The use of infrared spectroscopy and machine learning tools for detection of Meloidogyne infestations

Plant parasitic nematodes are generally soilborne pathogens that attack plants and cause economic losses in many crops. The infested plants show nonspecific symptoms or, often, are symptomless; therefore, diagnosis is performed by taking soil and root tissue samples. Here, we show that a combination of different infrared spectra analysis and machine learning algorithms can be used to detect plant parasitic nematode infestations before symptoms become visible, using leaves instead of roots and soil as samples. We found that tomato and guava plants infested with Meloidogyne enterorlobii produced different spectral patterns compared to uninfested plants. Using partial spectra from 1,450 to 900/cm as the "fingerprint region", principal component analyses indicated that after 5 (tomatoes) or 8 weeks (guava), plants with no visible symptoms of infestations were positively diagnosed. To improve the early detection response, we used machine learning modelling. A support vector machine (SVM) was used to obtain more robust, accurate models. The SVM model contained 34 support vectors, 17 for each level. The overall performance of the model was >97% and the total accuracy was significantly higher, demonstrating the absence of chance prediction. The best prediction of infestation was obtained at the second and fourth weeks for tomatoes and guavas, respectively, reducing the diagnostic time by half. The combined application of these techniques reduces the processing time from field to laboratory and shows enormous advantages by avoiding root and soil sampling.     

基于边缘检测和区域定位的玉米根茎导航线提取方法

基于玉米根茎图像信息，提出一种基于边缘检测和区域定位的玉米根茎导航线提取方法。首先，利用最大类间方差法自动分割2G-R-B灰度图像，得到二值化图像，结合形态学处理、位置/面积去噪方法提高二值化图像质量，对去噪图像按列累加得到列像素累加曲线；针对传统方法得到的特征点中伪特征点较多的问题，引入高斯滤波器平滑累加曲线，并运用极值法减少玉米根茎伪特征点的干扰，在提取玉米茎秆边缘直线时，提出基于最远茎秆成像宽度的双侧边缘判别思路，通过扫描每条边缘直线的四边形封闭邻域有效剔除伪边缘直线；最后，根据边缘直线二次定位玉米的根茎区域范围，并剔除伪特征点，采用最小二乘线性拟合方法准确提取导航线。试验表明，本文算法处理一幅1280像素×720像素图像耗时约236ms，特征点拟合准确率为92%。与传统方法相比，本文算法精度高、实时性好，在缺苗、杂草较多和株距不标准的情况下仍具有较强的鲁棒性。     

Application of morphometric measures in estimation of body weight and discrimination of Astyanax lacustris and Astyanax fasciatus

The objective of this study was to verify the direct and indirect correlation between morphometric measures, ratios, body weight and yield in two lambari species Astyanax lacustris and Astyanax fasciatus and whether the discriminant analysis is capable of separating and allocating the species. We used 102 lambari yellow tail and 60 lambari red tail. The fish were weighed and submitted to the evaluation of the morphometric measurements. The direct and indirect effects were evaluated by the method of track analysis, considering weight at slaughter, weight of body parts and body yields as dependent variables and measures and morphometric ratios as explanatory variables. Astyanax lacustris presented higher height and body width, carcass yield and trunk, while A. fasciatus presented higher head yield and viscera weight. The discriminant analysis was able to classify 79.5% of the two species. Track analysis demonstrated that the morphometric measurements can be used for estimation of body and body components’ weight in A. lacustris and A. fasciatus. However, regarding the corporal yield; the morphometric measures were insufficient to explain the yield variations of the species.     

农药自动混药装置的研究现状与展望

目前,我国农作物病虫害防治主要依靠施用农药,现有的大多数农药剂型需要加水稀释,配制成一定浓度后再施用。近年,我国农业航空迅速发展,对农业航空的智能化农业机械装备的需求变得非常迫切。系统总结了农药自动混药装置类型以及农药自动混药装置特点,通过对农药自动混药装置研究发展概况分析,阐明了国内农药自动混药装置所存在的问题,展望未来农药混药装置的发展趋势,为后续的研究方向奠定基础。     

Precipitation forecasting by large-scale climate indices and machine learning techniques

Global warming is one of the most complicated challenges of our time causing considerable tension on our societies and on the environment. The impacts of global warming are felt unprecedentedly in a wide variety of ways from shifting weather patterns that threatens food production, to rising sea levels that deteriorates the risk of catastrophic flooding. Among all aspects related to global warming, there is a growing concern on water resource management. This field is targeted at preventing future water crisis threatening human beings. The very first stage in such management is to recognize the prospective climate parameters influencing the future water resource conditions. Numerous prediction models, methods and tools, in this case, have been developed and applied so far. In line with trend, the current study intends to compare three optimization algorithms on the platform of a multilayer perceptron (MLP) network to explore any meaningful connection between large-scale climate indices (LSCIs) and precipitation in the capital of Iran, a country which is located in an arid and semi-arid region and suffers from severe water scarcity caused by mismanagement over years and intensified by global warming. This situation has propelled a great deal of population to immigrate towards more developed cities within the country especially towards Tehran. Therefore, the current and future environmental conditions of this city especially its water supply conditions are of great importance. To tackle this complication an outlook for the future precipitation should be provided and appropriate forecasting trajectories compatible with this region's characteristics should be developed. To this end, the present study investigates three training methods namely backpropagation (BP), genetic algorithms (GAs), and particle swarm optimization (PSO) algorithms on a MLP platform. Two frameworks distinguished by their input compositions are denoted in this study: Concurrent Model Framework (CMF) and Integrated Model Framework (IMF). Through these two frameworks, 13 cases are generated: 12 cases within CMF, each of which contains all selected LSCIs in the same lead-times, and one case within IMF that is constituted from the combination of the most correlated LSCIs with Tehran precipitation in each lead-time. Following the evaluation of all model performances through related statistical tests, Taylor diagram is implemented to make comparison among the final selected models in all three optimization algorithms, the best of which is found to be MLP-PSO in IMF.     

支持转速现场标定的玉米精密排种器电驱控制系统研究

针对现有玉米精密电驱排种控制系统无法快速适应多类型排种器排种控制的问题,在玉米CAN总线电动排种的基础上,设计了一种对玉米排种器排种驱动进行现场标定的电驱控制系统。系统在排种驱动电动机控制信号与排种盘转速之间的对应关系中,采用分段线性插值的方法现场获取排种器驱动曲线,实现排种盘转速标定与控制。以国产气吸式玉米精密排种器和指夹式玉米精密排种器为试验对象,在模拟车速下,对系统排种盘转速现场标定的控制准确性进行试验。电驱气吸式排种器排种盘转速控制性能试验中,株距设定为25 cm,车速设定为3~12 km/h(间隔3 km/h),结果表明,系统调节时间最长为0.80 s,稳态误差最大为0.81 r/min,控制精度最低为97.42%。电驱指夹式排种器排种盘转速控制性能试验中,株距分别设定为20、25、32 cm,车速设定为4~9 km/h(间隔1 km/h),结果表明,总体排种盘转速平均调节时间为1.09 s,标准差为0.26 s;总体平均稳态误差为0.38 r/min,标准差为0.23 r/min;总体平均控制精度为98.30%,标准差为1.01%。与分段PID排种转速控制系统控制性能进行对比得出,支持转速现场标定的系统具有更好的适应性,平均调节时间减少0.51 s,平均稳态误差增大0.16 r/min,平均控制精度降低0.63个百分点。选用指夹式排种器,进行了播种均匀性田间试验,株距为20 cm,车速范围为4~7 km/h(间隔1 km/h),结果表明,播种合格指数大于等于84.26%,变异系数小于等于18.29%,说明系统能够完成对玉米精密排种器排种转速控制曲线的高控制精度现场标定,能够精准控制电驱排种转速。     

Analysis of residuals for stability assessment of rice genotypes

The analysis of residuals computes general adaptabilities (GAs) and specific adaptabilities (SAs) of genotypes similar to combining ability analysis. The residual analysis is simple and robust being based on the first-degree statistics, and is assumption-free. A unique statistic of ratio of variances (VR) of residuals for individual genotypes allows comparing their stability. The residuals analysis of genotype × environment (GE) interactions was performed for grain yield (t ha^?1) of 25 rice (Oryza sativa L.) genotypes, including two developed by marker-assisted selection for root traits quantitative trait loci (QTL), tested across 16 sites. Results were compared with the linear regression analysis for (i) full set of 16 sites showing adequacy of linear model and (ii) subset of nine-poor sites showing inadequacy of linear model. There was agreement between the two analyses for situation (i) but not for situation (ii) when the regression analysis fails because of nonlinearity but the residual analysis retains its validity.     

上海水稻精量机械穴直播技术研究与应用

以上海地区主推水稻品种为材料,在精量机械穴直播条件下,开展了不同类型水稻品种的小区试验和示范,明确了精量机械穴直播水稻的生育特性、抗倒伏性、群体建成和产量表现,并分析了采用水稻精量机械穴直播后土壤的微生物群落结构的变化和水稻生产经济效益。试验示范结果表明,精量机械穴直播水稻生育期明显缩短,单株分蘖力强,群体结构合理,穗粒结构协调,干物质积累多,抗倒能力较强,增产优势明显,节本低耗,生产效率显著提高,是目前极具生产潜力的一种新型高效低耗种植模式,适合上海都市农业发展的需求,也顺应了水稻低碳种植发展趋势,具有广阔的应用前景。     

基于合成法的定位误差的分析与计算

为保证机床夹具设计的可靠性,定位误差的计算是机床夹具设计过程中必不可少的重要环节之一。为此从定位误差的产生机理出发,介绍了定位误差的概念、产生原因,并给出计算公式,有助于快速实现定位误差的求解。     

调环酸钙25%悬浮剂对直播稻抗倒伏和产量性状的影响

为了研究新型植物生长调节剂调环酸钙在直播稻上的应用效果,本研究设计了调环酸钙25%悬浮剂在直播稻武运粳8号上的田间试验,用来了解其对直播稻的抗倒伏性状和产量性状的影响。试验结果表明:在直播稻武运粳8号的分蘖期使用调环酸钙25%悬浮剂12~15g/667m2均能够显著降低水稻的株高,缩短茎基部节间长度,提高水稻的抗倒伏能力,同时也能显著提高水稻产量。