作物行识别算法的虚拟试验方法

针对作物行识别算法的传统开发过程对田间作物生长周期依赖性较强，错过适当的田间图像采集时期将直接导致算法开发周期延长等问题，提出一种基于虚拟场景的作物行识别算法测试方法，即在虚拟环境下模拟农田作物行场景和图像采集系统，运用虚拟作物行图像测试作物行的识别算法。该方法在虚拟现实环境下建立作物行场景模型；提出一种融合建模法，根据作物和杂草的几何特征建立对应的三维几何模型；根据实际田间作物的空间分布特征，建立株距、行距可调的田间作物行模型；以Vega Prime为视景仿真工具，通过配置投影模式、渲染模式、视点位姿和图像采集规格，构建图像采集系统，输出作物行场景图像。以苗期棉花作物行为建模对象，对一种经过田间试验验证的双目视觉作物行识别算法进行测试试验。对比实际棉田图像对应的试验结果，同一作物行识别算法的识别正确率、偏差角和图像处理时间均相近。结果表明，本文建立的虚拟棉田作物行与实际棉田作物行场景相近，能够用于作物行识别算法的测试。     

Identifying Soil and Plant Nutrition Factors Affecting Yield in Irrigated Mature Pistachio Orchards

The main objective of this study was to evaluate the potential use of a hybrid Genetic Algorithm-Artificial Neural Network (GA–ANN) method for predicting pistachio yield and for identifying the determinant factors affecting pistachio yield in Rafsanjan region, Iran. A total of 142 pistachio orchards were selected randomly and soil samples were taken at three depths. Besides, water samples and leaves from branches without fruit were taken in each sampling point. Management information and pistachio yields were achieved by completing a questionnaire. Primarily, 58 variables affecting pistachio yield were measured, and then 26 out of them were selected by minimizing mean square error (MSE) using a feature selection (FS) method. The results showed that the accuracy of the method was acceptable. Furthermore, the sensitivity analysis showed that the main determinant features affecting the pistachio yield were the irrigation water amount, leaf phosphorus, soil soluble magnesium, electrical conductivity (EC), and leaf nitrogen.     

基于深度卷积神经网络的小麦赤霉病高光谱病症点分类方法

为快速、高效地利用高光谱成像技术诊断小麦赤霉病病症,分析了卷积层结构与光谱病症特征的关联性,并重点研究了高光谱的像元分类建模方法。首先,基于深度卷积神经网络的2种典型结构,构建了不同深度的卷积神经网络,比较了小麦赤霉病高光谱数据点集的训练和测试结果。结果显示:Visual Geometry Group(VGG)结构随着网络深度的增加,模型损失值不断下降;残差神经网络(ResNet)结构随着深度增加,损失值没有明显降低,说明ResNet网络的深度与模型性能无关。从测试集评测模型泛化性可知,具有4个基础单元模块的22层VGG网络在所有深度卷积模型中最优,其建模和验证准确率远高于传统的支持向量机(SVM),分别为0.846和0.843,测试集准确率为0.742。以VGG为基础单元构建的深度神经网络,能有效提取小麦赤霉病病症的高光谱特征。研究结果可为大尺度小麦赤霉病的智能成像诊断提供理论基础。     

Mapping the current and future distributions of Onosma species endemic to Iran

Climate change may cause shifts in the natural range of species especially for those that are geographically restricted and/or endemic species. In this study, the spatial distribution of five endemic and threatened species belonging to the genus Onosma (including O. asperrima, O. bisotunensis, O. kotschyi, O. platyphylla, and O. straussii) was investigated under present and future climate change scenarios: RCP2.6 (RCP, representative concentration pathway; optimistic scenario) and RCP8.5 (pessimistic scenario) for the years 2050 and 2080 in Iran. Analysis was conducted using the maximum entropy (MaxEnt) model to provide a basis for the protection and conservation of these species. Seven environmental variables including aspect, depth of soil, silt content, slope, annual precipitation, minimum temperature of the coldest month, and annual temperature range were used as main predictors in this study. The model output for the potential habitat suitability of the studied species showed acceptable performance for all species (i.e., the area under the curve (AUC)>0.800). According to the models generated by MaxEnt, the potential current patterns of the species were consistent with the observed areas of distributions. The projected climate maps under optimistic and pessimistic scenarios (RCP2.6 and RCP8.5, respectively) of 2050 and 2080 resulted in reductions and expansions as well as positive range changes for all species in comparison to their current predicted distributions. Among all species, O. bisotunensis showed the most significant and highest increase under the pessimistic scenario of 2050 and 2080. Finally, the results of this study revealed that the studied plant species have shown an acute adaptability to environmental changes. The results can provide useful information to managers to apply appropriate strategies for the management and conservation of these valuable Iranian medicinal and threatened plant species in the future.     

Improving wood volume predictions in dry tropical forest in the semi-arid Brazil

The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates. In this study, we analyzed a database of thinning trees from a forest management plan in the Contendas de Sincorá National Forest, southwestern Bahia State, Brazil. The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm. Adjustments, validation and statistical selection of four volumetric models were performed. Due to the difference in height values for the same diameter and the low correlation between both variables, we do not suggest models which only use the diameter at breast height (DBH) variable as a predictor because they accommodate the largest estimation errors. In comparing the best single entry model (Hohenald-Krenn) with the Spurr model (best fit model), it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion (AIC) and adjusted determination coefficient (R2 adj), which are poorer than the second best model (Schumacher-Hall). Regarding the minimum sample size, errors in estimation (root mean square error (RMSE) and bias) of the best model decrease as the sample size increases, especially when a larger number of trees with DBH≥15.0 cm are randomly sampled. Stratified sampling by diameter class produces smaller volume prediction errors than random sampling, especially when considering all trees. In summary, the Spurr and Schumacher-Hall models perform better. These models suggest that the total variance explained in the estimates is not less than 95%, producing reliable forecasts of the total volume with shell. Our estimates indicate that the bias around the average is not greater than 7%. Our results support the decision to use regression methods to build models and estimate their parameters, seeking stratification strategies in diameter classes for the sample trees. Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest. Stratified sampling of the data set for model adjustment and selection is necessary, since we find significant results with mean error square root values and bias of up to 70% of the total database.     

Predicting the potential geographic distribution of Bactrocera bryoniae and Bactrocera neohumeralis(Diptera: Tephritidae) in China using MaxEnt ecological niche modeling

Bactrocera bryoniae and Bactrocera neohumeralis are highly destructive and major biosecurity/quarantine pests of fruit and vegetable in the tropical and subtropical regions in the South Pacific and Australia. Although these pests have not established in China, precautions must be taken due to their highly destructive nature. Thus, we predicted the potential geographic distribution of B. bryoniae and B. neohumeralis across the world and in particular China by ecological niche modeling of the Maximum Entropy(Max Ent) model with the occurrence records of these two species. Bactrocera bryoniae and B. neohumeralis exhibit similar potential geographic distribution ranges across the world and in China, and each species was predicted to be able to distribute to over 20% of the globe. Globally, the potential geographic distribution ranges for these two fruit fly species included southern Asia, the central and the southeast coast of Africa, southern North America, northern and central South America, and Australia. While within China, most of the southern Yangtze River area was found suitable for these species. Notably, southern China was considered to have the highest risk of B. bryoniae and B. neohumeralis invasions. Our study identifies the regions at high risk for potential establishment of B. bryoniae and B. neohumeralis in the world and in particular China, and informs the development of inspection and biosecurity/quarantine measures to prevent and control their invasions.     

2UPR-RPU过约束并联机构刚度性能评价

运用螺旋理论和应变能方法研究了具有2R1T三自由度的2UPR-RPU过约束并联机构的静弹性刚度性能,模型考虑了杆件和关节的柔度。首先,基于螺旋理论得到分支的约束螺旋系;其次,基于材料力学得到分支中杆件的应变能,通过映射分支约束螺旋系到铰空间得到关节的应变能,通过汇总杆件、关节的应变能和卡氏定理得到与约束螺旋系对应的分支紧凑刚度矩阵;最后,通过虚功原理得到机构的总体刚度矩阵。采用有限元商业软件建立了有限元模型,并与理论模型进行对比,验证了理论模型的正确性。定义弹性元件存储的应变能与总应变能之比作为应变能因子指标,给出了应变能因子指标在规则工作空间的四维切片分布图,从应变能的角度定量评价了各弹性元件对机构刚度性能的影响程度,给出了不同载荷作用下的全局应变能因子指标。本研究为定位对机构刚度性能影响最大的弹性元件提供了新的思路。     

基于Simulink的液压系统建模与仿真

针对农业机械中液压系统结构复杂、研发周期长等问题,通过分析农业机械中液压回路各个组成元件特性,基于Simulink构建元件动态数学模型。对液压回路进行研究,分析了液压回路在不同元件参数下的动态性能,为优化液压回路系统提供参考。     

基于t分布函数的玉米群体三维模型构建方法

为利用少量实测数据快速构建能够反映因品种、环境条件、栽培管理措施等因素产生形态结构差异的玉米群体三维模型,提出基于t分布函数的玉米群体三维模型构建方法。通过实测数据构建主要株型参数的t分布函数,在其约束下生成群体内各植株主要株型参数,通过构造株型参数相似性度量函数调用玉米器官三维模板资源库中的器官几何模板,结合人工交互或图像提取的各植株生长位置与植株方位平面角2组群体结构信息生成玉米群体几何模型。利用三维数字化仪获取的玉米群体田间原位三维数字化数据所构建玉米群体计算得到的LAI与该方法构建玉米群体计算得到的LAI进行对比验证,结果表明:该方法所生成玉米群体叶面积指数与原位三维数字化数据所构建玉米群体计算得到的LAI相比,误差在±2%以内,可以满足面向可视化计算的玉米结构功能分析研究需求。方法可为玉米株型优化设计、耐密性鉴定、品种适应性评价等虚拟试验研究提供技术手段。     

Simulating long-term effect of Hyrcanian forest loss on phosphorus loading at the sub-watershed level

Conversion of forest land to farmland in the Hyrcanian forest of northern Iran increases the nutrient input, especially the phosphorus(P) nutrient, thus impacting the water quality. Modeling the effect of forest loss on surface water quality provides valuable information for forest management. This study predicts the future impacts of forest loss between 2010 and 2040 on P loading in the Tajan River watershed at the sub-watershed level. To understand drivers of the land cover, we used Land Change Modeler(LCM) combining with the Soil Water Assessment Tool(SWAT) model to simulate the impacts of land use change on P loading. We characterized priority management areas for locating comprehensive and cost-effective management practices at the sub-watershed level. Results show that agricultural expansion has led to an intense deforestation. During the future period 2010–2040, forest area is expected to decrease by 34,739 hm~2. And the areas of pasture and agriculture are expected to increase by 7668 and 27,071 hm~2, respectively. In most sub-watersheds, P pollution will be intensified with the increase in deforestation by the year 2040. And the P concentration is expected to increase from 0.08 to 2.30 mg/L in all of sub-watersheds by the year 2040. It should be noted that the phosphorous concentration exceeds the American Public Health Association′s water quality standard of 0.2 mg/L for P in drinking water in both current and future scenarios in the Tajan River watershed. Only 30% of sub-watersheds will comply with the water quality standards by the year 2040. The finding of the present study highlights the importance of conserving forest area to maintain a stable water quality.