Colour based detection of volunteer potatoes as weeds in sugar beet fields using machine vision

The possible spread of late blight from volunteer potato plants requires the removal of these plants from arable fields. Because of high labour, energy, and chemical demands, a method of automatic detection and removal is needed. The development and comparison of two colour-based machine vision algorithms for in-field volunteer potato plant detection in two sugar beet fields are discussed. Evaluation of the results showed that both methods gave closely matched results within fields, although large differences exist between the fields. At plant level, in one field up to 97% of the volunteer potato plants were correctly classified. In another field, only 49% of the volunteer plants were correctly identified. The differences between the fields were higher than the differences between the methods used for plant classification.     

Adaptive detection of volunteer potato plants in sugar beet fields

Volunteer potato is an increasing problem in crop rotations where winter temperatures are often not cold enough to kill tubers leftover from harvest. Poor control, as a result of high labor demands, causes diseases like Phytophthora infestans to spread to neighboring fields. Therefore, automatic detection and removal of volunteer plants is required. In this research, an adaptive Bayesian classification method has been developed for classification of volunteer potato plants within a sugar beet crop. With use of ground truth images, the classification accuracy of the plants was determined. In the non-adaptive scheme, the classification accuracy was 84.6 and 34.9% for the constant and changing natural light conditions, respectively. In the adaptive scheme, the classification accuracy increased to 89.8 and 67.7% for the constant and changing natural light conditions, respectively. Crop row information was successfully used to train the adaptive classifier, without having to choose training data in advance.     

A neural network experiment on the site-specific simulation of potato tuber growth in Eastern Canada

The objective of this work was to optimize a neural network (NN) for modelling potato tuber growth and its in-field variations in eastern Canada. In addition to climatic inputs, the cumulative and maximal leaf area index (LAI) were incorporated to account for in-field scale variability. Soil and genetic parameters were assumed to be integrated in LAI as suggested by earlier work. Each input and combination of inputs was evaluated from the changes they induced in MAE (mean absolute error) and RMSE (root mean square error). Results using data from several replicated on-farm experiments between 2005 and 2008 suggest that a NN model using cumulative solar radiation, cumulative rainfall and cumulative LAI can adequately model site-specific tuber growth. The MAE of the retained model was 209 kg DM ha⁻¹, which represents less than 4% of the mean final tuber yield for the 3 years of the study. Non-linear effects of explicative variables on tuber yield were attested by comparing the results of the NN simulations to those of a multiple linear regression (MLR). The failure of MLR to simulate temporal discontinuities in tuber growth supports the use of a non-linear approach such as a NN to model tuber growth.     

Spatial patterns of wilting in sugar beet as an indicator for precision irrigation

Precision irrigation requires the mapping of within-field variations of water requirement. Conventional remote sensing techniques provide estimates of water status at only shallow soil depths. The ability of a water sensitive crop, sugar beet, to act as an intermediate sensor providing an integrated measure of water status throughout its rooting depth is tested here. Archive aerial photographs and satellite imagery of Eastern England show crop patterns resulting from past periglacial processes. The patterns were found to be spatially and temporally consistent. Field sampling of soil cores to 1 m depth established that the within-field wilting zones were significantly associated with coarser or shallow soils. The stress classes, determined by classification of the digitised images, were weakly correlated with total available water (Pearson correlation r = 0.588, P < 0.05). These results suggest that wilting in sugar beet can be used as an intermediate sensor for quantifying potential soil water availability within the root zone. Within-field stress maps generated in 1 year could be applied as a strategic tool allowing precision irrigation to be applied to high-value crops in following years, helping to make more sustainable use of water resources.     

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.     

超高压下Ca~(2+)与Na~+离子对甜菜果胶结构及流变性质的影响

[目的]研究超高压下不同浓度Ca2+与Na+对甜菜果胶结构及流变性质的影响,为甜菜果胶在食品中的应用提供理论依据。[方法]甜菜果胶用浓度0.05 mol·L-1的Tris-HCl溶液溶解,添加不同浓度Ca2+(2、12和20mmol·L-1)和Na+(0.05、0.1和0.6 mol·L-1),配制成1%(w/v)甜菜果胶溶液后进行超高压处理,然后分别对甜菜果胶分子量、微观结构、黏度和动态粘弹性进行测定。[结果]与常压下相比,在450 MPa条件下处理不同时间(10、20、30和50 min)后,甜菜果胶在1 550 cm-1处均出现新的吸收峰,甜菜果胶溶液的屈服应力σ0显著增加,但不同超高压处理时间之间无显著差异。添加不同浓度Ca2+或Na+的甜菜果胶在450 MPa条件下处理30 min,其结构及流变性的变化有所不同。相对于未添加Ca2+或Na+的甜菜果胶,添加2 mmol·L-1 Ca2+离子使甜菜果胶溶液屈服应力σ0、储能模量G’和损耗模量G"均明显增加,当Ca2+浓度增加到12 mmol·L-1和20 mmol·L-1时,果胶的流变性质变化不显著;添加2 mmol·L-1 Ca2+使甜菜果胶分子发生明显的交联。果胶分子量由只高压处理的2.25×105 Da显著增加到6.07×105 Da,Ca2+的添加浓度增加到20 mmol·L-1,果胶的分子量变为5.99×105 Da,与添加2mmol·L-1 Ca2+时没有显著差异,其流变性质变化亦不显著。相对于未添加Ca2+或Na+的甜菜果胶,添加0.05 mol·L-1 Na+也使甜菜果胶的屈服应力σ0显著增加,并且随着Na+浓度的持续增加,果胶的屈服应力σ0显著增加;而只有当Na+浓度增加到0.6 mol·L-1时,甜菜果胶储能模量G’和损耗模量G"才发生明显增加。添加0.1 mmol·L-1 Na+的甜菜果胶,其果胶分子链相互交联成网状,果胶分子发生明显聚集,果胶分子量显著增加到11.95×105 Da;而当Na+浓度增加到0.6 mol·L-1时,果胶链呈棒状结构,果胶分子量显著降低到5.53×105 Da。[结论]超高压下Ca2+与Na+可能与甜菜果胶分子结合使其结构发生改变,进而影响甜菜果胶的结构及流变性质。     

超高压处理对甜菜果胶结构及乳化特性的影响

【目的】明确超高压处理对甜菜果胶结构和乳化特性的影响,为甜菜果胶在食品工业中的应用提供理论依据。【方法】配制1%（w/v）甜菜果胶溶液,比较不同压力（0.1、250、350、450和550 MPa）、pH 7条件下处理30 min,450 MPa、pH 7条件下处理不同时间（10、20、30和50 min）,以及450 MPa、不同pH（pH 3、pH 7和pH 8）条件下处理30 min对甜菜果胶结构及乳化特性的影响。【结果】在不同压力条件下对pH 7的甜菜果胶溶液进行处理,随着压力升高,甜菜果胶分子量由5.58×10⁵ Da（0.1 MPa）降至1.56×10⁵ Da（550 MPa）;酯化度和乙酰化度分别由61.29%和18.17%（0.1 MPa）增至68.24%和21.72%（550 MPa）;红外图谱显示甜菜果胶在1 760-1 730 cm^-1和1 630-1 600 cm^-1处的峰均比未加压处理的更为明显,在1 560-1 540 cm^-1也出现一个明显的吸收峰;在250 MPa处理30 min后,甜菜果胶的乳化活性由209 m²·g^-1增至230 m²·g^-1,乳化稳定性由79 min增至97 min,乳化液粒径D_4,3降低,比表面积Sv升高。继续增加压力,果胶的乳化特性变化不显著。在450 MPa下对pH 7的甜菜果胶做不同时间处理,发现随加压时间延长,甜菜果胶分子量、酯化度、乙酰化度、乳化活性及稳定性均未发生显著变化。在450 MPa加压处理30 min后,pH 3、7和8条件下果胶分子量分别由原来的5.88、5.58和5.44×10⁵ Da降至2.38、2.25和2.49×10⁵ Da;pH 3和pH 7的甜菜果胶酯化度变化不明显,乙酰化度显著升高,分别由19.35%和18.17%增至24.84%和21.70%;而pH 8的甜菜果胶酯化度和乙酰化度显著降低,分别由70.13%和19.53%降至50.24%和16.41%;pH 3、pH 7和pH 8的甜菜果胶在加压处理后乳化活性和乳化稳定性均显著提高,超高压处理后pH 3和pH 7的甜菜果胶乳化活性较高,pH 3甜菜果胶乳化稳定性最好。【结论】超高压处理降低了甜菜果胶的分子量,使果胶中的蛋白暴露,改善了甜菜果胶的乳化特性。     

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%).     

甜菜幼苗低温和长日照诱导表达基因的克隆和序列分析

 以代表性差异分析cDNA-RDA(representational difference analysis of cDNA)方法获得在低温和长日照诱导甜菜(Beta vulgaris L)幼苗茎尖中差异表达的基因片段DP3，并进行了cDNA的末端快速扩增（rapid amplification of cDNA ends），经RT-PCR 扩增和基因组DNA中PCR扩增，克隆测序后获得1个全长609 bp的cDNA和855 bp的DNA序列，分别命名为Ty7Br600 和Ty7Br900，并在GenBank注册，登录号分别为AY324115和AY324114。在GenBank中比较未发现同源序列，可能为新基因。序列分析发现，Ty7Br600具有1个编码136个氨基酸的开放读码框（ORF）, Ty7Br900序列中存在1个内含子。分别以克隆的低温诱导甜菜幼苗cDNA为探针，分别进行Northern印记和Southern印记杂交。结果表明，cDNA差异片段只在低温诱导的甜菜中表达，在甜菜基因组中以2个拷贝或低拷贝形式存在。     

甜菜营养诊断和平衡施肥计算机管理系统的研制与应用

甜菜营养诊断平衡施肥系统,是用于指导糖用甜菜定量施肥的计算机系统。该系统在建立高产高糖甜菜植株养分动态模型的基础上,以甜菜一生中各生育期标准养分状况为尺子,对相应生育期的大田甜菜植株10种营养元素的丰缺情况进行诊断,进而自动开出针对性很强的、定性又定量的施肥方案。准确地执行此方案可使被诊断田块内的甜菜达到以下要求:第一,所含营养元素完全;第二,每一元素含量充足;第三,元素之间彼此保持平衡状态。实现高产高糖的目的。     

Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance

Automatic methods for an early detection of plant diseases are vital for precision crop protection. The main contribution of this paper is a procedure for the early detection and differentiation of sugar beet diseases based on Support Vector Machines and spectral vegetation indices. The aim was (I) to discriminate diseased from non-diseased sugar beet leaves, (II) to differentiate between the diseases Cercospora leaf spot, leaf rust and powdery mildew, and (III) to identify diseases even before specific symptoms became visible. Hyperspectral data were recorded from healthy leaves and leaves inoculated with the pathogens Cercospora beticola, Uromyces betae or Erysiphe betae causing Cercospora leaf spot, sugar beet rust and powdery mildew, respectively for a period of 21 days after inoculation. Nine spectral vegetation indices, related to physiological parameters were used as features for an automatic classification. Early differentiation between healthy and inoculated plants as well as among specific diseases can be achieved by a Support Vector Machine with a radial basis function as kernel.The discrimination between healthy sugar beet leaves and diseased leaves resulted in classification accuracies up to 97%. The multiple classification between healthy leaves and leaves with symptoms of the three diseases still achieved an accuracy higher than 86%. Furthermore the potential of presymptomatic detection of the plant diseases was demonstrated. Depending on the type and stage of disease the classification accuracy was between 65% and 90%.     

甘薯抗线虫病相关基因片段克隆及序列分析初步研究

依据甜菜抗线虫病基因(Hs1pro-1)序列设计引物片段,对甘薯高抗线虫病品种金山25的总DNA进行PCR扩增,获得1条600 bp左右的特异片段.序列测定及同源性检索表明,克隆序列与甜菜的Hs1pro-1基因具有一定的同源性.     

Spring Wheat Response to Fertilizer Nitrogen Following a Sugar Beet Crop Varying in Canopy Color

Experiments were conducted in the Red River Valley (RRV) of Minnesota to determine the responses of hard red spring wheat (Triticum aerstivum L.) to fertilizer N after a sugar beet (Beta vulgaris L.) crop that varied spatially in canopy color and N content. A color aerial photograph was acquired of the sugar beet field just prior to root harvest, and six sites were selected that varied in sugar beet canopy color, three each of green and yellow canopy sites. The three green sugar beet canopies returned 369, 265, and 266 kg N ha^–1 to the soil while the three yellow sugar beet canopies returned 124, 71, and 73 kg N ha^–1 to the soil. Spring wheat response to fall-applied urea-N fertilizer (0, 45, 90, 135, and 180 kg N ha^–1) was determined the following year at each of the above antecedent canopy sites. Soil NO₃-N in the top 0.6 m of soil varied among the locations with a range of 35 to 407 kg NO₃-N ha^–1 at the green canopy sites and 12 to 23 kg NO₃-N ha^–1 at the yellow canopy sites. Application of fertilizer N according to traditional recommendation methods would have resulted in fertilizer applications at all three yellow canopy sites and two of the three green canopy sites. At the antecedent green sugar beet canopy sites, fertilizer N had little or no effect on spring wheat grain yields, grain N concentration, anthesis dry matter, and anthesis N content. In contrast, fertilizer N increased all four parameters at the antecedent yellow sugar beet canopy sites. The data indicate that fertilizer N management can be improved by using remote sensing to delineate management zones according to antecedent sugar beet canopy color.     

Development of the Berry Impact Recording Device sensing system: Hardware design and calibration

Bruising caused by the impact damage occurs frequently during mechanical harvest process for highbush blueberries. The overall goal of this study was to develop a miniature and low-cost sensor prototype to quantitatively measure the impact forces endured by blueberries during the mechanical harvest process, which could be used to reduce blueberry bruising through improved harvester design. The sensing system developed in this study had three essential components: the sensor, the interface box, and the computer software program. The round circuit board of sensor is less than one inch (19.4 mm), including three accelerometers with ±500 g sensing range in each orthogonal axis, one eight-bit microcontroller, one 128 KB memory chip, and other electronic components with low power consumption. The sensor board and rechargeable battery were cast into a one inch (25.4 mm) sphere using silicone rubber. The interface box serves as the intermediate communication platform to connect the sensor and the computer. The PC-software retrieves data from the sensor via the I²C communication and downloads data to a computer for further analysis via the RS232 communication. The sensor was calibrated using a centrifuge. The accuracy of the sensor output was 0.53% (2.60 g maximum deviation) and −0.33% (−1.26 g maximum deviation), with precision error of 0.63% (3.21 g) in the output span. This miniature and low-cost sensor prototype provides the opportunity to understand how the berry (or other small fruits) interacts with different machine parts within the harvester and to identify critical control points that cause the most mechanical impacts, which was not achievable in the past.     

低温诱导甜菜(Beta vulgaris L.)抽薹相关基因的RACE分析

采用cDNA的末端快速扩增(Rapid amplificationn of cDNA ends)技术,以代表性差异分析cDNA-RDA(Representational difference analysis of cDNA)技术获得的低温诱导甜菜茎尖中差异表达的基因片段为模板,进行巢式PCR扩增,然后根据RACE拼接结果合成引物,分别以低温诱导的cDNA和DNA为模板进行PCR扩增,克隆测序后获得1个全长609 bp的cDNA和855 bp的DNA,分别命名为Ty7Br600和Ty7Br900,在GenBank注册,登录号分别为AY324115和AY324114。在GenBank库中的核苷酸序列比较未发现它们的同源序列,因此认为这个cDNA序列可能是甜菜的一个新基因。     

甜安宁在甜菜上的残留研究

1991，1992年在宁夏银川市郊对甜安宁在甜菜和土壤中的消解与残留进行了研究。结果表明：甜安宁在甜菜中的消解速度较快，半衰期为1．4d，而在土壤中消解速度较慢，半衰期为14．8d。最终残留测定表明：16％甜安宁乳油，按有效成分计，每公顷用量820～980g，在甜菜定苗后施药一次，至甜菜收获时其植株，根中均未检出甜安宁。（植株＜0．05mg·kg（-1），根＜0．01mg·kg（-1））。     

新疆不同甜菜种植区域生产成本比较效益分析

【目的】比较分析新疆北疆甜菜投入与产出以及单位面积总生产成本状况。【方法】以新疆伊犁河谷、塔额盆地、天山北坡三大甜菜种植主产区为研究区域,采用调查问卷及实施调研方法,运用分类比较法、比例分析法,比较分析新疆甜菜甜菜种植情况及经济效益。【结果】天山北坡与塔额盆地均为6.67~33.3 hm²,所占比重较高,分别为33.33%与42.86%;单产在4~5 t/667 m²的种植户最多,占43.28%,其次是5~6 t/667 m²的种植户,占调查总数的32.84%;4 t/667 m²以下种植户仅占调查总数的5.97%。塔额盆地承包费为329.29元/667 m²,显著低于其他两个甜菜种植区,而天山北坡地区承包费为550元/667 m²较所有调查户承包费高出83.77元/667 m²。在甜菜种植总成本构成中,承包费、劳动力费以及机械费均占较高比例,天山北坡、塔额盆地、伊犁河谷地区甜菜种植成本分别为1 716.07、1 429.76和1 736.97元/667m²。【结论】甜菜的生产成本和经济效益受不同种植区域的影响,新疆北疆甜菜成本收益率表现为塔额盆地>天山北坡>伊犁河谷,承包费、劳动力费、机械费与肥料费是构成最主要甜菜种植成本。     

Bruise detection on ‘Golden Delicious’ apples by vis/NIR spectroscopy

Visible and near-infrared reflectance spectra were used to distinguish the bruises from the intact surfaces on ‘Golden Delicious’ apples. Reflectance spectra of apples were acquired for both bruised and intact surfaces within the range of 400–1700 nm. The effective wavebands for detecting bruises were determined by analysis of the correlelogram. The wavebands around 545 and 1200 nm clearly show the time evolution of the bruised tissue. The mean-normalized reflectance difference between wavebands centred at 745 and 905 nm was also an effective discriminator for detecting old bruises. Afterwards, a quadratic discrimination analysis was performed based on the selected discriminators. The total classification error for the 1-day-old bruises was about 16.3%. A more extensive study was carried out to determine the effects of storage time on classification performance. The detection error was decreased with the elapse of storage time after bruising. However, it was difficult to remove the time influence on the classification accuracy if only depending on the intensity value of reflectance.     

矮壮素对甜菜糖分积累及产量形成的影响

【目的】 研究矮壮素喷施次数对甜菜糖分积累及产量形成的影响。筛选适宜新疆南疆喀什新糖区甜菜糖分积累及产量形成的矮壮素喷施次数。【方法】 以KWS-9147甜菜品种为材料,选用矮壮素水剂(50%),采用大田随机区组设计,设置4个处理,分别在不同时间喷施矮壮素0次(CK)、1次(D₁)、2次(D₂)、3次(D₃),研究喷施矮壮素次数对甜菜植株特性、糖分积累动态变化、糖分积累对气象因子的响应及产量形成的影响。【结果】 喷施矮壮素3次(D₃)、2次(D₂)处理与CK处理相比,使甜菜株高和枯叶数分别降低19.77%、11.24%,17.52%、17.44%;根长及根直径分别增长26.37%、19.90%,10.37%、5.93%;且使甜菜单根重和含糖率分别增加16.54%、13.38%,6.60%和5.95%,甜菜产量增产率及产糖量增产率分别达到12.69%、8.90%,20.17%和15.35%。D₂、D₃处理间差异不显著(P>0.05),其D₂处理糖分积累动态变化与当地积温及日均温动态变化吻合度最高,拟合值分别为0.985 4、0.898 6。【结论】 喷施矮壮素2次(D₂)处理有效促进南疆喀什新糖区甜菜含糖量和较高产糖量的形成。     

甜菜营养生长期糖分积累及块根膨大规律

【目的】研究甜菜营养生长期的糖分积累过程以及块根膨大形成规律,分析新疆滴灌甜菜营养生长生育期的变化规律,为甜菜高产优质生产生育调控提供理论依据。【方法】间苗后每隔5 d田间定点测定5株甜菜绿叶数、枯叶数、叶丛高度、块根直径和周长,以及整株起挖连续不缺苗5株测定叶丛鲜重、茎叶长度、块根鲜重与锤度等指标,每隔15 d测定叶丛和块根干物质,分析植株地上部分生长以及地下部分变化。【结果】6月上旬至7月下旬叶丛快速生长,根叶比<1,叶丛生长速度高于根体增长速度,含糖率受水分影响波动大;7月下旬至9月上旬,叶丛增长呈下降趋势,根体增长速度超过叶丛增长速度,根叶比>1;9月上旬至10月上中旬,甜菜叶丛大量枯死,块根膨大增长趋于平缓,含糖率增长迅速。【结论】6月上旬至7月下旬为叶丛形成期,生长中心位于叶丛;7月下旬至9月上旬为块根糖分增长期,生长中心位于块根;9月上旬至10月上中旬为糖分积累期。