Retrieval of rice crop growth variables using multi-temporal RISAT-1 remotely sensed data

Microwave remote sensing sensors have great potential due to their capability to operate in any weather condition for the wide range of agricultural applications. The rice crop variables such as leaf area index (LAI) and plant height (PH) were retrieved for the monitoring of crop growth to improve crop production. The interaction of rice crop variables with medium spatial resolution (25 m) Radar Imaging Satellite-1 (RISAT-1) data for Varanasi district, India, was examined. The multi-temporal dual polarization (HH- and HV-) images having frequency 5.35 GHz at C-band were investigated. Crop growth profile derived from the analysis of temporal backscattering (July–October, 2013) showed 3–4 dB difference throughout its growth cycle. The rice crop variables were retrieved by the inversion of polynomial models and showed higher values of coefficient of determination (R²) for HH-polarization in comparison to HV-polarization.     

激光雷达和摄像机联合标定识别作物

作物识别技术广泛应用于杂草精准对靶施药、果实采摘机器人、植物病虫害识别等方面.从机器视觉和激光探测两方面分析了国内外作物识别的研究现状,机器视觉识别作物主要是利用作物的颜色、纹理、形状、位置特征;激光探测识别作物利用激光的测距信息.分析了国内外融合激光雷达和摄像机信息识别作物的研究现状,总结了激光雷达和摄像机联合标定的方法,并指出其在作物识别中的重要性.     

我国主要农作物品种养分及质量控制样品的定值

随着农业科学技术的进步,我国农作物品种在近20年有了很大变化,目前,我们掌握和应用的农作物养分值依然是20年前农作物品种养分数据。了解并掌握当前我国主要农作物品种养分状况是必要的。受全国农业技术推广中心的委托,黑龙江省农产品质量检验检测中心在2001年承担农业部植株质量控制样品及我国7种主要农作物品种养分的定值。     

Wireless sensor network deployment for integrating video-surveillance and data-monitoring in precision agriculture over distributed crops

Monitoring different parameters of interest in a crop has been proven as a useful tool to improve agricultural production. Crop monitoring in precision agriculture may be achieved by a multiplicity of technologies; however the use of Wireless Sensor Networks (WSNs) results in low-cost and low-power consumption deployments, therefore becoming a dominant option. It is also well-known that crops are also negatively affected by intruders (human or animals) and by insufficient control of the production process. Video-surveillance is a solution to detect and identify intruders as well as to better take care of the production process. In this paper, a new platform called Integrated WSN Solution for Precision Agriculture is proposed. The only cost-effective technology employed is IEEE 802.15.4, and it efficiently integrates crop data acquisition, data transmission to the end-user and video-surveillance tasks. This platform has been evaluated for the particular scenario of scattered crops video-surveillance by using computer simulation and analysis. The telecommunications metrics of choice are energy consumed, probability of frame collision and end-to-end latency, which have been carefully studied to offer the most appropriate wireless network operation. Wireless node prototypes providing agriculture data monitoring, motion detection, camera sensor and long distance data transmission (in the order of several kilometers) are developed. The performance evaluation of this real tests-bed scenario demonstrates the feasibility of the platform designed and confirms the simulation and analytical results.     

社会经济因素对农户作物选择的影响机制——以黑龙江省宾县为例

【目的】探讨社会经济因素对农户农作物选择的影响机制,为优化农作物结构调整与制定政策提供依据。【方法】本研究对黑龙江省宾县384个农户进行随机调查,采用频率分析法,从生产要素投入、作物纯收益、作物出售情况、农业生产技术推广及农业政策等5个方面,探讨其对农户种地积极性以及农作物选择的影响。【结果】作物纯收益的提高、新品种与新技术的推广以及农业补贴与引导政策在较大程度上能够提高种地积极性。而在农户作物选择方面,主要受作物纯收益影响,其次为农业补贴与引导政策。【结论】为进一步促进东北地区未来的种植业发展,研究区应从增加基础设施投入、提高粮食作物收购价格、提供稳定收购渠道、加强农业科技投入力度和拓展服务内容等方面调动农户种地积极性、优化农作物结构,并提高农民收入水平。     

Inverse meta-modelling to estimate soil available water capacity at high spatial resolution across a farm

Geo-referenced information on crop production that is both spatially- and temporally-dense would be useful for management in precision agriculture (PA). Crop yield monitors provide spatially but not temporally dense information. Crop growth simulation modelling can provide temporal density, but traditionally fail on the spatial issue. The research described was motivated by the challenge of satisfying both the spatial and temporal data needs of PA. The methods presented depart from current crop modelling within PA by introducing meta-modelling in combination with inverse modelling to estimate site-specific soil properties. The soil properties are used to predict spatially- and temporally-dense crop yields. An inverse meta-model was derived from the agricultural production simulator (APSIM) using neural networks to estimate soil available water capacity (AWC) from available yield data. Maps of AWC with a resolution of 10 m were produced across a dryland grain farm in Australia. For certain years and fields, the estimates were useful for yield prediction with APSIM and multiple regression, whereas for others the results were disappointing. The estimates contain ‘implicit information’ about climate interactions with soil, crop and landscape that needs to be identified. Improvement of the meta-model with more AWC scenarios, more years of yield data, inclusion of additional variables and accounting for uncertainty are discussed. We concluded that it is worthwhile to pursue this approach as an efficient way of extracting soil physical information that exists within crop yield maps to create spatially- and temporally-dense datasets.     

Determination of differences in crop injury from aerial application of glyphosate using vegetation indices

Crop injury caused by off-target drift of herbicide can seriously reduce growth and yield and is of great concern to farmers and aerial applicators. Farmers can benefit from identifying an indirect method for assessing the level of crop injury. This study evaluates the combined use of statistical methods and vegetation indices (VIs) derived from multispectral images to assess the level of crop injury. An experiment was conducted in 2009 to determine glyphosate injury differences among the cotton, corn, and soybean crops. The crops were planted in eight rows spaced 102 cm apart and 80 m long with four replications. Seven VIs were calculated from multispectral images collected at 7 and 21 days after the glyphosate application (DAA). At each image collection date, visual injury estimates were assessed and data were collected for plant height, chlorophyll content, and shoot dry weight. From the seven VIs evaluated as surrogate for glyphosate injury identification using a canonical correlation analysis (CCA), the Chlorophyll Vegetation Index (CVI) showed the highest correlation with field-measured plant injury data. CVI image values were subtracted from the CVI average values of the non-injured area to generate CVI residual images (CVI_res). Frequency distribution histograms of CVI_res image values were calculated to assess the level of injury between crops. These data suggested that injury increased from 7DAA to 21DAA with corn exhibiting higher severity of injury than cotton or soybean, while only moderate injury was observed for cotton. The techniques evaluated in this study are promising for estimating the level of glyphosate herbicide drift, which can be used to make appropriate management decisions considering crop proximity.     

From statistics to grids: A two-level model to simulate crop pattern dynamics

Crop planting patterns are an important component of agricultural land systems. These patterns have been significantly changed due to the combined impacts of climatic changes and socioeconomic developments. However, the extent of these changes and their possible impacts on the environment, terrestrial landscapes and rural livelihoods are largely unknown due to the lack of spatially explicit datasets including crop planting patterns. To fill this gap, this study proposes a new method for spatializing statistical data to generate multitemporal crop planting pattern datasets. This method features a two-level model that combines a land-use simulation and a crop pattern simulation. The output of the first level is the spatial distribution of the cropland, which is then used as the input for the second level, which allocates crop censuses to individual gridded cells according to certain rules. The method was tested using data from 2000 to 2019 from Heilongjiang Province, China, and was validated using remote sensing images. The results show that this method has high accuracy for crop area spatialization. Spatial crop pattern datasets over a given time period can be important supplementary information for remote sensing and thus support a wide range of application in agricultural land systems.     

Selection of ancillary data to derive production management units in sweet corn (<Emphasis Type="Italic">Zea Mays</Emphasis> var. rugosa) using MANOVA and an information criterion

In production systems where high-resolution harvest data are unavailable there is often a reliance on ancillary information to generate potential management units. In these situations correct identification of relevant sources of data is important to minimize cost to the grower. For three fields in a sweet corn production system in central NSW, Australia, several sets of high-resolution data were obtained using soil and crop canopy sensors. Management units were derived by k-means classification for 2–5 classes using three approaches: (1) with soil data, (2) with crop data and (3) a combination of both soil and crop data. Crop quantity and quality were sampled manually, and the sample data were related to the different management units using multivariate analysis of variance (MANOVA). The corrected Akaike information criterion (AICc) was then used to rank the different sources of data and the different orders of management units. For irrigated, short-season sweet corn production the management units derived from the crop canopy sensor data explained more variation in key harvest variables than management units derived from an apparent soil electrical conductivity (EC_a) survey or a mixture of crop and soil sensor data. Management units derived from crop data recorded just prior to side-dressing outperformed management units derived from data recorded earlier in the season. However, multi-temporal classification of early and mid-season crop data gave better results than single layer classification at any time. For all three fields in this study, a 3- or 4-unit classification gave the best results according to the information criterion (AICc). For growers interested in adopting differential management in irrigated sweet corn, investment in a crop canopy sensor will provide more useful high-resolution information than that in a high-resolution EC_a survey.     

Crop height variability detection in a single field by multi-temporal terrestrial laser scanning

Information on crop height, crop growth and biomass distribution is important for crop management and environmental modelling. For the determination of these parameters, terrestrial laser scanning in combination with real-time kinematic GPS (RTK–GPS) measurements was conducted in a multi-temporal approach in two consecutive years within a single field. Therefore, a time-of-flight laser scanner was mounted on a tripod. For georeferencing of the point clouds, all eight to nine positions of the laser scanner and several reflective targets were measured by RTK–GPS. The surveys were carried out three to four times during the growing periods of 2008 (sugar-beet) and 2009 (mainly winter barley). Crop surface models were established for every survey date with a horizontal resolution of 1 m, which can be used to derive maps of plant height and plant growth. The detected crop heights were consistent with observations from panoramic images and manual measurements (R² = 0.53, RMSE = 0.1 m). Topographic and soil parameters were used for statistical analysis of the detected variability of crop height and significant correlations were found. Regression analysis (R² < 0.31) emphasized the uncertainty of basic relations between the selected parameters and crop height variability within one field. Likewise, these patterns compared with the normalized difference vegetation index (NDVI) derived from satellite imagery show only minor significant correlations (r < 0.44).     

Curved and straight crop row detection by accumulation of green pixels from images in maize fields

This study proposes a new method for detecting curved and straight crop rows in images captured in maize fields during the initial growth stages of crop and weed plants. The images were obtained under perspective projection with a camera installed onboard and conveniently arranged at the front of a tractor. The final goal was the identification of the crop rows which are crucial for precise autonomous guidance and site-specific treatments, including weed removal based on the identification of plants outside the crop rows. Image quality is affected by uncontrolled lighting conditions in outdoor agricultural environments and by gaps in the crop rows (due to lack of germination or defects during planting). Also, different plants heights and volumes occur due to different growth stages affecting the crop row detection process. The proposed method was designed with the required robustness to cope with the above undesirable situations and it consists of three sequentially linked phases: (i) image segmentation, (ii) identification of starting points and (iii) crop row detection. The main contribution is the ability of the method to detect curved crop rows as well as straights rows even with irregular inter-row spaces. The method performance has been tested in terms of accuracy and time processing.     

一种基于图像分析提取作物冠层生物学参数的方法与验证

利用图像分析方法,通过准确识别冠层和背景像素进行棉花冠层生物学产量和叶面积系数估测。采用Olympus C740 Ultra Zoom数码相机拍摄棉花不同生育期冠层图像,在棉花冠层数码照片特征分析的基础上提出了棉花冠层图片计算机自动判读的方法,即混合采用图像色度(H)、绿光(G)、红光(R)灰度值构造提取条件,通过多重判断识别棉花冠层和背景,并编写了相应的计算机程序。利用该程序分析棉花不同施氮量下、不同生育期提取地面覆盖度参数与棉花生物学产量、叶面积系数间的关系,发现棉花冠层地面覆盖度指标可以有效预测棉花生物学产量和叶面积系数,二者间指数相关系数达到r=0.97以上,为极显著相关。     

植株自旋转多视角重建技术在小麦植株三维表型获取中的应用评估

基于多视角重建技术的作物三维表型高通量获取系统成本低、获取效率高，引起越来越多的关注。植物自旋转式拍摄平台易于搭建，但植物旋转过程中产生的抖动对点云三维重建和表型解析精度有一定影响。为评估旋转式多视角成像在小麦植株三维表型解析中的适用性，基于植物旋转设计了便携式小麦植株三维表型高通量采集系统，选取穗期不同品种的小麦植株作为实验样本进行点云重建，基于Hausdorff距离评价了重建点云的精度误差；并基于人工测量数据，对所提取的表型指标精度进行评价。结果表明，植物旋转式重建的点云与相机旋转式重建的点云有较高的一致性，点云精度差距基本控制在0.4 cm以下；获取的叶长、叶宽和株高的均根方误差分别为0.79、0.13和0.53 cm，平均绝对百分比误差分别为3.26%、7.63%和0.74%，表明该方式适合穗期的小麦植株表型重建，具有较高的点云重建和表型提取精度，并为小麦植株表型评价提供了一种低成本的解决方案。     

中美农作物收入保险产品:比较与启示

农作物收入保险能够对因产量降低、价格下跌或产量与价格共同变化所引致的收入损失提供补偿,因而成为国际农业保险产品发展的方向。美国农作物收入保险运作已经相当成熟,对中国发展收入保险具有重要借鉴意义。采用比较研究法,分别介绍中美两国农业保险的组织架构,阐释两国农作物收入保险产品的具体设计和运作,并将两国农业保险产品置于巨灾保险(CAT)、买入保障水平(BUY-UP)和补充保障选项(SCO)的产品分类框架下进行比较分析。结果表明,中国农作物收入保险以准收入保险产品为主,尚存在产量和价格数据设置不合理、保障水平和保费补贴政策单一等不足。相比之下,美国农作物收入保险种类丰富,以累积多年的农作物产量数据和期货价格数据为基础,提供多档次可供选择的保障水平,保险单元按照个体和农场进行了区分,并设置了差异化的保费补贴政策。因此,从政策性农业保险要追随农业政策发展目标、提高农业保险组织效率和加强农业保险数据建设方面,对我国发展农作物收入保险给出重要启示。     

浅谈农作物秸秆综合开发利用技术措施

农作物秸秆是农业生产的主要副产品,也是工农业生产的重要资源。文章介绍了农作物秸秆综合利用概况,论述了当前农作物秸秆综合利用的技术方法,提出了农作物秸秆未来综合开发利用的基本途径。     

油菜作物模型研究进展

作物模型是农业模型的重要内容,主要分为作物生长模型和作物形态结构模型。从油菜生长模型和油菜形态结构模型及可视化两方面,较系统综述了油菜作物的发育、生长（光合、呼吸、分配等）、水分平衡、氮素平衡、器官建成及管理等方面的国内外研究进展,分析了油菜模型的研究现状及存在问题,并展望了未来油菜作物模型的发展前景。     

Application of artificial neural networks in yield prediction of potato crop

Forecasting of crop yield is helpful in food management and growth of a nation, which has specially agriculture based economy. In the last few decades, Artificial Neural Networks have been used successfully in different fields of agricultural remote sensing especially in crop type classification and crop area estimation. The present work employed two types of Artificial Neural Networks i.e., a Generalized Regression Neural Network (GRNN) and a Radial Basis Function Neural Network (RBFNN) to predict the yield of potato crops, which have been sown differently (flat and rough). Crop parameters like leaf area index, biomass and plant height were used as input data, while the yield of potato fields as output dataset to train and test the Neural Networks. Both GRNN and RBNN predicted potato crop yield accurately. However based on quick learning capability and lower spread constant (0.5), the GRNN was found a better predictor than RBFNN. Furthermore, the rough surface field was found more productive than flat field.     

中国作物栽培科学发展的回顾与展望

作物栽培是农业生产中最基本和最重要的组成部分,是一门综合性很强的直接服务于作物生 产的科学。简要回顾了中国作物栽培科学的发展历史,指出作物栽培在人类发展文明史中占有重要 地位,明确作物栽培科学的涵义及其理论体系,强调了新世纪下发展作物栽培科学研究的必要性,并 针对当前作物栽培科学研究所面临的问题提出相应对策。现代农业科学技术的飞速发展,必将为作 物栽培科学的发展开拓许多新领域,新世纪下作物栽培科学必将与时俱进,得到进一步发展。     

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.     

Real-time image processing for crop/weed discrimination in maize fields

This paper presents a computer vision system that successfully discriminates between weed patches and crop rows under uncontrolled lighting in real-time. The system consists of two independent subsystems, a fast image processing delivering results in real-time (Fast Image Processing, FIP), and a slower and more accurate processing (Robust Crop Row Detection, RCRD) that is used to correct the first subsystem’s mistakes. This combination produces a system that achieves very good results under a wide variety of conditions. Tested on several maize videos taken of different fields and during different years, the system successfully detects an average of 95% of weeds and 80% of crops under different illumination, soil humidity and weed/crop growth conditions. Moreover, the system has been shown to produce acceptable results even under very difficult conditions, such as in the presence of dramatic sowing errors or abrupt camera movements. The computer vision system has been developed for integration into a treatment system because the ideal setup for any weed sprayer system would include a tool that could provide information on the weeds and crops present at each point in real-time, while the tractor mounting the spraying bar is moving.