基于加权列联表分析法的安徽省巢湖市小麦赤霉病预测

为有效预防安徽省巢湖市小麦赤霉病危害,选取巢湖市2003—2019年平均相对湿度、雨湿日光系数（雨日×降雨量×平均相对湿度/日照时数）、降雨量与日照时数作为预测因子,运用加权列联表分析法,分别对2011年秸秆还田前后小麦赤霉病流行进行预测。利用该方法对2020—2021年安徽省巢湖市小麦赤霉病发生情况进行预测,模型预测结果与小麦赤霉病实际发生情况匹配度较高,准确率为100%,表明该方法可于小麦赤霉病防治适期前50 ～ 60 d（小麦返青期）对小麦赤霉病发生程度进行中长期预测。基于加权列联表分析法建立的小麦赤霉病预测方法提高了安徽省巢湖市小麦赤霉病预测的准确性,为该病害的防治提供了部分参考依据。     

The vegetation classification in coal mine overburden dump using canopy spectral reflectance

The canopy spectral characteristics of typical plants in the overburden of the Fuxin coal mine dump were measured and analyzed. The reflectance of Leymus chinensis was affected by the soil, with a slight shift from green (550 nm) to the near infrared (NIR) region. Changes in chlorophyll and water absorption were not significant in the red (670 nm) and NIR bands, respectively. The reflectance curve trend for Artemisia lavandulaefolia was similar to those of Sophora japonica and Ulmus pumila, while the reflectance of S. japonica and U. pumila fluctuated in the NIR region (760-1200 nm), especially with greater water absorption around 930 and 1120 nm. In contrast, the reflectance of A. lavandulaefolia fluctuated slightly around 930 nm and a significant peak appeared at 1127 nm. In addition, the spectral reflectance of S. japonica was lower than for the other species in the visible band (400-700 nm). However, it was higher than for L. chinensis in the NIR region (780-1200 nm). Three classifiers, the self-organizing map (SOM), learning-vector quantization (LVQ), and a probabilistic neural network (PNN), were used to classify the vegetation and the results of all classifiers were compared based on total spectral reflectance data from 400 to 1200 nm. The PNN was the best classifier in terms of training and testing accuracy. The first difference reflectance was calculated, and the red edge parameter was able to classify the herbs (L. chinensis and A. lavandulaefolia) and the arbores (S. japonica and U. pumila) with an accuracy of 77 and 84%, respectively, although it did not perform as well for detail species. A mixing parameter matrix was built based on the sensitive wavelengths (550, 674, 810, 935, and 1125 nm), the vegetation indices (SAVI and NDGI), and the water absorption slope. High classification accuracy was obtained by applying the mixing parameter matrix. This method could be used for revegetation monitoring and in decision making.     

Hyperspectral waveband selection for internal defect detection of pickling cucumbers and whole pickles

Hyperspectral imaging under transmittance mode has shown potential for detecting internal defect, however, the technique still cannot meet the online speed requirement because of the need to acquire and analyze a large amount of image data. This study was carried out to select important wavebands for further development of an online inspection system to detect internal defect in pickling cucumbers and whole pickles. Hyperspectral transmittance/reflectance images were acquired from normal and defective cucumbers and whole pickles using a prototype hyperspectral reflectance (400-740 nm)/transmittance (740-1000 nm) imaging system. Up to four-waveband subsets were determined by a branch and bound algorithm combined with the k-nearest neighbor classifier. Different waveband binning operations were also compared to determine the bandwidth requirement for each waveband combination. The highest classification accuracies of 94.7 and 82.9% were achieved using the optimal four-waveband sets of 745, 805, 965, and 985 nm at 20 nm spectral resolution for cucumbers and of 745, 765, 885, and 965 nm at 40 nm spectral resolution for whole pickles, respectively. The selected waveband sets will be useful for online quality detection of pickling cucumbers and pickles.     

Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards

This paper evaluates the feasibility of applying visible-near infrared spectroscopy for in-field detection of Huanglongbing (HLB) in citrus orchards. Spectral reflectance data from the wavelength range of 350-2500 nm with 989 spectral features were collected from 100 healthy and 93 HLB-infected citrus trees using a visible-near infrared spectroradiometer. During data preprocessing, the spectral data were normalized and averaged every 25 nm to reduce the spectral features from 989 to 86. Three datasets were generated from the preprocessed raw data: first derivatives, second derivatives, and a combined dataset (generated by integrating preprocessed raw data, first derivatives and second derivatives). The preprocessed datasets were analyzed using principal component analysis (PCA) to further reduce the number of features used as inputs in the classification algorithm. The dataset consisting of principal components were randomized and separated into training and testing datasets such that 75% of the dataset was used for training; while 25% of the dataset was used for testing the classification algorithms. The number of samples in the training and testing datasets was 145 and 48, respectively. The classification algorithms tested were: linear discriminant analysis, quadratic discriminant analysis (QDA), k-nearest neighbor, and soft independent modeling of classification analogies (SIMCA). The reported classification accuracies of the algorithms are an average of three runs. When the second derivatives dataset were analyzed, the QDA-based classification algorithm yielded the highest overall average classification accuracies of about 95%, with HLB-class classification accuracies of about 98%. In the combined dataset, SIMCA-based algorithms resulted in high overall classification accuracies of about 92% with low false negatives (less than 3%).     

Crop biomass and humidity related factors reflect the spatial distribution of phytopathogenic <Emphasis Type="Italic">Fusarium</Emphasis> fungi and their mycotoxins in heterogeneous fields and landscapes

Fusarium head blight (FHB) is a global problem in small-grains agriculture that results in yield losses and, more seriously, produces harmful toxins that enter the food chain. This study builds on previous research identifying within-field humidity as an important factor in infection processes by Fusarium species and its mycotoxin production. Environmental variables describing topographic control of humidity (TWI), soil texture and related moisture by electrical conductivity (ECa), and canopy humidity by density (NDVI) were explored in their relationship to the fungal infection rates, the abundance of trichothecene-producing Fusarium spp. as determined by TRI 6 gene copies and mycotoxin accumulation. Field studies were performed at four field sites in northeastern Germany in 2009 and 2011. In the wet year 2011, a high Fusarium infection rate resulted in a high abundance of trichothecene-producing fungi as well as high concentrations of mycotoxins. Simultaneously, Fusarium spp. inhibited the development of other filamentous fungi. Overall, a very heterogeneous distribution of pathogen infections and mycotoxin concentrations were displayed in each field in each landscape. The NDVI serves as an important predictor of the occurrence of phytopathogenic Fusarium fungi and their mycotoxins in a field and landscape scale. In addition, the ECa reflects the distribution of the most frequently occurring mycotoxin deoxynivalenol within the fields and landscapes. In all cases, TWI was not found to be a significant variable in the models. All in all, the results extend our knowledge about suitable indicators of FHB infection and mycotoxin production within the field.     

基于BP神经网络的稻麦轮作区小麦赤霉病预测模型

为提高稻麦轮作区小麦赤霉病发生程度预测的准确度，以江苏洪泽、姜堰和张家港的历年赤霉病病穗率、田间初始菌源和气象因子为数据集，采用逐步回归分析，筛选影响小麦赤霉病发生的关键因子，进一步构建不同生态区的基于 BP 神经网络算法的小麦赤霉病发生预测模型，对江苏姜堰和张家港地区小麦赤霉病病穗率预测准确度均为 100%，对江苏洪泽地区小麦赤霉病病穗率预测准确度为 91.67%。     

Evaluating high resolution SPOT 5 satellite imagery for crop identification

High resolution satellite imagery offers new opportunities for crop monitoring and assessment. A SPOT 5 image acquired in May 2006 with four spectral bands (green, red, near-infrared, and short-wave infrared) and 10-m pixel size covering intensively cropped areas in south Texas was evaluated for crop identification. Two images with pixel sizes of 20 m and 30 m were also generated from the original image to simulate coarser resolution satellite imagery. Two subset images covering a variety of crops with different growth stages were extracted from the satellite image and five supervised classification techniques, including minimum distance, Mahalanobis distance, maximum likelihood, spectral angle mapper (SAM), and support vector machine (SVM), were applied to the 10-m subset images and the two coarser resolution images to identify crop types. The effects of the short-wave infrared band and pixel size on classification results were also examined. Kappa analysis showed that maximum likelihood and SVM performed better than the other three classifiers, though there were no statistical differences between the two best classifiers. Accuracy assessment showed that the 10-m, four-band images based on maximum likelihood resulted in the best overall accuracy values of 91% and 87% for the two respective sites. The inclusion of the short-wave infrared band statistically significantly increased the overall accuracy from 82% to 91% for site 1 and from 75% to 87% for site 2. The increase in pixel size from 10 m to 20 m or 30 m did not significantly affect the classification accuracy for crop identification. These results indicate that SPOT 5 multispectral imagery in conjunction with maximum likelihood and SVM classification techniques can be used for identifying crop types and estimating crop areas.     

Classification of sugar beet and volunteer potato reflection spectra with a neural network and statistical discriminant analysis to select discriminative wavelengths

The objectives of this study were to determine the reflectance properties of volunteer potato and sugar beet and to assess the potential of separating sugar beet and volunteer potato at different fields and in different years, using spectral reflectance characteristics. With the ImspectorMobile, vegetation reflection spectra were successfully repeatedly gathered in two fields, on seven days in 2 years that resulted in 11 datasets. Both in the visible and in the near-infrared reflection region, combinations of wavelengths were responsible for discrimination between sugar beet and volunteer potato plants. Two feature selection methods, discriminant analysis (DA) and neural network (NN), succeeded in selecting sets of discriminative wavebands, both for the range of 450-900 and 900-1650 nm. First, 10 optimal wavebands were selected for each of the 11 available datasets individually. Second, by calculating the discriminative power of each selected waveband, 10 fixed wavebands were selected for all 11 datasets analyses. Third, 3 fixed wavebands were determined for all 11 datasets. These three wavebands were chosen because these had been selected by both DA and NN and were for sensor 1: 450, 765, and 855 nm and for sensor 2: 900, 1440, and 1530 nm. With the resulting three sets of wavebands, classifications were performed with a DA, a neural network with 1 hidden neuron (NN1) and a neural network with two hidden neurons (NN2). The maximum classification performance was obtained with the near-infrared sensor coupled to the NN2 method with an optimal adapted set of 10 wavebands, where the percentages were 100 ± 0.1 and 1 ± 1.3% for true negative (TN) classified volunteer potato plants and false negative (FN) classified sugar beet plants respectively. In general the NN2 method gave the best classification results, followed by DA and finally the NN1 method. When the optimal adapted waveband sets were generalized to a set of 10 fixed wavebands, the classification results were still at a reasonable level of a performance at 87% TN and 1% FN for the NN2 classification method. However, when a further reduction and generalization was made to 3 fixed wavebands, the classification results were poor with a minimum performance of 69% TN and 3% FN for the NN2 classification method. So, these results indicate that for the best classification results it is required that the sensor and classification system adapt to the specific field situation, to optimally discriminate between volunteer potato and sugar beet pixel spectra.     

芽孢杆菌拮抗镰孢菌机制的研究进展

镰孢菌（Fusarium）分泌多种真菌毒素引起的枯萎病、赤霉病、根腐病和穗腐病等植物病害,造成重大的作物生产损失。化学防治是防治镰孢菌的重要手段,但其带来的镰孢菌抗性和生态环境污染等问题,严重制约了农业可持续发展。在病害管理中使用生物防治剂为控制镰孢菌引起的植物病害提供了一种安全、有效和可持续的手段,因此生物防治具有比化学防治更深远的优势。在生物防治剂中使用最广泛的生防微生物是芽孢杆菌属（Bacillus）的成员,其可通过多种机制为植物提供有效控制镰孢菌入侵的方案。芽孢杆菌作为一种优良的生物防治剂已被广泛研究,其可通过生态位竞争、产生抗菌物质、诱导植物系统抗性和塑造根际健康微生物组来拮抗镰孢菌侵染,本文从以上4个方面对芽孢杆菌拮抗镰孢菌的机制进行综述,为农业生产中芽孢杆菌防治镰孢菌病害的研究提供参考。     

Performance evaluation of a crop/weed discriminating microsprayer

An intelligent real-time microspraying weed control system was developed. The system distinguishes between weed and crop plants and a herbicide (glyphosate) is selectively applied to the detected weed plants. The vision system captures 40 RGB images per second, each covering 140 mm by 105 mm with an image resolution of 800 × 600 pixels. From the captured images the forward velocity is estimated and the spraycommands for the microsprayer are calculated. Crop and weed plants are identified in the image, and weed plants are sprayed. Performance of the microsprayer system was evaluated under laboratory conditions simulating field conditions. A combination of maize (Zea mays L.), oilseed rape (Brassica napus L.) and scentless mayweed (Matricaria inodora L.) plants, in growth stage BBCH10, was placed in pots, which were then treated by the microspray system. Maize simulated crop plants, while the other species simulated weeds. The experiment were conducted at a velocity of 0.5 m/s. Two weeks after spraying, the fraction of injured plants was determined visually. None of the crop plants were harmed while 94% of the oilseed rape and 37% of the scentless mayweed plants were significantly limited in their growth. Given the size and shape of the scentless mayweed plants and the microsprayer geometry it was calculated that the microsprayer could only hit 64% of the scentless mayweed plants. The system was able to effectively control weeds larger than 11 mm × 11 mm.     

Hyperspectral species mapping for automatic weed control in tomato under thermal environmental stress

This work studied the impacts of variations in environmental temperature on hyperspectral imaging features in the visible and near infrared regions for robust species identification for weed mapping in tomato production. Six major Californian processing tomato cultivars, black nightshade (Solanum nigrum L.) and redroot pigweed (Amaranthus retroflexus L.) were grown under a variety of diurnal temperature ranges simulating conditions common in the Californian springtime planting period and one additional treatment simulating greenhouse growing conditions. The principal change in canopy reflectance with varying temperature occurred in the 480-670 and 720-810 nm regions. The overall classification rate ranged from 62.5% to 91.6% when classifiers trained under single temperatures were applied to plants grown at different temperatures. Eliminating the 480-670 nm region from the classifier’s feature set mitigated the temperature effect by stabilizing the total crop vs. weed classification rate at 86.4% over the temperature ranges. A site-specific recalibration method was also successful in alleviating the bias created by calibrating the models on the extreme temperatures and increased the classification accuracy to 90.3%. A global calibration method, incorporating all four temperature conditions in the classifier feature space, provided the best average total classification accuracy of 92.2% out of the methods studied, and was fairly robust to the varying diurnal temperature conditions.     

Hyperspectral image analysis for water stress detection of apple trees

Plant stress significantly reduces plant productivity. Automated on-the-go mapping of plant stress would allow for a timely intervention and mitigation of the problem before critical thresholds were exceeded, thereby maximizing productivity. The spectral signature of plant leaves was analyzed by a hyperspectral camera to identify the onset and intensity of plant water stress. Five different levels of water treatment were created in young apple trees (cv. ‘Buckeye Gala’) in a greenhouse. The trees were periodically monitored with a hyperspectral camera along with an active-illuminated spectral vegetation sensor and a digital color camera. Individual spectral images over a 385-1000 nm wavelength range were extracted at a specific wavelength to estimate reflectance and generate spectral profiles for the five different water treatment levels. Various spectral indices were calculated and correlated to stress levels. The highest correlation was found with Red Edge NDVI at 705 and 750 nm in narrowband indices and NDVI at 680 and 800 nm in broadband indices. The experimental results indicated that intelligent optical sensors could deliver decision support for plant stress detection and management.     

Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae)

Detection of crop stress is one of the major applications of hyperspectral remote sensing in agriculture. Many studies have demonstrated the capability of remote sensing techniques for detection of nutrient stress on cotton with only few on pest damage but none so far on leafhopper (LH) severity. Subsequent to introduction of Bt cotton, leafhopper is emerging as a key pest in several countries. In view of its wide host range, geographical distribution and damage potential, a study was initiated to characterise leafhopper stress on cotton, identify sensitive bands, and derive hyperspectral vegetation indices specific to this pest. Cotton plants with varying levels of LH severity were selected from three locations across major cotton growing regions of India. About 57-58 cotton plants from each location exhibiting different levels of LH damage symptoms were selected. Reflectance measurements in the spectral range of 350-2500 nm were made using hyperspectral radiometer. Simultaneously chlorophyll (Chl) and relative water content (RWC) were also estimated from the selected plants. Reflectance from healthy and leafhopper infested plants showed a significant difference in VIS and NIR regions. Decrease in Chl a pigment was more significant than Chl b in the infested plants and the ratio of Chl a/b showed a decreasing trend with increase in LH severity. Regression analysis revealed a significant linear relation between LH severity and Chl (R² = 0.505^∗∗), and a similar fit was also observed for RWC (R² = 0.402^∗∗). Plotting linear intensity curves between reflectance at each waveband with infestation grade resulted in six sensitive bands that exhibited maximum correlation at different regions of the electromagnetic spectrum (376, 496, 691, 761, 1124 and 1457 nm). Regression analysis of several ratio indices formulated with two or more of these sensitive bands led to the identification of new leaf hopper indices (LHI) with a potential to detect leafhopper severity. These new indices along with 20 other stress related hyperspectral indices compiled from literature were further tested for their ability to detect LH severity. Two novel indices LHI 2 and LHI 4 proposed in this study showed significantly high coefficients of determination across locations (R² range 0.521 to 0.825^∗∗) and hence have the potential use for detection of leafhopper severity in cotton.     

基于光谱特征变量的湿地典型植物生态类型识别方法——以北京野鸭湖湿地为例

光谱特征变量的选择对于湿地植被识别的精度和效率有着直接的影响作用.以华北地区典型的淡水湿地——野鸭湖湿地为研究区,采用Field Spec 3野外高光谱辐射仪,获取了野鸭湖典型湿地植物的冠层光谱.以野外高光谱数据为基础,首先利用一阶导数与包络线去除的方法,分析和对比不同植物生态类型的光谱特征,选定了用于识别植物生态类型的光谱特征变量,选定的8个光谱特征变量为红边位置WP_r、红边幅值Dr、绿峰位置WP_g、绿峰幅值Rg、510 nm附近的吸收深度DEP-510和吸收面积AREA-510、675 nm附近的吸收深度DEP-675和吸收面积AREA-675.其中,7种植物生态类型的一阶导数光谱特征差异较小,吸收特征差异性相对较大.除WP_r和WP _g外,沉水植物Rg和Dr平均值最低,湿生植物的Rg平均值最高,达到0.164,栽培植物的Dr平均值最高,达到0.012.7种植物生态类型在675 nm附近的DEP-675和AREA-675均高于510 nm附近的DEP-510与AREA-510,除去栽培植物,随着水分梯度的变化,其他6种植物生态类型的吸收深度和吸收面积都表现出先升高后降低的趋势.然后利用单因素方差分析(One-way ANOVA)验证了所选光谱特征变量的区分度,在P≤0.01的置信水平下,选取的8个光谱特征变量都能够较好的区分7种植物生态类型,区分度的最小值为13,最大值为18,并且吸收特征参数的区分度优于一阶导数参数.最后应用非线性的反向传播人工神经网络(BP-ANN)与线性判别分析(FLDA)的类型识别方法,利用选定的8个光谱特征变量进行湿地植物生态类型识别,取得了较好的识别精度,两种方法的总分类精度分别达到85.5％和87.98％.单因素方差分析(One-way ANOVA)和不同分类器的分类精度表明,所选的8个光谱特征变量具有一定的普适性和可靠性.     

Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat

Hyperspectral data sets contain useful information for characterizing vegetation canopies not previously available from multi-spectral data sources. However, to make full use of the information content one has to find ways for coping with the strong multi-collinearity in the data. The redundancy directly results from the fact that only a few variables effectively control the vegetation signature. This low dimensionality strongly contrasts with the often more than 100 spectral channels provided by modern spectroradiometers and through imaging spectroscopy. With this study we evaluated three different chemometric techniques specifically designed to deal with redundant (and small) data sets. In addition, a widely used 2-band vegetation index was chosen (NDVI) as a baseline approach. A multi-site and multi-date field campaign was conducted to acquire the necessary reference observations. On small subplots the total canopy chlorophyll content was measured and the corresponding canopy signature (450-2500 nm) was recorded (n_obs = 42). Using this data set we investigated the predictive power and noise sensitivity of stepwise multiple linear regression (SMLR) and two ‘full spectrum’ methods: principal component regression (PCR) and partial least squares regression (PLSR). The NDVI was fitted to the canopy chlorophyll content using an exponential relation. For all techniques, a jackknife approach was used to obtain cross-validated statistics. The PLSR clearly outperformed all other techniques. PLSR gave a cross-validated RMSE of 51 mg m⁻² for canopy chlorophyll contents ranging between 38 and 475 mg m⁻² (0.99 ≤ LAI ≤ 8.74 m² m⁻²). The lowest accuracy was achieved using PCR (RMSE_cv = 82 mg m⁻² and ). The NDVI, even using chlorophyll optimized band settings, could not reach the accuracy of PLSR. Regarding the sensitivity to artificially created (white) noise, PCR showed some advantages, whereas SMLR was the most sensitive chemometric technique. For relatively small, highly multi-collinear data sets the use of partial least square regression is recommended. PLSR makes full use of the rich spectral information while being relatively insensitive to sensor noise. PLSR provides a regression model where the entire spectral information is taken - in a weighted form - into account. This method seems therefore much better adapted to deal with potentially confounding factors compared to any 2-band vegetation index which can only avoid the most harmful factor of variation.     

Digital image analysis to estimate the live weight of broiler

Computer assisted digital image analysis was performed to investigate the possibility of estimating body weight of live broiler. To achieve the stated objective, 100 Arbor acres broiler chicks were reared under standard rearing condition and 1200 digital images were captured from 20 randomly selected broilers during the 7-42 days growing period. The captured images were analyzed by raster image analysis software (IDRISI 32) to determine the broiler body surface area and developed a linear equation to estimate weights of the broiler from its body surface-area pixels. The developed weight predicted equation based on surface-area pixels was log W = 1.060406(log P) + 0.173756(log A) − 2.029268 (W = estimated body weight, P = surface-area pixels and A = age at weighing) and the degree of goodness of fit of this equation was 0.999. The relative error in weight estimation of broiler chicken by image analysis, expressed in terms of percent error of the residuals from surface-area pixels was in between 0.04 and 16.47. On the other hand, the estimated body weights were not significantly (p > 0.05) difference from manually measured body weights up to 35 days of age. Thus, the development of a practical imaging system for weighing live broiler is feasible.     

Development of the Berry Impact Recording Device sensing system: Software

This paper reports a complete impact data acquisition, processing, and analyzing software system that applies on the hardware platform of the Berry Impact Recording Device (BIRD). The software has three major sections that correspond to the hardware: The BIRD sensor program, the interface box program, and the computer software i-BIRD. The sensor program samples acceleration data from three axes and records them as single impacts with a maximum sampling rate of 3.0 kHz. Users can configure the sensor via the i-BIRD computer software, with different options of sampling frequencies (682-3050 Hz) and thresholds (0-205 g, where g is the gravitational acceleration). The data recorded can be downloaded, processed and graphically displayed on the computer. A real time clock was created using the interrupt service routine provided by the microcontroller. The accuracy of the sensor’s clock was calibrated with an error of 0.073%, which was adequate to record impact data in this application. The shape of impact curves recorded by the BIRD sensor at three sampling frequencies (682, 998, and 1480 Hz) matched well with the curves recorded by a high frequency (10 kHz) data logger with the maximum root mean squared error of 4.4 g. The velocity change had a relative error less than 5%. With confirmation of all those performances, the software system enabled the BIRD to be a useful tool to collect impact data during small fruit (such as blueberry) mechanical harvest.     

Quantitative analysis of minerals and electric conductivity of red grape homogenates by near infrared reflectance spectroscopy

The use of near infrared (NIR) reflectance spectroscopy to measure the concentration of minerals and electric conductivity (EC) in red grape homogenates was investigated. Wine grape samples (n = 209) from two vintages, representing a wide range of varieties and regions were analysed by Inductively Coupled Plasma Optical Emission Spectrometry (ICPOES) for the concentrations of calcium (Ca), potassium (K), magnesium (Mg), phosphorus (P), sulphur (S), iron (Fe), and manganese (Mn) and scanned in reflectance in a NIR instrument (400-2500 nm). The spectra were pre-processed using multiple scatter correction (MSC) before developing the calibration models using partial least squares (PLS) regression and cross validation. Coefficients of determination in cross validation (R²) and the standard errors of cross validation (SECV) obtained were for Fe (0.60 and 1.49 mg kg⁻¹), Mn (0.71 and 0.41 mg kg⁻¹), Ca (0.75 and 60.89 mg kg⁻¹), Mg (0.84 and 12.93 mg kg⁻¹), K (0.78 and 285.34 mg kg⁻¹), P (0.70 and 40.19 mg kg⁻¹), S (0.88 and 14.45 mg kg⁻¹) and EC (0.87 and 7.66 mS). The results showed that Mg, S and EC in grape berries might be measured by NIR reflectance spectroscopy.     

黄瓜种传镰刀菌种类的鉴定及其致病性研究

为明确我国主栽黄瓜品种的种子中携带镰刀菌的种类及其危害,对来自我国黄瓜主产区21个黄瓜品种的种子进行种传镰刀菌检测,从种胚和种子外部分离得到镰刀菌分离物9个,采用形态学及分子生物学的方法进行鉴定,并研究其对黄瓜种子发芽和幼苗致病性的影响.结果表明:9个分离物中,4个分离物为尖孢镰刀菌(Fusarium oxysporum),4个分离物为串珠镰刀菌(F.moniliforme),1个分离物为再育镰刀菌(F.proli feratum).4个尖孢镰刀菌分离物对黄瓜种子发芽均有显著影响,发芽指数、活力指数、根长和鲜重等指标均显著降低,且能导致黄瓜幼苗出现典型的枯萎症状,经柯赫氏法则检验证明其具有致病性.其他分离物对黄瓜种子发芽也有一定的抑制作用,但没有致病性.     

Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis

Detecting plant health condition is an important step in controlling disease and insect stress in agricultural crops. In this study, we applied neural network and principal components analysis techniques for discriminating and classifying different fungal infection levels in rice (Oryza sativa L.) panicles. Four infection levels in rice panicles were used in the study: no infection condition, light and moderate infection caused by rice glume blight disease, and serious infection caused by rice false smut disease. Hyperspectral reflectance of rice panicles was measured through the wavelength range from 350 to 2500 nm with a portable spectroradiometer in the laboratory. The spectral response characteristics of rice panicles were analyzed, and principal component analysis (PCA) was performed to obtain the principal components (PCs) derived from different spectra processing methods, namely raw, inverse logarithmic, first, and second derivative reflectance. A learning vector quantization (LVQ) neural network classifier was employed to classify healthy, light, moderate, and serious infection levels. Classification accuracy was evaluated using overall accuracy and Kappa coefficient. The overall accuracies of LVQ with PCA derived from the raw, inverse logarithmic, first, and second derivative reflectance spectra for the validation dataset were 91.6%, 86.4%, 95.5%, and 100% respectively, and the corresponding Kappa coefficients were 0.887, 0.818, 0.939 and 1. Our results indicated that it is possible to discriminate different fungal infection levels of rice panicles under laboratory conditions using hyperspectral remote sensing data.