Geostatistical and fuzzy clustering approach for delineation of site-specific management zones and yield-limiting factors in irrigated hot arid environment of India

Precision Agriculture - Delineation of management zones (MZs) are needed to manage fields in order to maximize economic return, minimize environmental impact, and improve soil and crop management....     

Data fusion techniques for delineation of site-specific management zones in a field in UK

Fusion of different data layers, such as data from soil analysis and proximal soil sensing, is essential to improve assessment of spatial variation in soil and yield. On-line visible and near infrared (Vis–NIR) spectroscopy have been proved to provide high resolution information about spatial variability of key soil properties. Multivariate geostatistics tools were successfully implemented for the delineation of management zones (MZs) for precision application of crop inputs. This research was conducted in a 18 ha field to delineate MZs, using a multi-source data set, which consisted of eight laboratory measured soil variables (pH, available phosphorus (P), cation exchange capacity, total nitrogen (TN), total carbon (TC), exchangeable potassium (K), sand, silt) and four on-line collected Vis–NIR spectra-based predicted soil variables (pH, P, K and moisture content). The latter set of data was predicted using the partial least squares regression (PLSR) technique. The quality of the calibration models was evaluated by cross-validation. Multi-collocated cokriging was applied to the soil and spectral data set to produce thematic spatial maps, whereas multi-collocated factor cokriging was applied to delineate MZ. The Vis–NIR predicted K was chosen as the exhaustive variable, because it was the most correlated with the soil variables. A yield map of barley was interpolated by means of the inverse distance weighting method and was then classified into 3 iso-frequency classes (low, medium and high). To assess the productivity potential of the different zones of the field, spatial association between MZs and yield classes was calculated. Results showed that the prediction performance of PLSR calibration models for pH, P, MC and K were of excellent to moderate quality. The geostatistical model revealed good performance. The estimates of the first regionalised factor produced three MZs of equal size in the studied field. The loading coefficients for TC, pH and TN of the first factor were highest and positive. This means that the first factor can be assumed as a synthetic indicator of soil fertility. The overall spatial association between the yield classes and MZs was about 40 %, which reveals that more than 50 % of the yield variation can be attributed to more dynamic factors than soil parameters, such as agro-meteorological conditions, plant diseases and nutrition stresses. Nevertheless, multivariate geostatistics proved to be an effective approach for site-specific management of agricultural fields.     

Management zone delineation using a modified watershed algorithm

Site-specific management (SSM) is a common way to manage within-field variability. This concept divides fields into site-specific management zones (SSMZ) according to one or several soil or crop characteristics. This paper proposes an original methodology for SSMZ delineation which is able to manage different kinds of crop and/or soil images using a powerful segmentation tool: the watershed algorithm. This image analysis algorithm was adapted to the specific constraints of precision agriculture. The algorithm was tested on high-resolution bio-physical images of a set of fields in France.     

Spatio-temporal population dynamics and area-wide delineation of Bactrocera oleae monitoring zones using multi-variate geostatistics

Area-wide integrated pest management requires an understanding of insect population dynamics and definition of suitable techniques to quantify spatio-temporal variability to make better pest management decisions. However, the viability of area-wide integrated pest management has often been questioned because of the high monitoring costs. The present study aimed to: (i) analyse the spatial and temporal dynamics of the olive fruit fly over a large olive growing area (Ormylia, Greece), and (ii) define a methodology to determine monitoring zones to optimize the monitoring effort over space and time in area-wide integrated pest management programmes. Data from an olive fruit fly monitoring network based on McPhail traps were utilized. The multi-variate spatial (elevation) and temporal (6 periods) data of olive fruit fly population density were analysed by principal component analysis, co-kriging and factor kriging to produce thematic maps and to delineate monitoring zones. Olive fruit fly density was spatially correlated from 200 to 4?000?m. The spatial pattern changed over the monitoring season. Areas with high density of olive fruit flies shifted from high altitudes in summer to lower altitudes towards autumn. Three recommended levels of monitoring intensity were defined, thus delineating homogeneous monitoring zones for summer (July to September) and October. It was concluded that delineating monitoring zones through multi-variate geostatistics is a suitable approach for optimising the monitoring effort, because population density distribution is spatially structured over large areas and changes over time.     

Model-based optimal delineation of drip irrigation management zones

Precision Agriculture - Delineation of management zones for applying Variable Rate Irrigation (VRI) in drip irrigation presents unique challenges due to the directionality of the drip lines and the...     

A comparison of different algorithms for the delineation of management zones

One approach to the application of site-specific techniques and technologies in precision agriculture is to subdivide a field into a few contiguous homogenous zones, often referred to as management zones (MZs). Delineating MZs can be based on some sort of clustering, however there is no widely accepted method. The application of fuzzy set theory to clustering has enabled researchers to account better for the continuous variation in natural phenomena. Moreover, the methods based on non-parametric density estimation can detect clusters of unequal size and dispersion. The objectives of this paper were to: (1) compare different procedures for creating management zones and (2) determine the relation of the MZs delineated with potential yield. One hundred georeferenced point measurements of soil and crop properties were obtained from a 12 ha field cropped with durum wheat for two seasons. The trial was carried out at the experimental farm of CRA-CER in Foggia (Italy). All variables were interpolated on a 1 × 1 m grid using the geostatistical techniques of kriging and cokriging. The techniques compared to identify MZs were: (1) the ISODATA method, (2) the fuzzy c-means algorithm and (3) a non-parametric density algorithm. The ISODATA method, which was the simplest, subdivided the field into three distinct classes of suitable size for uniform management, whereas the other two methods created two classes. The non-parametric density algorithm characterized the edge properties between adjacent clusters more efficiently than the fuzzy method. The clusters from the non-parametric density algorithm and yield maps for three seasons (2005–2006, 2006–2007 and 2007–2008) were compared and agreement measures were computed. The kappa coefficients for the three seasons were negative or small positive values which indicate only slight agreement. These results illustrate the importance of temporal variation in spatial variation of yield in rainfed conditions, which limits the use of the MZ approach.     

Mapping management zones in a sandy pasture soil using an objective model and multivariate techniques

Soils occupied by dryland pastures usually have low fertility but can exhibit a high spatial variability. Consequently, logical application of fertilisers should be based on an appropriate knowledge of spatial variability of the main soil properties that can affect pasture yield and quality. Delineation of zones with similar soil fertility is necessary to implement site-specific management, reinforcing the interest of methods to identify these homogeneous zones. Thus, the formulation of the objective Rasch model constitutes a new approach in pasture fields. A case study was performed in a pasture field located in a montado (agrosilvopastoral) ecosystem. Measurements of some soil properties (texture, organic matter, nitrogen, phosphorus, potassium, cation exchange capacity and soil apparent electrical conductivity) at 24 sampling locations were integrated in the Rasch model. A classification of all sampling locations according to pasture soil fertility was established. Moreover, the influence of each soil property on the soil fertility was highlighted, with the clay content the most influential property in this sandy soil. Then, a clustering process was undertaken to delimit the homogeneous zones, considering soil pasture fertility, elevation and slope as the input layers. Three zones were delineated and vegetation indices (normalized difference vegetation index, NDVI, and normalized difference water index, NDWI) and pasture yield data at sampling locations were employed to check their differences. Results showed that vegetation indices were not suitable to detect the spatial variability between zones. However, differences in pasture yield and quality were evident, besides some key soil properties, such as clay content and organic matter.

     

The delineation of agricultural management zones with high resolution remotely sensed data

Remote sensing (RS) techniques have been widely considered to be a promising source of information for land management decisions. The objective of this study was to develop and compare different methods of delineating management zones (MZs) in a field of winter wheat. Soil and yield samples were collected, and five main crop nutrients were analyzed: total nitrogen (TN), nitrate nitrogen (NN), available phosphorus (AP), extractable potassium (EP) and organic matter (OM). At the wheat heading stage, a scene of Quickbird imagery was acquired and processed, and the optimized soil-adjusted vegetation index (OSAVI) was determined. A fuzzy k-means clustering algorithm was used to define MZs, along with fuzzy performance index (FPI), and modified partition entropy (MPE) for determining the optimal number of clusters. The results showed that the optimal number of MZs for the present study area was three. The MZs were delineated in three ways; based on soil and yield data, crop RS information and the combination of soil, yield and RS information. The evaluation of each set of MZs showed that the three methods of delineating zones can all decrease the variance of the crop nutrients, wheat spectral parameters and yield within the different zones. Considering the consistent relationship between the crop nutrients, wheat yield and the wheat spectral parameters, satellite remote sensing shows promise as a tool for assessing the variation in soil properties and yield in arable fields. The results of this study suggest that management zone delineation using RS data was reliable and feasible.     

Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

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.     

基于模糊聚类的泰宁各乡镇旅游资源评价

对泰宁县11个乡镇旅游资源进行模糊聚类.当λ=0.031时,分类效果特别显著.结果表明:新桥乡、下渠乡、开善乡及龙安乡聚为Ⅰ区;上青乡、龙湖镇、大田乡和大布乡聚为Ⅱ区;朱口乡为Ⅲ区;杉城镇为Ⅳ区;梅口乡为Ⅴ区.基于模糊聚类结果用模糊综合评价法对旅游资源进行分区评价.结果表明:(1)Ⅳ-Ⅴ区的总体评价结果具有明显优势,旅游资源质量高,区位、交通条件、经济条件和心理条件比其它乡镇更成熟,但清洁性、生物多样性和生态旅游环境承载力指标得分低于Ⅰ-Ⅲ区;(2)Ⅱ、Ⅴ区的资源影响力、保护工程和保健性指标优于Ⅰ、Ⅲ-Ⅳ区;(3)Ⅲ、Ⅴ区的和谐性和生态环境指标优于Ⅰ-Ⅱ、Ⅳ区.     

Delineating site-specific management zones on pasture soil using a probabilistic and objective model and geostatistical techniques

Precision Agriculture - In recent years, different algorithms have been utilised to delineate management zones, areas with similar properties, within agricultural fields. However, there are few...     

A comparison between spatial clustering models for determining N-fertilization management zones in orchards

Precision Agriculture - Site-specific agricultural management (SSM) relies on identifying within-field spatial variability and is used for variable rate input of resources. Precision agricultural...     

应用模糊聚类分析研究菜蚜种群动态

在分别描述了杭州郊区秋末冬初季节小白菜（Ｂｒａｓｓｉｃａｃａｍｐｅｓｔｒｉｓｓｓｐ．ｃｈｉｎｅｎｓｉｓ．）上菜蚜种群的数量动态和空间动态的基础上，应用模糊聚类分析法研究了菜蚜种群的数量和空间的整体动态。结果表明，桃蚜、萝卜蚜及其混合种群的数量动态均呈指数或Ｌｏｇｉｓｔｉｃ曲线变化，它们的空间格局呈聚集分布；而且聚集强度始终从高到低呈持续下降。运用模糊聚类法，可将其种群的整体动态分成４个时期，依次为：苗期（或移栽后的返青期）的迁入期，成株初期的增殖扩散期，成株后生长盛期的繁殖高峰期，外围叶片明显枯黄时的数量饱和期。作者据此对各个时期的菜蚜种群特征进行了具体分析。     

基于改进模糊C均值聚类算法的草坪杂草识别

【目的】为了实现草坪杂草管理的精准化施药,针对自然环境中杂草与草坪颜色相近导致杂草难以分割的问题,提出一种改进模糊C均值(Fuzzy C-means, FCM)聚类的分割算法。【方法】利用超绿算子提取感兴趣区域,融合HSV空间的多通道信息进行图像预处理,扩大杂草与草坪的特征差异。使用区域面积约束滤波范围,去除预处理图像中的草坪背景噪声,降低中值滤波造成的目标区域灰度级损失。提出一种各向灰度分布差异(Difference of gray distribution, DGD)检测算子,在聚类过程中引入像素周围不同方向的灰度分布差异特征实现草坪杂草分割。【结果】与传统FCM、FCM-S2、FCMNLS以及RSFCM算法相比,本文算法对大多数噪声区域抑制效果较好,可以实现较为理想的杂草分割效果。本文算法能有效分割草坪杂草,平均分割准确率达到91.45%,比FCM、FCM-S2、FCMNLS和RSFCM算法分别提高16.35%、4.12%、6.80%和8.06%。【结论】本文算法可有效地分割自然环境中的草坪杂草,为草坪杂草精准化施药提供了条件,具有实际应用价值。     

盐渍土分类的模糊聚类法探讨

【目的】寻求更为准确的盐渍土分类方法。【方法】对模糊聚类法的原理进行了介绍,并结合西北某机场重要建筑物的地质特点,利用模糊聚类法对该地区10个检测点的地基土进行了分类。【结果】西北某机场10个检测点的地基土可分为4类,即(1,2,3,4,8,9)区域属于亚硫酸盐渍土;(5,6)区域属于亚氯盐渍土;(10)区域属于碳酸盐渍土;而(7)区域属于非盐渍土。【结论】盐渍土分类的模糊聚类法较传统分类法简单、准确可靠,尤其是能具体判断出某点或某区域是否为盐渍土,对盐渍土的评判和地基处理有较大帮助。     

基于模糊c-均值聚类的SVC迭代训练算法

针对支撑向量机（Support vector machine,SVM）在大规模数据的问题,提出了一种基于模糊c-均值聚类样本选择策略的SVC（SVM for classification）迭代训练算法,从样本抽取、迭代训练两个方面进行了改进,并在多个较大规模UCI标准测试集上进行了试验.结果表明,所提出的迭代训练算法收敛快,在保证学习精度的同时使训练速度加倍、支撑向量减少一半.     

茶树花粉形态及其模糊聚类的研究

用光学显微镜和扫描电子显微镜对31个茶树品种(系)的花粉形态进行研究,并以14个花粉形态性状进行模糊聚类分析,结果表明:茶树花粉粒为中粒或大粒,多为长球形、三孔沟,内孔大、横长,赤道面观多为网状或拟网状纹饰,网眼和网脊大小、形状有一定差异,但品种(系)间花粉形态的变异程度较小.31个茶树品种(系)经聚类被分成六个组,基本上以原产地相近的品种(系)聚在一起,从相似性系数的大小可见茶树品种(系)间的亲缘关系之密切程度.     

夏季苗圃的生产管理

本文从七个方面介绍了如何进行夏季苗圃的生产管理。     

Site-specific management zones based on the Rasch model and geostatistical techniques

Delineation of management zones (MZ), i.e. areas within the field which represent subfield regions of similar production potential, is the first stage to implement site-specific management. During the last years different algorithms have been proposed to define MZ, with different results. In this work, the use of an objective method, the formulation of the Rasch model, which synthesizes data with different units into a uniform analytical framework, is considered to get representative measures of soil fertility potential which could be used to delimit MZ.To illustrate the method, a case study was conducted in a experimental field using five soil properties: clay, sand and silt content, and deep (ECd) and shallow (ECs) soil apparent electrical conductivity (approximately 0-90 and 0-30 cm depths, respectively). Two main results were obtained after applying this method: (1) a classification of all locations according to the soil fertility potential, which was the value of the Rasch measure and (2) the influence on the soil fertility of each individual soil property, being ECs the most influential and silt content the less influential property.Later, from the measures of soil fertility potential at sampled points, estimates were carried out using the ordinary kriging technique. Consequently, kriged estimates were utilized to map soil fertility potential and MZ were delimited using an equal-size classification method, which practically coincided with the MZ determined by a unsupervised classification.It is also shown the possibility of using probability maps to delimit MZ or provide information for hazard assessment of soil fertility in a field.