Hierarchical artificial immune system for crop stage classification

This paper presents a new hierarchical clustering algorithm for crop stage classification using hyperspectral satellite image. Amongst the multiple benefits and uses of remote sensing, one of the important application is to solve the problem of crop stage classification. Modern commercial imaging satellites, owing to their large volume of satellite imagery, offer greater opportunities for automated image analysis. Hence, we propose a unsupervised algorithm namely Hierarchical Artificial Immune System (HAIS) of two steps: splitting the cluster centers and merging them. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The classification results have been compared with K-means and Artificial Immune System algorithms. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is accurate.

Crop stage classification of hyperspectral data using unsupervised techniques

The presence of a large number of spectral bands in the hyperspectral images increases the capability to distinguish between various physical structures. However, they suffer from the high dimensionality of the data. Hence, the processing of hyperspectral images is applied in two stages: dimensionality reduction and unsupervised classification techniques. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The selected dimensions are classified using Niche Hierarchical Artificial Immune System (NHAIS). The NHAIS combines the splitting method to search for the optimal cluster centers using niching procedure and the merging method is used to group the data points based on majority voting. Results are presented for two hyperspectral images namely EO-1 Hyperion image and Indian pines image. A performance comparison of this proposed hierarchical clustering algorithm with the earlier three unsupervised algorithms is presented. From the results obtained, we deduce that the NHAIS is efficient.

Gene expression programming-fuzzy logic method for crop type classification

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.

Improved modeling of groundwater recharge in agricultural watersheds using a combination of crop model and remote sensing

For improved water management and efficiency of use in agriculture, studies dealing with coupled crop-surface water-groundwater models are needed. Such integrated models of crop and hydrology can provide accurate quantification of spatio-temporal variations of water balance parameters such as soil moisture store, evapotranspiration and recharge in a catchment. Performance of a coupled crop-hydrology model would depend on the availability of a calibrated crop model for various irrigated/rainfed crops and also on an accurate knowledge of soil hydraulic parameters in the catchment at relevant scale. Moreover, such a coupled model should be designed so as to enable the use/assimilation of recent satellite remote sensing products (optical and microwave) in order to model the processes at catchment scales. In this study we present a framework to couple a crop model with a groundwater model for applications to irrigated groundwater agricultural systems. We discuss the calibration of the STICS crop model and present a methodology to estimate the soil hydraulic parameters by inversion of crop model using both ground and satellite based data. Using this methodology we demonstrate the feasibility of estimation of potential recharge due to spatially varying soil/crop matrix.

Classification calibration dimension for general multiclass losses

We study consistency properties of surrogate loss functions for general multiclass classification problems, defined by a general loss matrix. We extend the notion of classification calibration, which has been studied for binary and multiclass 0-1 classification problems (and for certain other specific learning problems), to the general multiclass setting, and derive necessary and sufficient conditions for a surrogate loss to be classification calibrated with respect to a loss matrix in this setting. We then introduce the notion of \emph{classification calibration dimension} of a multiclass loss matrix, which measures the smallest `size' of a prediction space for which it is possible to design a convex surrogate that is classification calibrated with respect to the loss matrix. We derive both upper and lower bounds on this quantity, and use these results to analyze various loss matrices. In particular, as one application, we provide a different route from the recent result of Duchi et al.\ (2010) for analyzing the difficulty of designing `low-dimensional' convex surrogates that are consistent with respect to pairwise subset ranking losses. We anticipate the classification calibration dimension may prove to be a useful tool in the study and design of surrogate losses for general multiclass learning problems.

Extension of TSVM to multi-class and hierarchical text classification problems With general losses

Transductive SVM (TSVM) is a well known semi-supervised large margin learning method for binary text classification. In this paper we extend this method to multi-class and hierarchical classification problems. We point out that the determination of labels of unlabeled examples with fixed classifier weights is a linear programming problem. We devise an efficient technique for solving it. The method is applicable to general loss functions. We demonstrate the value of the new method using large margin loss on a number of multi-class and hierarchical classification datasets. For maxent loss we show empirically that our method is better than expectation regularization/constraint and posterior regularization methods, and competitive with the version of entropy regularization method which uses label constraints.

Surrogate Regret Bounds for Bipartite Ranking via Strongly Proper Losses

The problem of bipartite ranking, where instances are labeled positive or negative and the goal is to learn a scoring function that minimizes the probability of mis-ranking a pair of positive and negative instances (or equivalently, that maximizes the area under the ROC curve), has been widely studied in recent years. A dominant theoretical and algorithmic framework for the problem has been to reduce bipartite ranking to pairwise classification; in particular, it is well known that the bipartite ranking regret can be formulated as a pairwise classification regret, which in turn can be upper bounded using usual regret bounds for classification problems. Recently, Kotlowski et al. (2011) showed regret bounds for bipartite ranking in terms of the regret associated with balanced versions of the standard (non-pairwise) logistic and exponential losses. In this paper, we show that such (non-pairwise) surrogate regret bounds for bipartite ranking can be obtained in terms of a broad class of proper (composite) losses that we term as strongly proper. Our proof technique is much simpler than that of Kotlowski et al. (2011), and relies on properties of proper (composite) losses as elucidated recently by Reid and Williamson (2010, 2011) and others. Our result yields explicit surrogate bounds (with no hidden balancing terms) in terms of a variety of strongly proper losses, including for example logistic, exponential, squared and squared hinge losses as special cases. An important consequence is that standard algorithms minimizing a (non-pairwise) strongly proper loss, such as logistic regression and boosting algorithms (assuming a universal function class and appropriate regularization), are in fact consistent for bipartite ranking; moreover, our results allow us to quantify the bipartite ranking regret in terms of the corresponding surrogate regret. We also obtain tighter surrogate bounds under certain low-noise conditions via a recent result of Clemencon and Robbiano (2011).

Comparative evaluation of inversion approaches of the radiative transfer model for estimation of crop biophysical parameters

The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for para-meters irrespective of inversion approaches was leaf area index > canopy chlorophyll content > leaf chlorophyll content > equivalent leaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.

Occurrence of sorghum midge stenodiplosis sorghicola (Coq,) during the second crop growing season on the pacific coastal plain of Nicaragua

La mosquita de la panojas del sorgo, Stenediplosls sorghicola (Coq.) es una de las plagas mas importantes que atacan al sorgo en Nicaragua. Varios estudios fueron conducidos en el pacifico de Nicaragua durante 1998 y 1999 para determinar hospederos y la actividad de esta plaga durante la segunda fecha de siembra de este cultivo (Postrera). Hembras oviposltaron en sorgo desde mediados de Septiembre hasta los últimos dfas de Diciembre. Cuando sorgo no se encontraba presente, hembras ovipositaron en sorgo escobero, Sorghum bicolor (L.) o pastoJonson, Sorghum halepense L. Pasto Jonson florea durante todo el ano y puede servir como hospedero mientras las otras especies de sorghum no se encuentran en el campo. Cuando estos tres hospederos estaban floreando al mismo tiempo, las hembras exhibieron una fuerte preferencia para ovlpositar en sorgo. Basado en estas observaciones la mosquita de la panoja del sorgo puede estar activa durante todo el ano en el pacifico de Nicaragua. Conocimiento de la ocurrencia y comportamiento de la mosquita en el area, es muy Importante para el desarrollo de estrategias de manejo de esta plaga.

Effects of preceding crop and weed control method on weeds and common bean (Phaseoulus vulgaris L.) yield in a no-tillage cropping system

En 1994, se inició un trabajo de investigación el cual se continuo durante tres años, con el objetivo de evaluar el efecto de cultivos antecesores y métodos de control de malezas sobre el rendimiento frijol común ( P h a s e o l u s vulgaris L.) y la dinámica de las malezas. El frijol y los cultivos antecesores fueron sembrados en sistema de cero labranza. Las secuencias de cultivos estudiadas fueron: maíz (Zea mays L), seguido de frijol y frijol seguido de frijol. Se analizaron tres ciclos de los cultivos (1994, 1995 y 1996). La secuencia de cultivo que produjo el máximo rendimiento fue maíz seguido de frijol en 1994, así como también el promedio de rendimiento a través de los años. Por otro lado, los mayores valores de vainas por planta y semillas por vaina se obtuvieron cuando el frijol antecedió al frijol en 1994. Parcelas con controles de malezas mecánico y químico obtuvieron menores densidades y peso seco de malezas y mejores rendimientos que aquellas parcelas en las cuales se controlo la maleza a través de cobertura muerta.

Effects of perturbations on estuarine microcosms

Microcosms containing planktonic communities from Chesapeake Bay responded to enrichment with sewage by developing larger standing crops of phytoplankton and zooplankton. Data suggest that increased productivity would be reflected up the food chain but might increase existing problems with dissolved oxygen and might lead to qualitative changes in the composition of the zooplankton. Either phosphorus or nitrogen was removed more rapidly from solution depending on where and when the experimental water was obtained. Increases in standing crop of algae were associated with loss of nitrogen from solution in two experiments and losses of both nitrogen and phosphorus from solution in one experiment.

Impact of fertlizers and pesticides on soil and water quality

[ES]En la presente tesis se ha estudiado el impacto de diferentes fertilizantes y pesticidas utilizados en la Zona Vulnerable de Vitoria-Gasteiz en la calidad del suelo y las aguas de dicha zona. Se ha podido constatar que hoy en día siguen lixiviándose cantidades significativas de nitratos y pesticidas (e.g., etofumesato y difenoconazol) a las aguas de la Zona Vulnerable, durante el cultivo de remolacha azucarera (Beta vulgaris L.), muy característico de la zona de estudio. Se comprobó que el alto contenido en nitratos de las aguas subterráneas en la Zona Vulnerable es mitigado, al menos en parte, por la acción de la actividad microbiana desnitrificante que alberga la zona riparia del humedal de Salburua. Dicho proceso, sin embargo, supone la emisión a la atmósfera de importantes cantidades de gases de efecto invernadero (CO2 y N2O), y puede verse afectado negativamente por la presencia de pesticidas (e.g., deltametrina) en el medio.Por otra parte, hemos observado que diversos pesticidas (deltametrina, etofumesato, difenoconazol) aplicados en concentraciones similares a las dosis de aplicación en campo inducen cambios, de carácter limitado y transitorio, en las comunidades microbianas edáficas, siendo más significativos en el caso del fungicida difenoconazol. El efecto de los pesticidas fue más acusado a medida que aumentaba su concentración en el medio. Finalmente, encontramos que la aplicación de abonos orgánicos (avicompost), en lugar de los fertilizantes sintéticos tradicionales (NPK), además de mejorar la degradación de los pesticidas y disminuir el impacto de éstos sobre la calidad del suelo, podría ayudar a reducir las pérdidas de nitratos por lixiviación.

A quantitative analysis of the phytoplankton of the Gulf of Panama II. On the relationship between C14 assimilation and the diatom standing crop

ENGLISH: The Inter-American Tropical Tuna Commission has maintained a hydro-biological station in the Gulf of Panama located at 8°45'N, 79°23'W in connection with their ecological investigation of the anchoveta (Cetengraulis mysticetus), a tuna baitfish (see Peterson, 1961, for references) . The depth is approximately 42 meters at mean low water at this station. Routine hydrographic and biological observations have been made (Schaefer, Bishop and Howard, 1958; Schaefer and Bishop, 1958; Forsbergh, 1963), including the collection of quantitative phytoplankton samples from November 1954 through May 1957 (Smayda, 1959; unpublished). The seasonal and regional variations in phytoplankton growth in the Gulf of Panama have also been investigated (Smayda, 1963). The relationships existing between C1 4 assimilation as determined by 24 hour in situ experiments and diatom standing crop at 10 meters when expressed as cell numbers, cell volume, cell surface area and cell plasma volume have been assessed for 30 observations made between November 1954 and May 1957 at 8°45'N, 79°23'W. The average cell volume and cell surface area characteristics for 110 diatom species and varieties are presented. SPANISH: Las relaciones existentes entre la asimilación del C14 , determinadas después de 24 horas de experimentos in situ, y la cosecha estable de las diatomeas a 10 metros, expresando el número de células, volumen celular, área de la superficie celular y volumen del plasma celular, han sido determinadas por medio de 30 observaciones hechas entre noviembre de 1954 y mayo de 1957, a los 8°45'N, 79°23'W. Se presenta, para 110 especies y variedades de diatomeas, el promedio de las características del volumen celular y del área de la superficie celular. (PDF contains 67 pages.)

Assessment of post-harvest losses in Nigeria fishery: the Kainji Lake model

The paper provides a description of a methodology used for quantitative assessment of post harvest losses in the Kainji Lake Fishery (Nigeria). The sample population was made up of 314 fisherfolk, 115 processors, 125 fish buyers and 111 fish sellers. For the determination of handling losses, 24,839 fishes weighing 2,389.31 kg belonging to 43 species were examined of which 10% by number and 9% by weight deteriorated at checking and 4% by number and 3% by weight at landing. Processing losses recorded 22% by number and 16% by weight deteriorated prior to and during smoking with the traditional 'Banda' kiln. During marketing, 16% of fish sold had deteriorated and 6% by weight of fish bought also deteriorated, mainly due to insect infestation during storage. Based on the 1995 yield estimate for Kainji Lake fishery, approximately 1000 tons of fish estimated at 80 million Naira were lost during handling alone. This figure would be much higher if the level of losses during processing and marketing are included. This assessment technique is recommended for use in obtaining quantifiable data on post harvest losses from other water bodies in Nigeria

A preliminary investigation into the post harvest losses of fish in Shiroro Lake area

The findings are presented of a study conducted to assess the post harvest losses in Shiroro Lake, Nigeria. The major objectives were to identify and quantify the types of losses, to provide recommendations that would enhance formulation of policy guidelines for utilization and exploitation of the declining fishery resources of the lake