Digital infrared thermography for monitoring canopy health of wheat

Computer-aided diagnosis and prognosis models have been used for management decisions in crop protection. Initial infection rate, temperature and leaf wetness are important parameters in disease epidemiology and for decision support models. So far, in-field variability and variability between fields have not been taken into account for management decisions in disease control. This study aimed at testing the use of an imaging IR thermography system as a tool for monitoring the microclimatic conditions promoting incidence and severity of diseases within wheat fields with a high spatial resolution. Experiments were conducted on the detection and differentiation of leaf wetness on a single leaf scale and a crop canopy scale (1 m²) under controlled conditions. Field studies focused on comparing ground-based and air-borne thermographic data and linking these to ground-truth data.     

An autonomous intelligent gateway infrastructure for in-field processing in precision viticulture

Wireless sensor networks have found multiple applications in precision viticulture. Despite the steady progress in sensing devices and wireless technologies, some of the crucial items needed to improve the usability and scalability of the networks, such as gateway infrastructures and in-field processing, have been comparatively neglected. This paper describes the hardware, communication capabilities and software architecture of an intelligent autonomous gateway, designed to provide the necessary middleware between locally deployed sensor networks and a remote location within the whole-farm concept. This solar-powered infrastructure, denoted by iPAGAT (Intelligent Precision Agriculture Gateway), runs an aggregation engine that fills a local database with environmental data gathered by a locally deployed ZigBee wireless sensor network. Aggregated data are then retrieved by external queries over the built-in data integration system. In addition, embedded communication capabilities, including Bluetooth, IEEE 802.11 and GPRS, allow local and remote users to access both gateway and remote data, as well as the Internet, and run site-specific management tools using authenticated smartphones. Field experiments provide convincing evidence that iPAGAT represents an important step forward in the development of distributed service-oriented information systems for precision viticulture applications.     

传感器在精准农业中的应用

加快我国精准农业发展速度,拓宽其普及应用范围,发展高效、稳定、价格低廉的传感器至关重要。介绍 了生物传感器、电子舌、电子鼻、机器视觉等新型传感器的基本原理及其在农业领域的应用现状,并探讨了在农副产 品生产、加工、储运和流通的各个环节对传感器的不同需求,同时对农情监控传感器的发展方向进行展望。     

Optimal placement of proximal sensors for precision irrigation in tree crops

Precision Agriculture - Soil water or plant water status-based precision irrigation has the potential to improve water productivity. In this study, the question of number as well as placement of...     

Evaluation of two crop canopy sensors for nitrogen variability determination in irrigated maize

Advances in precision agriculture technology have led to the development of ground-based active remote sensors that can determine normalized difference vegetation index (NDVI). Studies have shown that NDVI is highly related to leaf nitrogen (N) content in maize (Zea mays L.). Remotely sensed NDVI can provide valuable information regarding in-field N variability and significant relationships between sensor NDVI and maize grain yield have been reported. While numerous studies have been conducted using active sensors, none have focused on the comparative effectiveness of these sensors in maize under semi-arid irrigated field conditions. Therefore, the objectives of this study were (1) to determine the performance of two active remote sensors by determining each sensor’s NDVI relationship with maize N status and grain yield as driven by different N rates in a semi-arid irrigated environment and, (2) to determine if inclusion of ancillary soil or plant data (soil NO₃ concentration, leaf N concentration, SPAD chlorophyll and plant height) would affect these relationships. Results indicated that NDVI readings from both sensors had high r ² values with applied N rate and grain yield at the V12 and V14 maize growth stages. However, no single or multiple regression using soil or plant variables substantially increased the r ² over using NDVI alone. Overall, both sensors performed well in the determination of N variability in irrigated maize at the V12 and V14 growth stages and either sensor could be an important tool to aid precision N management.     

Comparison of passive and active canopy sensors for the estimation of vine biomass production

Recent advances in optical designs and electronic circuits have allowed the transition from passive to active proximal sensors. Instead of relying on the reflectance of natural sunlight, the active sensors measure the reflectance of modulated light from the crop and so they can operate under all lighting conditions. This study compared the potential of active and passive canopy sensors for predicting biomass production in 25–32 randomly selected positions of a Merlot vineyard. Both sensors provided estimates of the normalized difference vegetation index (NDVI) from a nadir view of the canopy at veraison that were good predictors of pruning weight. Although the red NDVI of the passive sensors explained more of the variation in biomass (R ² = 0.82), its relationship to pruning weight was nonlinear and was best described by a quadratic regression (NDVI = 0.55 + 0.50 wt−0.21 wt²). The theoretically greater linearity of the amber NDVI-biomass relationship could not be verified under conditions of high biomass. The linear correlation to stable isotope content in leaves (¹³C and ¹⁵N) provided evidence that canopy reflectance detected plant stresses as a result of water shortage and limited fertilizer N uptake. Thus, the canopy reflectance data provided by these mobile sensors can be used to improve site-specific management practices of vineyards.     

Comparison between vegetation indices for detecting spatial and temporal variabilities in soybean crop using canopy sensors

Precision Agriculture - Crop monitoring through remote sensing techniques enable greater knowledge of average variability in crop growth. Canopy sensors help provide information on the variability...     

确定监测区域建立森林郁闭度估测方程最优样地的研究

在对监测区域样地进行合理分类的基础上,需抽取一定数量能代表监测区域森林资源分布状况的最优样地,根据所抽样地对应影响郁闭度估测的主要遥感和GIS因子,可建立以样地为单位的郁闭度估测方程. 最优样地是指能代表监测区域森林种类及分布状况的样地,它包含样地的数量及样地的代表性两个方面的含义. 如何抽取最优样地属多目标优化问题. 该文在理论描述特定监测区域最优样地数学模型的基础上,通过实例进行了系统研究与分析,有效解决了最优样地的抽样问题,所得结果可用于指导生产实践.      

基于Zigbee的播种质量监控系统的设计

为了满足智能化农业的需求,应用无线传感网络技术和PLC控制器技术,设计了一个基于Zigbee的播种质量无线监控系统。该系统采用光电传感器实现对排种状态的检测,采集的信息通过无线传输模块发送到PLC;利用霍尔传感器检测拖拉机行进速度,步进电机驱动排种轴,以实现播种状况的实时检测、控制及排种速度与拖拉机速度的同步。试验表明：该系统具有高可靠性、高精度的特点,在高速通信的同时有效地实现了播种信息的实时监控。     

Evaluating the navigation performance of multi-information integration based on low-end inertial sensors for precision agriculture

The main objective of this research was to evaluate the navigation performance of multi-information integration based on a low-end inertial measurement unit (IMU) in precision agriculture by utilizing different auxiliary information (i.e., GNSS real-time kinematic (RTK), non-holonomic constraints (NHC) and dual antenna GNSS). A series of experiments with different operation scenes (e.g., open sky in wet and dry soils) were carried out for quantitative analysis. For the position drift error during a 20-s GNSS outage, the dual-antenna GNSS-assisted approach did not provide a reduction, and the NHC reduced the maximum error in the lateral and vertical directions by over 80% in the dry soil test, but only by approximately 30% in the wet soil test. The heading error with continuous GNSS assistance can be less than 0.03° and be reduced by more than 90% with the aid of dual-antenna GNSS. Additionally, the NHC reduced the heading error from 0.54° to 0.21° and from 0.34° to 0.25° in the dry and wet soil tests respectively. The results suggested that the multi-information integration improved the positioning and orientation reliability. Moreover, the lateral positioning accuracy required for the control of agriculture autonomous vehicles was achieved at approximately 3.0 mm with over a 60% accuracy improvement brought by the dual-antenna GNSS assistance. In contrast to the vulnerability of a single system, multi-information integration can provide comprehensive navigation information with higher reliability and lower costs. Hence, multi-information fusion will be a great opportunity for agriculture to meet the high-accuracy and high-reliability requirements of precision agriculture.

     

次郎柿冠层受光特性分析

运用ACCUPAR LP-80冠层分析仪对果园两种密度下次郎柿冠层光学特性进行测定,结果表明,树冠不同高度和方位在一天内的光合有效辐射呈单峰曲线变化,且各个部位和方位的光合有效辐射在一天中的最大值和出现的时间差别很大,而同一密度下柿树的各个部位和方位光合有效辐射在一天4个时间段的显著变化情况也是不一致的。天顶角﹑叶态分布参数在同一地区基本没有变化,对有效辐射值基本没有影响,而果园密度﹑行向对有效辐射值影响比较大。     

Inversion of a canopy reflectance model using hyperspectral imagery for monitoring wheat growth and estimating yield

Applications of hyperspectral remote sensing data to derive relevant properties for precision agriculture are described. Green leaf area index, fraction of senescent material and grain yield are retrieved from the hyperspectral data. Two sensors were used to obtain these data; the airborne visible/infrared imaging spectrometer AVIS and the space-borne compact high-resolution imaging spectrometer CHRIS; they show the applicability of the methods to different spatial scales. In addition, the bi-directional observation capability of the CHRIS sensor is used to derive information about the average leaf angle of the canopies which are used to link canopy structure with phenological development. Derivation of the canopy properties, green leaf area index and fraction of senescent material was done with the radiative transfer model, SLC (soil–leaf–canopy). The results were used as input into the crop growth model PROMET-V to calculate grain yield. Two years of data from the German research project preagro are presented.     

Developing a new Crop Circle active canopy sensor-based precision nitrogen management strategy for winter wheat in North China Plain

Active canopy sensor (ACS)—based precision nitrogen (N) management (PNM) is a promising strategy to improve crop N use efficiency (NUE). The GreenSeeker (GS) sensor with two fixed bands has been applied to improve winter wheat (Triticum aestivum L.) N management in North China Plain (NCP). The Crop Circle (CC) ACS-470 active sensor is user configurable with three wavebands. The objective of this study was to develop a CC ACS-470 sensor-based PNM strategy for winter wheat in NCP and compare it with GS sensor-based N management strategy, soil N_min test-based in-season N management strategy and conventional farmer’s practice. Four site-years of field N rate experiments were conducted from 2009 to 2013 to identify optimum CC vegetation indices for estimating early season winter wheat plant N uptake (PNU) and grain yield in Quzhou Experiment Station of China Agricultural University located in Hebei province of NCP. Another nine on-farm experiments were conducted at three different villages in Quzhou County in 2012/2013 to evaluate the performance of the developed N management strategy. The results indicated that the CC ACS-470 sensor could significantly improve estimation of early season PNU (R² = 0.78) and grain yield (R² = 0.62) of winter wheat over GS sensor (R² = 0.60 and 0.33, respectively). All three in-season N management strategies achieved similar grain yield as compared with farmer’s practice. The three PNM strategies all significantly reduced N application rates and increased N partial factor productivity (PFP) by an average of 61–67 %. It is concluded that the CC sensor can improve estimation of early season winter wheat PNU and grain yield as compared to the GS sensor, but the PNM strategies based on these two sensors perform equally well for improving winter wheat NUE in NCP. More studies are needed to further develop and evaluate these active sensor-based PNM strategies under more diverse on-farm conditions.     

基于LBPHSV+ResNet50融合的水稻冠层氮素营养监测方法

为采用数码相机拍摄的水稻冠层图像来估测作物的氮素含量。以自然环境下获得的水稻冠层图像为研究对象,提出一种基于图像纹理色彩特征(LBPHSV)和ResNet50网络融合算法的氮素含量预测方法。LBPHSV+ResNet50融合算法是通过运用LBP算子和HSV颜色空间矩阵提取图像特征参数,将提取到的融合特征集作为ResNet50模型输入以加强对作物氮素营养的表征,并将预测结果与常用的多元线性回归、随机森林(RF)、支持向量回归模型、多层感知机、卷积神经网络、长短记忆网络(LSTM)及组合模型预测结果进行对比分析。结果显示:相比于浅层机器学习模型,深度学习算法能显著提高预测模型的准确率;LBPHSV+ResNet50融合模型的预测能力和泛化能力达到最优,R²和 RMSE分别为 0.97、0.02。相比于RF、LBP+LSTM、ResNet50,新模型的R²分别提升了16.36%、9.72%、16.55%和1.13%,RMSE 分别下降了 0.35、0.46、0.05和 0.002。因此,LBPHSV+ResNet50融合模型在预测水稻氮素含量时可提供令人满意的性能,能够满足对水稻氮素营养无损精准监测的农业需求。     

Influence of time of day on measurement with chlorophyll meters and canopy reflectance sensors of different crop N status

Precision Agriculture - Optical sensors are a promising approach for assessing nitrogen (N) status of vegetable crops. However, their potential may be undermined if time of day influences...     

Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds

LiDAR sensors are widely used in many areas and, in recent years, that includes agricultural tasks. In this work, a self-developed mobile terrestrial laser scanner based on a 2D light detection and ranging (LiDAR) sensor was used to scan an intensive olive orchard, and different algorithms were developed to estimate canopy volume. Canopy volume estimations derived from LiDAR sensor readings were compared to conventional estimations used in fruticulture/horticulture research and the results prove that they are equivalent with coefficients of correlation ranging from r = 0.56 to r = 0.82 depending on the algorithms used. Additionally, tools related to analysis of point cloud data from the LiDAR-based system are proposed to extract further geometrical and structural information from tree row crop canopies to be offered to farmers and technical advisors as digital raster maps. Having high spatial resolution information on canopy geometry (i.e., height, width and volume) and on canopy structure (i.e., light penetrability, leafiness and porosity) may result in better orchard management decisions. Easily obtainable, reliable information on canopy geometry and structure may favour the development of decision support systems either for irrigation, fertilization or canopy management, as well as for variable rate application of agricultural inputs in the framework of precision fruticulture/horticulture.     

Estimation of soil classes and their relationship to grapevine vigor in a Bordeaux vineyard: advancing the practical joint use of electromagnetic induction (EMI) and NDVI datasets for precision viticulture

Precision Agriculture - Working within a vineyard in the Pessac Léognan Appellation of Bordeaux, France, this study documents the potential of using simple statistical methods with...