Wireless Sensor Networks for Paddy Field Crop Monitoring Application in Kuttanad

The evolution of wireless sensor network technology has enabled us to develop advanced systems for real time monitoring. In the present scenario wireless sensor networks are increasingly being used for precision agriculture. The advantages of using wireless sensor networks in agriculture are distributed data collection and monitoring, monitor and control of climate, irrigation and nutrient supply. Hence decreasing the cost of production and increasing the efficiency of production.This paper describes the application of wireless sensor network for crop monitoring in the paddy fields of kuttand, a region of Kerala, the southern state of India.

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.

Spectral response of winter wheat to crop development and various agronomic limitations

For maximizing the effective applications of remote sensing in crop recognition, crop performance assessment and canopy variables estimation at large areas, it is essential to fully understand the spectral response of canopy to crop development and varying growing conditions. In this paper, the spectral properties of winter wheat canopy under different growth stages and different agronomic conditions were investigated at the field level based on reflectance measurements. It was proved that crop growth and development, nitrogen fertilization rates, nutrient deficit (e.g. lacking any kind of nitrogen, phosphorus and kalium fertilizer or lacking all of them), irrigation frequency and plant density had direct influence on canopy reflectance in 400-900 nm which including the visible/near infrared bands, and resulted in great changes of spectral curves. It was suggested that spectral reflectance of crop canopy can well reflect the growth and development of crop and the impacts from various factors, and was feasible to provide vital information for crop monitoring and assessment. ©2010 IEEE.

Analysing industry needs to fine tune IPM project activities

As a first step to better targeting the activities of a project for improving management of western flower thrips, Frankliniella occidentialis, (WFT) in field grown vegetable crops, we surveyed growers, consultants and other agribusiness personnel in two regions of Queensland. Using face-to-face interviews, we collected data on key pests and measures used to manage them, the importance of WFT and associated viral diseases, sources of pest management information and additional skills and knowledge needed by growers and industry. Responses were similar in the two regions. While capsicum growers in one northern Queensland district had suffered serious losses from WFT damage in 2002, in general the pest was not seen as a major problem. In cucurbit crops, the silverleaf whitefly (Bemisia tabaci biotype B) was considered the most difficult insect pest to manage. Pest control tactics were largely based on pesticides although many respondents mentioned non-chemical methods such as good farm hygiene practices, control of weed hosts and regular crop monitoring, particularly when prompted. Respondents wanted to know more about pest identification, biology and damage, spray application and the best use of insecticides. Natural enemies were mentioned infrequently. Keeping up to date with available pesticide options, availability of new chemicals and options for a district-wide approach to managing pests emerged as key issues. Growers identified agricultural distributors, consultants, Queensland Department of Primary Industries staff, other growers and their own experience as important sources of information. Field days, workshops and seminars did not rank highly. Busy vegetable growers wanted these activities to be short and relevant, and preferred to be contacted by post and facsimile rather than email. In response to these results, we are focusing on three core, interrelated project extension strategies: (i) short workshops, seminars and farm walks to provide opportunities for discussion, training and information sharing with growers and their agribusiness advisors; (ii) communication via newsletters and information leaflets; (iii) support for commercialisation of services.

Identification of insects, spiders and mites in vegetable crops. Workshop Manual, 2nd edition

Accurate and confident identification of the insects, spiders and mites in vegetable crops is the first step towards successful management of pests and natural enemies. It is an essential prerequisite for crop monitoring, which is the backbone of an effective pest management program. This workshop manual and trainer's handbook were compiled as part of an insect, spider and mite identification program for Australian vegetable growers. The workshop training is designed to help growers to: • know how to collect and preserve insects for identification • be able to classify most common insects (particularly those of horticultural significance) into broad groups • appreciate the importance of these groups in pest, predator and parasite identification and management • collect and classify some insect pests, predators and parasites of horticultural importance.

Development and Deployment of Wireless Sensor Network in Paddy Fields of Kuttanad

The evolution of wireless sensor network technology has enabled us to develop advanced systems for real time monitoring. In the present scenario wireless sensor networks are increasingly being used for precision agriculture. The advantages of using wireless sensor networks in agriculture are distributed data collection and monitoring, monitor and control of climate, irrigation and nutrient supply. Hence decreasing the cost of production and increasing the efficiency of production. This paper describes the development and deployment of wireless sensor network for crop monitoring in the paddy fields of Kuttanad, a region of Kerala, the southern state of India.

Energy Optimized Secure Routing Protocol for Wireless Sensor Networks

The evolution of wireless sensor network technology has enabled us to develop advanced systems for real time monitoring. In the present scenario wireless sensor networks are increasingly being used for precision agriculture. The advantages of using wireless sensor networks in agriculture are distributed data collection and monitoring, monitor and control of climate, irrigation and nutrient supply. Hence decreasing the cost of production and increasing the efficiency of production. This paper describes the security issues related to wireless sensor networks and suggests some techniques for achieving system security. This paper also discusses a protocol that can be adopted for increasing the security of the transmitted data

Análisis de la política anti-narcóticos en la República del Perú y la influencia de la cooperación estadounidense (2006-2011)

En este estudio de caso se presenta un repaso histórico de las políticas anti-narcóticos en el Perú así como la influencia que ha ejercido EE. UU en ésta a través de la cooperación bilateral. Se analizan igualmente los programas de cooperación en el gobierno de Alan García(2006 - 2011) y sus resultados

Maize yield and rainfall on different spatial and temporal scales in Southern Brazil

This study aimed to establish relationships between maize yield and rainfall on different temporal and spatial scales, in order to provide a basis for crop monitoring and modelling. A 16-year series of maize yield and daily rainfall from 11 municipalities and micro-regions of Rio Grande do Sul State was used. Correlation and regression analyses were used to determine associations between crop yield and rainfall for the entire crop cycle, from tasseling to 30 days after, and from 5 days before tasseling to 40 days after. Close relationships between maize yield and rainfall were found, particularly during the reproductive period (45-day period comprising the flowering and grain filling). Relationships were closer on a regional scale than at smaller scales. Implications of the crop-rainfall relationships for crop modelling are discussed.

HDC poinsettia tracker: flexible graphical tracking software

Growing pot poinsettia and similar crops involves careful crop monitoring and management to ensure that height specifications are met. Graphical tracking represents a target driven approach to decision support with simple interpretation. HDC (Horticultural Development Council) Poinsettia Tracker implements a graphical track based on the Generalised Logistic Curve, similar to that of other tracking packages. Any set of curve parameters can be used to track crop progress. However, graphical tracks must be expected to be site and cultivar specific. By providing a simple Curve fitting function, growers can easily develop their own site and variety specific ideal tracks based on past records with increasing quality as more seasons' data are added. (C) 2009 Elsevier B.V. All rights reserved.

New developments in UAV remote sensing for pest management and the implications for cotton

Efficient crop monitoring and pest damage assessments are key to protecting the Australian agricultural industry and ensuring its leading position internationally. An important element in pest detection is gathering reliable crop data frequently and integrating analysis tools for decision making. Unmanned aerial systems are emerging as a cost-effective solution to a number of precision agriculture challenges. An important advantage of this technology is it provides a non-invasive aerial sensor platform to accurately monitor broad acre crops. In this presentation, we will give an overview on how unmanned aerial systems and machine learning can be combined to address crop protection challenges. A recent 2015 study on insect damage in sorghum will illustrate the effectiveness of this methodology. A UAV platform equipped with a high-resolution camera was deployed to autonomously perform a flight pattern over the target area. We describe the image processing pipeline implemented to create a georeferenced orthoimage and visualize the spatial distribution of the damage. An image analysis tool has been developed to minimize human input requirements. The computer program is based on a machine learning algorithm that automatically creates a meaningful partition of the image into clusters. Results show the algorithm delivers decision boundaries that accurately classify the field into crop health levels. The methodology presented in this paper represents a venue for further research towards automated crop protection assessments in the cotton industry, with applications in detecting, quantifying and monitoring the presence of mealybugs, mites and aphid pests.

Colombia como referente en la lucha contra las drogas : perspectivas de la cooperación con África Occidental

Este estudio de caso se realiza con el objetivo de analizar cómo la cooperación entre Colombia y África occidental en la lucha contra el tráfico de drogas repercute en la imagen del Estado colombiano como referente en esfuerzos antinarcóticos desde la periferia. En consecuencia, se busca conocer la forma en la cual los acuerdos bilaterales interinstitucionales, la participación en foros y la creación de una agenda internacional de lucha contra las drogas para un escenario nacional transformado, configuran la imagen del Estado colombiano. Para tal objetivo, el trabajo se desarrollará a través de los conceptos de identidad de Alexander Wendt, periferia de Mohammed Ayoob y Cooperación Sur-Sur de la Organización de Naciones Unidas y la Agencia Presidencial de Cooperación Internacional de Colombia.

Modern viticulture in southern Europe: Vulnerabilities and strategies for adaptation to water scarcity

Water is now considered the most important but vulnerable resource in the Mediterranean region. Nev ertheless, irrigation expanded fast in the region (e.g. South Portugal and Spain) to mitigate environmental stress and to guarantee stable grape yield and quality. Sustainable wine production depends on sustain able water use in the wine’s supply chain, from the vine to the bottle. Better understanding of grapevine stress physiology (e.g. water relations, temperature regulation, water use efficiency), more robust crop monitoring/phenotyping and implementation of best water management practices will help to mitigate climate effects and will enable significant water savings in the vineyard and winery. In this paper, we focused on the major vulnerabilities and opportunities of South European Mediterranean viticulture (e.g. in Portugal and Spain) and present a multi-level strategy (from plant to the consumer) to overcome region’s weaknesses and support strategies for adaptation to water scarcity, promote sustainable water use and minimize the environmental impact of the sector.

Modern viticulture in southern Europe: vulnerabilities and strategies for adaptation to water scarcity

Water is now considered the most important but vulnerable resource in the Mediterranean region. Nevertheless, irrigation expanded fast in the region (e.g. South Portugal and Spain) to mitigate environmental stress and to guarantee stable grape yield and quality. Sustainable wine production depends on sustainable water use in the wine’s supply chain, from the vine to the bottle. Better understanding of grapevine stress physiology (e.g. water relations, temperature regulation, water use efficiency), more robust crop monitoring/phenotyping and implementation of best water management practices will help to mitigate climate effects and will enable significant water savings in the vineyard and winery. In this paper, we focused on the major vulnerabilities and opportunities of South European Mediterranean viticulture (e.g. in Portugal and Spain) and present a multi-level strategy (from plant to the consumer) to overcome region’s weaknesses and support strategies for adaptation to water scarcity, promote sustainable water use and minimize the environmental impact of the sector.

Field-oriented assessment of agricultural crops through temporal segmentation of modis VI data.

Monitoring agricultural crops constitutes a vital task for the general understanding of land use spatio-temporal dynamics. This paper presents an approach for the enhancement of current crop monitoring capabilities on a regional scale, in order to allow for the analysis of environmental and socio-economic drivers and impacts of agricultural land use. This work discusses the advantages and current limitations of using 250m VI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for this purpose, with emphasis in the difficulty of correctly analyzing pixels whose temporal responses are disturbed due to certain sources of interference such as mixed or heterogeneous land cover. It is shown that the influence of noisy or disturbed pixels can be minimized, and a much more consistent and useful result can be attained, if individual agricultural fields are identified and each field's pixels are analyzed in a collective manner. As such, a method is proposed that makes use of image segmentation techniques based on MODIS temporal information in order to identify portions of the study area that agree with actual agricultural field borders. The pixels of each portion or segment are then analyzed individually in order to estimate the reliability of the temporal signal observed and the consequent relevance of any estimation of land use from that data. The proposed method was applied in the state of Mato Grosso, in mid-western Brazil, where extensive ground truth data was available. Experiments were carried out using several supervised classification algorithms as well as different subsets of land cover classes, in order to test the methodology in a comprehensive way. Results show that the proposed method is capable of consistently improving classification results not only in terms of overall accuracy but also qualitatively by allowing a better understanding of the land use patterns detected. It thus provides a practical and straightforward procedure for enhancing crop-mapping capabilities using temporal series of moderate resolution remote sensing data.