brazil: plant growth regulator effects under different edaphic conditions

Journal of Soils and Sediments - After the greatest environmental disaster in the history of Brazil and the deposition of the iron ore tailings in alluvial regions, the process of revegetation for...     

BODIUM—A systemic approach to model the dynamics of soil functions

The increasing demand for biomass for food, animal feed, fibre and bioenergy requires optimization of soil productivity, while at the same time, protecting other soil functions such as nutrient cycling and buffering, carbon storage, habitat for biological activity and water filter and storage. Therefore, one of the main challenges for sustainable agriculture is to produce high yields while maintaining all the other soil functions. Mechanistic simulation models are an essential tool to fully understand and predict the complex interactions between physical, biological and chemical processes of soils that generate those functions. We developed a soil model to simulate the impact of various agricultural management options and climate change on soil functions by integrating the relevant processes mechanistically and in a systemic way. As a special feature, we include the dynamics of soil structure induced by tillage and biological activity, which is especially relevant in arable soils. The model operates on a 1D soil profile consisting of a number of discrete layers with dynamic thickness. We demonstrate the model performance by simulating crop growth, root growth, nutrient and water uptake, nitrogen cycling, soil organic matter turnover, microbial activity, water distribution and soil structure dynamics in a long-term field experiment including different crops and different types and levels of fertilization. The model is able to capture essential features that are measured regularly including crop yield, soil organic carbon, and soil nitrogen. In this way, the plausibility of the implemented processes and their interactions is confirmed. Furthermore, we present the results of explorative simulations comparing scenarios with and without tillage events to analyse the effect of soil structure on soil functions. Since the model is process-based, we are confident that the model can also be used to predict quantities that have not been measured or to estimate the effect of management measures and climate states not yet been observed. The model thus has the potential to predict the site-specific impact of management decisions on soil functions, which is of great importance for the development of a sustainable agriculture that is currently also on the agenda of the ‘Green Deal’ at the European level.