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.     

Early detection of Fusarium infection in wheat using hyper-spectral imaging

Infections of wheat, rye, oat and barley by Fusarium ssp. are serious problems worldwide due to the mycotoxins, potentially produced by the fungi. In 2005, limit values were issued by the EU commission to avoid health risks by mycotoxins, both for humans and animals. This increased the need to develop tools for early detection of infections. Occurrence of Fusarium-caused head blight disease can be detected by spectral analysis (400-1000 nm) before harvest. With this information, farmers could recognize Fusarium contaminations. They could, therefore, harvest the grains separately and supply it to other utilizations, if applicable. In the present study, wheat plants were analyzed using a hyper-spectral imaging system under laboratory conditions. Principal component analysis (PCA) was applied to differentiate spectra of diseased and healthy ear tissues in the wavelength ranges of 500-533 nm, 560-675 nm, 682-733 nm and 927-931 nm, respectively. Head blight could be successfully recognized during the development stages (BBCH-stages) 71-85. However, the best time for disease determination was at the beginning of medium milk stage (BBCH 75). Just after start of flowering (BBCH 65) and, again, in the fully ripe stage (BBCH 89), distinction by spectral analysis is impossible. With the imaging analysis method ‘Spectral Angle Mapper’ (SAM) the degree of disease was correctly classified (87%) considering an error of visual rating of 10%. However, SAM is time-consuming. It involves both the analysis of all spectral bands and the setup of reference spectra for classification. The application of specific spectral sub-ranges is a very promising alternative. The derived head blight index (HBI), which uses spectral differences in the ranges of 665-675 nm and 550-560 nm, can be a suitable outdoor classification method for the recognition of head blight. In these experiments, mean hit rates were 67% during the whole study period (BBCH 65-89). However, if only the optimal classification time is considered, the accuracy of detection can be largely increased.     

Weed Detection Using Canopy Reflection

For site-specific application of herbicides, automatic detection and evaluation of weeds is desirable. Since reflectance of crop, weeds and soil differs in the visual and near infrared wavelengths, there is potential for using reflection measurements at different wavelengths to distinguish between them. Reflectance spectra of crop and weed canopies were used to evaluate the possibilities of weed detection with reflection measurements in laboratory circumstances. Sugarbeet and maize and 7 weed species were included in the measurements. Classification into crop and weeds was possible in laboratory tests, using a limited number of wavelength band ratios. Crop and weed spectra could be separated with more than 97% correct classification. Field measurements of crop and weed reflection were conducted for testing spectral weed detection. Canopy reflection was measured with a line spectrograph in the wavelength range from 480 to 820 nm (visual to near infrared) with ambient light. The discriminant model uses a limited number of narrow wavelength bands. Over 90% of crop and weed spectra can be identified correctly, when the discriminant model is specific to the prevailing light conditions.     

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.     

Crop water status assessment in controlled environment using crop reflectance and temperature measurements

Crop water status is an important parameter for plant growth and yield performance in greenhouses. Thus, early detection of water stress is essential for efficient crop management. The dynamic response of plants to changes of their environment is called ‘speaking plant’ and multisensory platforms for remote sensing measurements offer the possibility to monitor in real-time the crop health status without affecting the crop and environmental conditions. Therefore, aim of this work was to use crop reflectance and temperature measurements acquired remotely for crop water status assessment. Two different irrigation treatments were imposed in tomato plants grown in slabs filed with perlite, namely tomato plants under no irrigation for a certain period; and well-watered plants. The plants were grown in a controlled growth chamber and measurements were carried out during August and September of 2014. Crop reflectance measurements were carried out by two types of sensors: (i) a multispectral camera measuring the radiation reflected in three spectral bands centred between 590–680, 690–830 and 830–1000 nm regions, and (ii) a spectroradiometer measuring the leaf reflected radiation from 350 to 2500 nm. Based on the above measurements several crop indices were calculated. The results showed that crop reflectance increased due to water deficit with the detected reflectance increase being significant about 8 h following irrigation withholding. The results of a first derivative analysis on the reflectance data showed that the spectral regions centred at 490–510, 530–560, 660–670 and 730–760 nm could be used for crop status monitoring. In addition, the results of the present study point out that sphotochemical reflectance index, modified red simple ratio index and modified ratio normalized difference vegetation index could be used as an indicator of plant water stress, since their values were correlated well with the substrate water content and the crop water stress index; the last being extensively used for crop water status assessment in greenhouses and open field. Thus, it could be concluded that reflectance and crop temperature measurements might be combined to provide alarm signals when crop water status reaches critical levels for optimal plant growth.     

Improved vegetation segmentation with ground shadow removal using an HDR camera

A vision-based weed control robot for agricultural field application requires robust vegetation segmentation. The output of vegetation segmentation is the fundamental element in the subsequent process of weed and crop discrimination as well as weed control. There are two challenging issues for robust vegetation segmentation under agricultural field conditions: (1) to overcome strongly varying natural illumination; (2) to avoid the influence of shadows under direct sunlight conditions. A way to resolve the issue of varying natural illumination is to use high dynamic range (HDR) camera technology. HDR cameras, however, do not resolve the shadow issue. In many cases, shadows tend to be classified during the segmentation as part of the foreground, i.e., vegetation regions. This study proposes an algorithm for ground shadow detection and removal, which is based on color space conversion and a multilevel threshold, and assesses the advantage of using this algorithm in vegetation segmentation under natural illumination conditions in an agricultural field. Applying shadow removal improved the performance of vegetation segmentation with an average improvement of 20, 4.4, and 13.5% in precision, specificity and modified accuracy, respectively. The average processing time for vegetation segmentation with shadow removal was 0.46 s, which is acceptable for real-time application (<1 s required). The proposed ground shadow detection and removal method enhances the performance of vegetation segmentation under natural illumination conditions in the field and is feasible for real-time field applications.     

基于无人机低空遥感的水稻田间杂草分布图研究

目的 获取水稻田的低空遥感图像并分析得到杂草分布图,为田间杂草精准施药提供参考。方法 使用支持向量机(SVM)、K最近邻算法(KNN)和AdaBoost 3种机器学习算法,对经过颜色特征提取和主成分分析(PCA)降维后的无人机拍摄的水稻田杂草可见光图像进行分类比较;引入一种无需提取特征和降维、可自动获取图像特征的卷积神经网络(CNN),对水稻田杂草图像进行分类以提升分类精度。结果 SVM、KNN和AdaBoost对测试集的测试运行时间分别为0.500 4、2.209 2和0.411 1 s,分类精度分别达到89.75%、85.58%和90.25%,CNN对图像的分类精度达到92.41%,高于上述3种机器学习算法的分类精度。机器学习算法及CNN均能有效识别水稻和杂草,获取杂草的分布信息,生成水稻田间的杂草分布图。结论 CNN对水稻田杂草的分类精度最高,生成的水稻田杂草分布图效果最好。     

黄河上游尖扎地区最高·最低气温及日较差变化

利用1960~2007年尖扎气温资料,对尖扎年、季平均气温、平均最高、最低气温及日较差变化趋势进行了分析。结果表明,近48年尖扎年平均气温与全省基本一致,呈现出显著的上升趋势,气候倾向率为0.24℃/10a,其中冬季气温变暖最明显,秋季次之;平均最高气温较最低气温升幅大,这种特点在秋季和夏季最为明显;日较差也呈上升趋势,尤其是秋季最为显著。最高气温的升高是年平均日较差变化呈增加趋势最主要的原因,即年平均日较差变大是以最高温度增加幅度大于最低温度变暖为特征。     

哈尔滨秋季8种灌木叶片色彩属性与气温变化相关性的研究

[目的]运用秋季灌木叶片色彩属性变化规律,从定性、定量的视角构建哈尔滨秋季植物景观的色彩,总结地域性植物色彩设计的科学性。[方法]以能够兼容CAD和Photoshop绘图软件的NCS色卡,在温侯法界定的哈尔滨秋季时段,随秋季时序采集8种灌木的NCS色彩值,确定其色彩属性与变化规律。通过采集哈尔滨秋季气温日温的最低、最高值,分析其叶片NCS色彩值与气候温度的日温差、最低最高温度变化的规律。[结果]8种灌木NCS叶色值总体范围为NCS S 5540-G40Y~NCS S 1580-Y90R,分布于24个标准颜色中。最低气温9℃,日温差11℃,叶片开始变色3种;最低气温9℃,日温差15℃,叶片开始变色3种;最低气温7℃,日温差10℃,叶片开始变色1种;霜后,最低气温5℃,日温差13℃,叶片开始变色1种。最低气温7℃持续2d,连续日温差8~13℃,进入变色盛期7种;最低气温3℃,日温差13℃,进入变色盛期1种。[结论]利用秋季灌木叶片色彩属性变化规律构建哈尔滨秋季植物景观色彩是提升秋季季相色彩设计质量的重要途径,具有一定的科学性。     

外来入侵杂草黄顶菊的发生特性与综合控制技术

黄顶菊是中国近年来新记录的一种外来入侵恶性杂草,入侵后迅速蔓延并造成大面积危害.综述近年来对黄顶菊的分类学地位、生长、光合及遗传特性、防控措施及其分布等方面的研究成果.黄顶菊具有喜光喜湿、耐盐碱贫瘠、生长繁殖迅速、结实量大等特点,环境适应性极强,其种子易于随气流传播和混在农产品中人为传播扩散.预期黄顶菊在未来10～20年内将可能扩散到更大范围危害,对该杂草应加强研究,创制出经济安全而有效的持续控制技术措施.     

Fuzzy controller decreases tomato cracking in greenhouses

Sunlight heats the greenhouse air temperature during the day and can encourage tomato cracking and decrease marketable product. A fuzzy controller was designed to control greenhouse climate to reduce tomato cracking using as variables solar radiation, substrate temperature and canopy temperature. A movable shade screen reduced incoming radiation during warm and sunny conditions; meanwhile irrigation was controlled according to canopy and substrate temperature. The shade screen was opened or closed with a gear motor driven by a photovoltaic system. The motor controlled by a pulse width modulated inverter started softly decreasing its starting current. The fuzzy system injected additional water and nutrients between 12:00 and 15:00 h; irrigation cycles were removed during very cloudy days. Tomato cracking decreased from 52% to 17% using the fuzzy controller and canopy temperature never exceeded 30 °C.     

西宁市区番茄温棚的气象效应研究简

[目的]研究西宁市区番茄温棚的气象效应。[方法]利用2012年4月～2013年3月对温棚德福番茄开花一果实采摘发育期的观测资料和同期温棚内外的气温、地温、湿度的观测资料,对青海省西宁市区番茄温棚的气象条件效应进行分析。[结果]西宁市区番茄温棚气象效应显著,气温、地温、湿度日变化效应依次为1、11、4月,7月最小,季节依次为冬季、秋季、春季、夏季,比温棚外冬季气温提高13．9～25．9℃、湿度提高13％～40％、地温提高16．1～22．8℃,夏季气温提高4．9～7．6℃、湿度提高O％～t1％、地温提高1．8—8．8℃;年变化比温棚外气温提高2．O～21．4℃、地温提高5．1～34．3℃、湿度提高2％～54％。[结论]该研究为西宁市区特色设施农业的大面积推广发展提供科学依据。     

不同天气条件下温室番茄栽培环境因子的变化特征研究

[目的]研究南疆干旱气候区春季沙尘频发期,不同天气条件下温室内番茄栽培环境因子变化特征,为合理调控戈壁温室内环境因子,指导温室番茄生产提供依据.[方法]采用无线远程环境监控系统监测典型的晴天、多云和沙尘天气温室内温度、湿度、光照、土壤温度的日变化,对温室内环境因子变化特征进行分析.[结果]温度和光照强度日变化在晴天呈明显的“双波峰型”曲线,多云、沙尘天气则呈“单波峰型”,三种天气条件下气温、土壤温度变化均能达到番茄生长发育的适宜温度范围.晴天、多云天气光照强度完全达到番茄生长发育光强的需求,沙尘天气对温室内光强影响较大,日最高光强为29.3 Klx.晴天天气湿度早晚下降的幅度远比沙尘、多云天气小,其变化范围在26.2;～61.1;,沙尘天气湿度下降相对滞后.[结论]南疆早春茬沙尘频发期气温、土壤温度变化均能满足番茄生长发育的适宜温度.沙尘天气通风应使降湿与保温互相兼顾,及时清除棚膜上的尘土,改善温室内光照条件.晴天及时通风降温降湿.     

Robust pixel-based classification of obstacles for robotic harvesting of sweet-pepper

Sweet-pepper plant parts should be distinguished to construct an obstacle map to plan collision-free motion for a harvesting manipulator. Objectives were to segment vegetation from the background; to segment non-vegetation objects; to construct a classifier robust to variation among scenes; and to classify vegetation primarily into soft (top of a leaf, bottom of leaf and petiole) and hard obstacles (stem and fruit) and secondarily into five plant parts: stem, top of a leaf, bottom of a leaf, fruit and petiole. A multi-spectral system with artificial lighting was developed to mitigate disturbances caused by natural lighting conditions. The background was successfully segmented from vegetation using a threshold in a near-infrared wavelength (>900 nm). Non-vegetation objects occurring in the scene, including drippers, pots, sticks, construction elements and support wires, were removed using a threshold in the blue wavelength (447 nm). Vegetation was classified, using a Classification and Regression Trees (CART) classifier trained with 46 pixel-based features. The Normalized Difference Index features were the strongest as selected by a Sequential Floating Forward Selection algorithm. A new robust-and-balanced accuracy performance measure P_Rob was introduced for CART pruning and feature selection. Use of P_Rob rendered the classifier more robust to variation among scenes because standard deviation among scenes reduced 59% for hard obstacles and 43% for soft obstacles compared with balanced accuracy. Two approaches were derived to classify vegetation: Approach A was based on hard vs. soft obstacle classification and Approach B was based on separability of classes. Approach A (P_Rob = 58.9) performed slightly better than Approach B (P_Rob = 56.1). For Approach A, mean true-positive detection rate (standard deviation) among scenes was 59.2 (7.1)% for hard obstacles, 91.5 (4.0)% for soft obstacles, 40.0 (12.4)% for stems, 78.7 (16.0)% for top of a leaf, 68.5 (11.4)% for bottom of a leaf, 54.5 (9.9)% for fruit and 49.5 (13.6)% for petiole. These results are insufficient to construct an accurate obstacle map and suggestions for improvements are described. Nevertheless, this is the first study that reports quantitative performance for classification of several plant parts under varying lighting conditions.     

Software to quantify and map vegetative cover in fallow fields for weed management decisions

Mapping weed cover during the fallow period of dryland crop rotations would be valuable for weed management in subsequent crops and could be done with low cost color digital cameras, however most managers lack the specialized software and expertise needed to create a map from the images. A system of software was developed to quantify weed cover in fallow fields in digital images and to simplify and automate the most challenging tasks that non-GIS professionals confront in creating and using maps derived from a large number of images. A GIS file of image locations is created with inexpensive consumer software. Images are classified, a GIS file is generated and the map is displayed in a simple GIS viewer with free software we developed. A map can be generated from 1000 images and 5000 GPS coordinates in 30 min, including image classification. The classified and original images for all locations can be viewed together easily from the map application. The accuracy of estimating weed cover was evaluated using images collected in 15 fields under natural light with a consumer grade camera mounted on an ATV driving 8-11 km h⁻¹. Weed cover was estimated with 96% accuracy for images, regardless of the amount of crop residue, unless part of the image was shaded by the camera. In those images, accuracy was 90% or better. This system will work with many professional and consumer digital cameras and GPS units and the classification algorithm can be easily modified for other applications.     

亚低温胁迫下补光对番茄幼苗生长及光合性能的影响

【目的】研究亚低温逆境胁迫下,补光对番茄幼苗生长发育及光合性能等生理指标的影响,分析亚低温胁迫下番茄幼苗对补光的生理响应,为亚低温胁迫下培育番茄壮苗提供理论依据和技术支撑。【方法】以番茄Money maker为材料,利用改良Hoagland营养液定期定量灌溉,采用白∶蓝为2∶1的复合光于亚低温胁迫条件下(昼15℃/8夜℃)对番茄叶片补光,共设置4个处理:正常温度处理(CK)、正常温度补光处理(CK+USL)、亚低温胁迫处理(LT)和亚低温胁迫补光处理(LT+USL)。【结果】与亚低温胁迫相比,亚低温补光后,番茄植株株高、茎粗分别显著增加24.92%和4.58%,叶、茎、根鲜重及总鲜重分别显著增加了24.71%、23.68%、31.01%和25.23%;番茄幼苗总根长、根系表面积、根系体积及投影面积分别显著增加了25.73%、29.31%、25.93%和14.22%;叶片净光合速率(Pn)、气孔导度(Gs)及蒸腾速率(Tr)分别增加85.77%、19.09%和44.59%,而胞间二氧化碳浓度(Ci)降低15.60%。【结论】亚低温胁迫条件下,叶背面补光可以显著改善番茄幼苗的生长及光合性能,促进番茄幼苗的健壮生长。     

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.     

分光光度法对马铃薯中酪氨酸酶活性的测定

[目的]研究酪氨酸酶的活性,为防止马铃薯及其他舍酪氨酸酶的水果和蔬菜等发生褐变提供参考。[方法]以马铃薯为原料,利用分光光度法测定酪氨酸酶的活性;为确定提取酪氨酸酶的适宜条件,测定不同温度和不同pH值对酶活性影响。[结果]结果显示,多巴溶液的吸收光谱最大吸收峰λmax为480nm,随着时间的增大吸光度变化趋于稳定。以此建立动力学曲线,由曲线的斜率可计算出酪氨酸酶的活性,并由试验得出酪氨酸酶的活性受温度和溶液的pH值影响较大。因此,提取和测定酪氨酸酶的活性时的适宜条件为：可见波长λmax为480nm,温度控制在30℃,pH值6．5。[结论]为保障酪氨酸酶的活性,在提取和测定时应选择适宜的条件。     

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%).