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Dong Taifeng Shang Jiali Liu Jiangui Qian Budong Jing Qi Ma Baoluo Huffman Ted Geng Xiaoyuan Sow Abdoul Shi Yichao Canisius Francis Jiao Xianfeng Kovacs John M. Walters Dan Cable Jeff Wilson Jeff 《Precision Agriculture》2019,20(6):1231-1250
Precision Agriculture - Remote sensing has been recognized as a cost-effective way to detect the spatial and temporal variability of crop growth and productivity. In this study, multispectral... 相似文献
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高分一号GF1/WFV遥感影像具有较高的时间和空间分辨率,利用多时相影像开展农作物分类调查具有明显优势。以安徽省颍上县为研究区域,利用2017年5月至9月共6景多时相GF-1/WFV卫星遥感影像数据对主要农作物的分类识别提取。首先,通过分析研究区主要农作物的典型植被指数NDVI、EVI和WDRVI时序变化特征,明析了不同作物在各时相对不同VI的响应特征;其次,基于作物在不同时相的敏感VI变化响应,构建了决策树分层分类模型,成功提取了研究区玉米、水稻、大豆和甘薯四种主要作物种植空间分布情况。结果表明:总体精度达到90.9%,Kappa系数为0.895。同时,采用最大似然法、支持向量机对研究区作物进行分类,通过分类效果对比发现,最大似然法最差,支持向量机次之,决策树分类方法最佳。研究表明:利用多时相时间序列的遥感影像数据,结合作物植被指数特征,采用决策树分类方法可以有效提高作物分类的精度。 相似文献
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农作物空间格局遥感监测研究进展 总被引：73，自引：10，他引：63
遥感技术因其高时效、宽范围和低成本等优点正被广泛应用于对地观测活动中,为大区域尺度掌握农作物空间格局提供了新的科学技术手段。本文系统总结了近10年来国内外农作物空间格局遥感监测在理论、方法、实践应用等方面取得的新进展,指出了亟待解决的问题,并对今后的发展方向进行了展望。研究认为,农作物种植面积遥感监测主要根据遥感传感器记录的不同农作物光谱特征的差异,进行不同农作物种植面积的识别,方法主要包括:基于光谱特征、基于作物物候特征和基于多源数据的农作物遥感识别方法。遥感技术应用于农作物复种模式监测主要根据时间序列植被指数描述的作物季节活动过程,利用不同的拟合方法得到作物生长曲线,实现作物复种模式有效监测。农作物种植方式遥感监测是更高层次的遥感应用,主要利用时间序列遥感数据,根据作物植被指数的变化规律区分不同作物生育周期,判断不同复种模式下作物的种植顺序和方式。在未来相当长的一段时间内,建立农作物空间格局遥感监测的理论和技术体系、发展和改进遥感影像分类方法、优化时间序列遥感数据平滑技术和提高信息提取的自动化与流程化将是农作物空间格局遥感监测需要重点解决的几个关键问题。 相似文献
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中国玉米遥感估产区划研究 总被引：3，自引：0，他引：3
农作物遥感估产区划是大面积农作物遥感估产研究和实践的基础。根据农作物遥感估产技术的具体要求,结合农作物区划理论,重点考虑春玉米和夏玉米估产,提出了我国玉米遥感估产区划的原则和依据,针对玉米遥感估产中的各项工作,分别作出了全国玉米遥感估产最佳时相、信息源和土地利用结构分类方案,并在此基础上,设计区划指标,把全国分为13个估产区和28个估产亚区。 相似文献
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Bruno Basso Costanza Fiorentino Davide Cammarano Urs Schulthess 《Precision Agriculture》2016,17(2):168-182
Nitrogen (N) fertilizer application can lead to increased crop yields but its use efficiency remains generally low which can cause environmental problems related to nitrate leaching as well as nitrous oxide emissions to the atmosphere. The objectives of this study were to: (i) to demonstrate that properly identified variable rates of N fertilizer lead to higher use efficiency and (ii) to evaluate the capability of high spectral resolution satellite to detect within-field crop N response using vegetation indices. This study evaluated three N fertilizer rates (30, 70, and 90 kg N ha?1) and their response on durum wheat yield across the field. Fertilizer rates were identified through the adoption of the SALUS crop model, in addition to a spatial and temporal analysis of observed wheat grain yield maps. Hand-held and high spectral resolution satellite remote sensing data were collected before and after a spring side dress fertilizer application with FieldSpec, HandHeld Pro® and RapidEye?, respectively. Twenty-four vegetation indices were compared to evaluate yield performance. Stable zones within the field were defined by analyzing the spatial stability of crop yield of the previous 5 years (Basso et al. in Eur J Agron 51: 5, 2013). The canopy chlorophyll content index (CCCI) discriminated crop N response with an overall accuracy of 71 %, which allowed assessment of the efficiency of the second N application in a spatial context across each management zone. The CCCI derived from remotely sensed images acquired before and after N fertilization proved useful in understanding the spatial response of crops to N fertilization. Spectral data collected with a handheld radiometer on 100 grid points were used to validate spectral data from remote sensing images in the same locations and to verify the efficacy of the correction algorithms of the raw data. This procedure was presented to demonstrate the accuracy of the satellite data when compared to the handheld data. Variable rate N increased nitrogen use efficiency with differences that can have significant implication to the N2O emissions, nitrate leaching, and farmer’s profit. 相似文献
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An automated stand-alone in-field remote sensing system (SIRSS) for in-season crop monitoring 总被引：1，自引：0，他引：1
A stand-alone in field remote sensing system (SIRSS) with high spatial and temporal resolution was developed in this study. System control and image processing algorithms consisted of image acquisition control, camera parameter control, crop canopy reflectance calibration, image rectification, image background segmentation and vegetation indices map generation were developed and embedded in the SIRSS. The SIRSS is able to automatically capture multispectral images over a testing field at any predefined time points during the growing season and process captured images in real-time. This paper presents the SIRSS system design, image analysis procedures and determination of vegetation indices. In a validation experiment over an 8-plot corn field with three different nutrient treatments spanning the 2006 growing season, a total of 91 images were acquired and four different vegetation indices were derived from the images of each day. The largest differences of indices values among three treatments were indentified during the V6-V8 stages which implied this period could be the best time to detect variability caused by the nitrogen stress in the cornfield. The SIRSS has shown the potential of monitoring changes in vegetation status and condition. 相似文献
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Spatially discriminating Russian wheat aphid induced plant stress from other wheat stressing factors 总被引：1，自引：0，他引：1
Georges F. Backoulou Norman C. ElliottKristopher Giles Mpho PhoofoloVasile Catana Mustafa MirikJerry Michels 《Computers and Electronics in Agriculture》2011,78(2):123-129
The Russian wheat aphid (RWA) Diuraphis noxia (Mordvilko) is a major pest of winter wheat and barley in the United States. RWA induces stress to the wheat crop by damaging plant foliage, lowering the greenness of plants, and affecting productivity. The utilization of multispectral remote sensing is effective at detecting plant stress in agricultural crops. Stress to wheat plants detected in fields can be caused by several factors that can vary spatially in their presence and intensity across a field. Stress can result from factors such as nutrient deficiency, drought, diseases, and pests that can occur individually or collectively. The present study investigated the potential of using spatial pattern metrics derived from multispectral images in combination with topographic and edaphic variables to identify a set of variables to differentiate the stress induced by RWA from other stress causing factors. A discriminant function analysis was applied to 15 discriminating variables. A set of 13 variables were retained to develop a model to differentiate the three types of stress. Overall, 97 percent of patches of stress used to validate the model were correctly categorized. Stressed patches caused by RWA were 98 percent correctly classified, patches caused by drought were 94 percent correctly classified, and patches caused by agronomic conditions were 99 correctly classified. It is possible to discriminate stress induced by RWA from other stress causing factors in multispectral data when spatial attributes of the stress causing factors are incorporated in the analysis. 相似文献
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农作物遥感识别中的多源数据融合研究进展 总被引：10，自引：2，他引：8
农作物遥感识别是地理学和生态学研究的前沿和热点,多源数据在农作遥感识别中日益发挥重要作用。笔者从多源数据融合的角度,归纳了2000年后多源数据在农作物遥感识别中应用的总体概况,系统梳理并提炼了当前多源数据融合的主要融合技术和融合模式。围绕与多源数据融合和农作物遥感识别相关的关键词,在Google学术、ISI Web of Knowledge和中国知网中对2000-2014年间国内外发表的论文进行检索,并统计不同传感器的使用频率及结合方式。研究表明,以提高空间分辨率为目标的多源数据融合和以提高时间分辨率为目标的多源数据融合技术是当前的两种主要方式,可以在一定程度上实现时空尺度的扩展。前者的融合技术包括图像融合、正态模糊分布神经网络模型、成分替换、半经验数据模型融合及多分辨率小波分解等,可以提升遥感数据的空间分解力和清晰度,较好弱化混合像元产生的影响,但农作物光谱信息有一定程度的丢失或扭曲,农作物空间分布局部细节信息与纹理特征依然会缺失;后者的融合技术形式灵活多样,可分为同源数据联合扩展时序的时空优化技术和异源数据联合扩展时序的时空优化技术,其可以有效排除短时间段内农作物生育期交叉,但易受不同遥感数据源间光谱反射率或植被指数转换模型及光谱波段设置差异的影响。在融合模式方面,根据数据类型分为光学数据的融合、光学数据与微波数据的融合以及遥感与非遥感数据的融合,以实现卫星资源优势互补为宗旨,充分挖掘不同类型农作物在遥感数据上呈现的光谱、时间和空间特征差异信息。同样,农作物遥感识别研究中的多源遥感数据融合也存在诸多挑战,在未来一段时间内,完善不同传感器之间的合作、更深层次挖掘融合信息以及多尺度长时间序列的中高分辨率农作物空间分布数据集的需求是多源数据融合的农作物遥感识别研究的重点发展方向和亟待解决的问题。研究结果有助于更好地理解多源遥感数据融合的技术和模式,为摸清多源数据融合在农作物识别中总体进展提供支撑,同时也为其他多源数据融合研究提供借鉴。 相似文献
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Noa Schor Sigal Berman Aviv Dombrovsky Yigal Elad Timea Ignat Avital Bechar 《Precision Agriculture》2017,18(3):394-409
Automation of disease detection and monitoring can facilitate targeted and timely disease control, which can lead to increased yield, improved crop quality and reduction in the quantity of applied pesticides. Further advantages are reduced production costs, reduced exposure to pesticides for farm workers and inspectors and increased sustainability. Symptoms are unique for each disease and crop, and each plant may suffer from multiple threats. Thus, a dedicated integrated disease-detection system and algorithms are required. The development of such a robotic detection system for two major threats of bell pepper plants: powdery mildew (PM) and Tomato spotted wilt virus (TSWV), is presented. Detection algorithms were developed based on principal component analysis using RGB and multispectral NIR-R-G sensors. High accuracy was obtained for pixel classification as diseased or healthy, for both diseases, using RGB imagery (PM: 95%, TSWV: 90%). NIR-R-G multispectral imagery yielded low classification accuracy (PM: 80%, TSWV: 61%). Accordingly, the final sensing apparatus was composed of a RGB sensor and a single-laser-beam distance sensor. A relatively fast cycle time (average 26.7 s per plant) operation cycle for detection of the two diseases was developed and tested. The cycle time was mainly influenced by sub-tasks requiring motion of the manipulator. Among these tasks, the most demanding were the determination of the required detection position and orientation. The time for task completion may be reduced by increasing the robotic work volume and by improving the algorithm for determining position and orientation. 相似文献
11.
Sensing technologies for precision specialty crop production 总被引：6，自引：0，他引：6
W.S. Lee V. Alchanatis C. Yang M. Hirafuji D. Moshou C. Li 《Computers and Electronics in Agriculture》2010,74(1):2-33
With the advances in electronic and information technologies, various sensing systems have been developed for specialty crop production around the world. Accurate information concerning the spatial variability within fields is very important for precision farming of specialty crops. However, this variability is affected by a variety of factors, including crop yield, soil properties and nutrients, crop nutrients, crop canopy volume and biomass, water content, and pest conditions (disease, weeds, and insects). These factors can be measured using diverse types of sensors and instruments such as field-based electronic sensors, spectroradiometers, machine vision, airborne multispectral and hyperspectral remote sensing, satellite imagery, thermal imaging, RFID, and machine olfaction system, among others. Sensing techniques for crop biomass detection, weed detection, soil properties and nutrients are most advanced and can provide the data required for site specific management. On the other hand, sensing techniques for diseases detection and characterization, as well as crop water status, are based on more complex interaction between plant and sensor, making them more difficult to implement in the field scale and more complex to interpret. This paper presents a review of these sensing technologies and discusses how they are used for precision agriculture and crop management, especially for specialty crops. Some of the challenges and considerations on the use of these sensors and technologies for specialty crop production are also discussed. 相似文献
12.
P. Kumara R. Prasad D. K. Gupta A. K. Vishwakarma A. Choudhary 《Russian Agricultural Sciences》2017,43(6):461-465
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. 相似文献
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【目的】研究新疆北部沿天山主要种植区马铃薯病害种类以及病原菌的种类,采集奇台农场、尼勒克县实验站、巴里坤石人子村等地区的感病植株与块茎分离鉴定。【方法】采用组织分离法结合显微形态观察、柯赫氏法则、16S rDNA及ITS鉴定方法,分离鉴定引起马铃薯病害的病原菌。【结果】病原菌有假单胞菌(Pseudomonas lactis)、致病性尖孢镰刀菌(Fusarium oxysporum)、茄病镰刀菌(Fusarium solani)、链格孢菌(Alternaria sp.)、细级链格孢菌(Alternaria tenuissima)、赤星病菌(Alternaria alternata)、白地霉(Galactomyces candidum),分别引起青枯病、枯萎病、干腐病、早疫病、黑斑病、赤星病、白地霉干腐病,其中致病性尖孢镰刀菌(Fusarium oxysporum)致病性较强。【结论】新疆马铃薯主产区的病害有枯萎病、干腐病、早疫病,并且青枯病、白地霉干腐病、赤星病、黑斑病。 相似文献
14.
《农业科学学报》2017,(2)
Variation in phenological stage is the major nonlinearity in monitoring,modeling and various estimations of agricultural systems. Indices are used as a common means of evaluating agricultural monitoring data from remote sensing and terrestrial observation systems,and many of these indices have linear characteristics. The analysis of and relationships between indices are dependent on the type of plant,but they are also highly variable with respect to its phenological stage. For this reason,variations in the phenological stage affect the performance of spatiotemporal crop status monitoring. We hereby propose an adaptive event-triggered model for monitoring crop status based on remote sensing data and terrestrial observations. In the proposed model,the estimation of phenological stage is a part of predicting crop status,and spatially distributed remote sensing parameters and temporal terrestrial monitoring data are used together as inputs in a state space system model. The temporal data are segmented with respect to the phenological stage-oriented timing of the spatial data,so instead of a generalized discrete state space model,we used logical states combined with analog inputs and adaptive trigger functions,as in the case of a Mealy machine model. This provides the necessary nonlinearity for the state transitions. The results showed that observation parameters have considerably greater significance in crop status monitoring with respect to conventional agricultural data fusion techniques. 相似文献
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A. Elvanidi N. Katsoulas T. Bartzanas K. P. Ferentinos C. Kittas 《Precision Agriculture》2017,18(3):332-349
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. 相似文献
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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. 相似文献
18.
In light of the increasing demand for food production, climate change challenges for agriculture, and economic pressure, precision farming is an ever-growing market. The development and distribution of remote sensing applications is also growing. The availability of extensive spatial and temporal data—enhanced by satellite remote sensing and open-source policies—provides an attractive opportunity to collect, analyze and use agricultural data at the farm scale and beyond. The division of individual fields into zones of differing yield potential (management zones (MZ)) is the basis of most offline and map-overlay precision farming applications. In the process of delineation, manual labor is often required for the acquisition of suitable images and additional information on crop type. The authors therefore developed an automatic segmentation algorithm using multi-spectral satellite data, which is able to map stable crop growing patterns, reflecting areas of relative yield expectations within a field. The algorithm, using RapidEye data, is a quick and probably low-cost opportunity to divide agricultural fields into MZ, especially when yield data is insufficient or non-existent. With the increasing availability of satellite images, this method can address numerous users in agriculture and lower the threshold of implementing precision farming practices by providing a preliminary spatial field assessment. 相似文献
19.
棉花黄萎病冠层高光谱遥感监测技术研究 总被引：3，自引：2，他引：1
通过小区试验地病圃田和大田实验,不同时期对不同品种棉花黄萎病冠层光谱分别进行测定,定性和定量地分析了其反射光谱特征.结果表明:不同时期、不同品种棉花黄萎病冠层光谱与正常冠层之间有着显著的差异,冠层光谱均随严重度(SL)的增加表现出有规律的变化,可见光(620～700 nm)波段,光谱反射率随SL增加呈现上升趋势,近红外(700～1 300 nm)波段则表现出相反的趋势,在近红外(760～1 300 nm)尤为明显.黄萎病冠层光谱与SL相关后发现,806 nm附近SL(Y)与冠层光谱反射率(X)的相关性达到了极显著水平,二者之间的回归模型为:Y=-11.64 X+7.072 2(R2=0.675).研究为今后航空、航天遥感大面积监测棉花黄萎病提供了理论依据,为棉花其他病害遥感监测提供了借鉴和参考. 相似文献
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Late blight, caused by the fungal pathogen Phytophthora infestans, is a disease that quickly spreads in tomato fields under suitable weather conditions and can threaten the sustainability
of tomato farming in California, USA. This paper explores the applicability of remotely sensed images to detect disease spectral
anomalies for precision disease management. We used the indices approach and generated a 5-index image that we used to identify
the disease in tomato fields based on information from field-collected spectra and linear combinations of the spectral indices.
Field results indicated that we were able to identify five clusters in the image space with small overlaps of a few clusters.
Using the identified 5-cluster scheme to classify the tomato field images, we were able to successfully separate the diseased
tomatoes from the healthy ones before economic damage was caused. Hence, the method based on a 5-index image may significantly
enhance the capability of multispectral remote sensing for disease discrimination at the field level. 相似文献