Cotton Image Segmentation Algorithm Based on Fusion Method of Markov Random Field and Quantum Particle Swarm Cluster

[Objective] The aim of this study was to improve the cotton image segmentation accuracy in a picking robot image processing system. [Method] An image segmentation algorithm based on a fusion method of Markov random field and quantum particle swarm optimization clustering was proposed. The process of the proposed algorithm is as follows: first, transform the RGB (red, green, blue) images into grayscale; second, use it to segment these images; finally, the threshold of the connected area is set on the basis of the segmented image to obtain the target area. Then, the cotton front image and the cotton side image are selected from the images collected from different angles. The segmentation experiment was carried out by using this algorithm, and compared with the Otsu algorithm, the fuzzy C-means algorithm, the quantum particle swarm image segmentation algorithm and the Markov random field image segmentation algorithm. [Result] The results showed that the segmentation accuracy and peak signal to noise ratio of the proposed algorithm were 98.94% and 77.48 dB. When compared with the Otsu algorithm, fuzzy C-means algorithm, quantum particle swarm optimization algorithm and Markov random field algorithm, the average segmentation accuracy and peak signal to noise ratio of the proposed algorithm increased by 2.47%–4.56%, and 9.81–13.11 dB, respectively. [Conclusion] The proposed algorithm had higher segmentation accuracy and higher peak signal to noise ratio than the other algorithms tested.     

Intensive organic vegetable production increases soil organic carbon but with a lower carbon conversion efficiency than integrated management

Intensive vegetable production in greenhouses has rapidly expanded in China since the 1990s and increased to 1.3 million ha of farmland by 2016, which is the highest in the world. We conducted an 11‐year greenhouse vegetable production experiment from 2002 to 2013 to observe soil organic carbon (SOC) dynamics under three management systems, i.e., conventional (CON), integrated (ING), and intensive organic (ORG) farming. Soil samples (0–20 and 20–40 cm depth) were collected in 2002 and 2013 and separated into four particle‐size fractions, i.e., coarse sand (> 250 µm), fine sand (250–53 µm), silt (53–2 µm), and clay (< 2 µm). The SOC contents and δ¹³C values of the whole soil and the four particle‐size fractions were analyzed. After 11 years of vegetable farming, ORG and ING significantly increased SOC stocks (0–20 cm) by 4008 ± 36.6 and 2880 ± 365 kg C ha^?1 y^?1, respectively, 8.1‐ and 5.8‐times that of CON (494 ± 42.6 kg C ha^?1 y^?1). The SOC stock increase in ORG at 20–40 cm depth was 245 ± 66.4 kg C ha^?1 y^?1, significantly higher than in ING (66 ± 13.4 kg C ha^?1 y^?1) and CON (109 ± 44.8 kg C ha^?1 y^?1). Analyses of ¹³C revealed a significant increase in newly produced SOC in both soil layers in ORG. However, the carbon conversion efficiency (CE: increased organic carbon in soil divided by organic carbon input) was lower in ORG (14.4%–21.7%) than in ING (18.2%–27.4%). Among the four particle‐sizes in the 0–20 cm layer, the silt fraction exhibited the largest proportion of increase in SOC content (57.8% and 55.4% of the SOC increase in ORG and ING, respectively). A similar trend was detected in the 20–40 cm soil layer. Over all, intensive organic (ORG) vegetable production increases soil organic carbon but with a lower carbon conversion efficiency than integrated (ING) management.     

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.     

麻山药种植田沙壤土流动性离散元模型颗粒放尺效应

为简化麻山药-沙壤土复合体离散元模型，提高离散单元法在农业领域中的计算效率，以沙壤土为研究对象，在EDEM离散元软件中构建非球形颗粒，进行双目标参数标定试验，采用放大颗粒粒径方法，利用转鼓、坍塌与FT4流变仿真试验，从颗粒群动态堆积角、流动质量、流动速率以及能量等方面探究了颗粒放尺效应对颗粒群物理特性的影响。试验结果表明：干燥处理后的2 mm粒径沙壤土基质静态堆积角和动态堆积角平均值分别为32.16°和35.02°；与独立标定试验相比，双目标标定试验获得的仿真参数更具准确性与唯一性；在动态堆积角试验中，真实沙壤土颗粒在转鼓中所形成的动态堆积角随粒径和旋转速度的增大而减小，而在仿真试验中，转鼓转速相同情况下，非球形颗粒群在增大粒径的情况下所产生的动态休止角差异较小；坍塌试验中，不同粒径颗粒群在流动过程中的流动质量与平均流速变化趋势基本一致，但误差随粒径增大而增大；颗粒质量相同时，将粒径分别放大2倍及4倍，颗粒数量同比减少87.24%、98.92%，仿真时间明显缩短，计算效率显著提高；FT4流变试验表明，当放尺因子S为2时，阻力FV及其力矩T随时间变化的拟合曲线值约为原尺时的2倍，而当放尺因子S为4时，与原尺相比，拟合曲线斜率差异显著，相关性明显降低。研究结果可为构建沙壤土离散元放尺模型提供理论依据，同时也可为农业工程离散元放尺仿真计算提供一定参考。     

AM真菌对桑树根围土壤团聚体的影响机制

为揭示丛枝菌根 (Arbuscular mycorrhizal，AM)真菌对植桑土壤的影响及机制，采用盆栽试验研究接种摩西管柄囊霉 (Funneliformis mosseae，Fm)和根内根生囊霉 (Rhizophagus intraradices，Ri)对土壤有机碳(Soil organic carbon, SOC)、球囊霉素相关土壤蛋白 (Glomalin related soil protein, GRSP)及团聚体组成与稳定性的影响。结果表明：⑴ 接种Ri显著增加土壤大团聚体百分比，并提高平均质量直径 (Mean weight diameter, MWD)和几何平均直径 (Geometric mean diameter, GMD)、显著降低团聚体破坏率 (Percentage of aggregate destruction, PAD)。⑵ 接种Fm和Ri均显著增加微团聚体SOC含量，接种Fm显著降低大团聚体总GRSP含量，而接种Ri却显著增加大团聚体和微团聚体总GRSP含量及易提取GRSP含量。⑶ 接种AM真菌对整体SOC的效应为负，土壤总GRSP对SOC占比在25.5%~76.5%之间，土壤易提取GRSP对SOC占比在4.87%~5.93%之间，且Ri的接种效应高于Fm。⑷ 总GRSP、易提取GRSP和SOC对团聚体组成表现均为正向显著影响，其中易提取GRSP是主要驱动因子，而总GRSP是土壤团聚体稳定性的主要影响因子。综上，AM真菌作用下桑树根围土壤团聚体得以改善并趋于稳定，Ri的接种效应明显大于Fm；土壤团聚体的形成主要依赖易提取GRSP，而其稳定性主要受总GRSP影响。     

川西高山树线交错带海拔梯度上土壤有机碳稳定性特征

高山树线交错带是高海拔地带对环境变化最为敏感的生态系统之一，土壤有机碳库及其稳定性在生态系统变化中具有显著指示作用。通过物理-化学方法对土壤不同活性有机碳进行连续分级分离，研究土壤有机碳不同组分在海拔环境梯度下分布特征及稳定性。结果表明，高山树线交错带土壤有机碳主要集中在物理分级组分（≥0.02 mm），且粒径越大有机碳含量越高，即主要以颗粒态有机碳形式存在，占比均高于98%；在<0.02 mm有机-无机复合体中有机碳采用化学分级分离，这部分有机碳占比低于土壤总有机碳2%，且主要以腐殖质（胡敏素）形式存在（占土壤总有机碳0.6%~0.8%），腐殖质占有机碳比例远低于一般土壤；随着海拔升高，土壤有机碳总量上升，颗粒态有机碳（物理分级组分）比例升高，胡敏素类腐殖质比例下降。因此，川西高海拔树线交错带的高寒土壤有机碳，主要以不稳定有机碳（POC）形式存在，随着温度上升（海拔降低）将导致土壤矿质化和腐殖化加剧，有机碳总量下降，土壤不稳定性组分（物理分级组分）降低，土壤稳定性（腐殖化比例）上升。     

基于流式细胞术的桫椤基因组大小测定

以买麻藤(Gnetum montanum)为内参,测定5种常用裂解液(Tris·MgCl_2、LB01、WPB、Otto’s、Galbraith’s)对桫椤(Alsophila spinulosa)细胞核的裂解效果,并通过比较桫椤和买麻藤流式细胞仪测定的荧光峰值,计算出桫椤的基因组大小。结果表明:5种裂解液中,WPB为桫椤最佳裂解液;桫椤基因组大小为6 003.25Mb,即2C DNA含量为6.138 pg。