Fuzzy control system for variable rate irrigation using remote sensing

Variable rate irrigation (VRI) is the capacity to spatially vary the depth of water application in a field to handle different types of soils, crops, and other conditions. Precise management zones must be developed to efficiently apply variable rate technologies. However, there is no universal method to determine management zones. Using speed control maps for the central pivot is one option. Thus, this study aims to develop an intelligent fuzzy inference system based on precision irrigation knowledge, i.e., a system that can create prescriptive maps to control the rotation speed of the central pivot. Satellite images are used in this study because remote sensing offers quick measurements and easy access to information on crops for large irrigation areas. Based on the VRI-prescribed map created using the intelligent decisionmaking system, the pivot can increase or decrease its speed, reaching the desired depth of application in a certain irrigation zone. Therefore, considering the spatial variability in the crop has made the strategy of speed control more realistic than traditional methods for crop management. The intelligent irrigation system pointed out areas with lower leaf development, indicating that the pivot must reduce its speed, thus increasing the water layer applied to that area. The existence of well-divided zones could be observed; each zone provides a specific value for the speed that the pivot must develop for decreasing or increasing the application of the water layer to the crop area. Three quarters of the total crop area had spatial variations during water application. The set point built by the developed system pointed out zones with a decreased speed in the order of 50%. From the viewpoint of a traditional control, the relay from pivot percent timer should have been adjusted from 70% to 35% whenever the central pivot passed over that specific area. The proposed system obtained values of 37% and 47% to adjust the pivot percent timer. Therefore, it is possible to affirm that traditional control models used for central-pivot irrigators do not support the necessary precision to meet the demands of speed control determined by the developed VRI systems. Results indicate that data from the edaphoclimatic variables when well-fitted to the fuzzy logic can solve uncertainties and non-linearities of an irrigation system and establish a control model for high-precision irrigation

Bacteria classification using Cyranose 320 electronic nose

Background An electronic nose (e-nose), the Cyrano Sciences' Cyranose 320, comprising an array of thirty-two polymer carbon black composite sensors has been used to identify six species of bacteria responsible for eye infections when present at a range of concentrations in saline solutions. Readings were taken from the headspace of the samples by manually introducing the portable e-nose system into a sterile glass containing a fixed volume of bacteria in suspension. Gathered data were a very complex mixture of different chemical compounds. Method Linear Principal Component Analysis (PCA) method was able to classify four classes of bacteria out of six classes though in reality other two classes were not better evident from PCA analysis and we got 74% classification accuracy from PCA. An innovative data clustering approach was investigated for these bacteria data by combining the 3-dimensional scatter plot, Fuzzy C Means (FCM) and Self Organizing Map (SOM) network. Using these three data clustering algorithms simultaneously better 'classification' of six eye bacteria classes were represented. Then three supervised classifiers, namely Multi Layer Perceptron (MLP), Probabilistic Neural network (PNN) and Radial basis function network (RBF), were used to classify the six bacteria classes. Results A [6 × 1] SOM network gave 96% accuracy for bacteria classification which was best accuracy. A comparative evaluation of the classifiers was conducted for this application. The best results suggest that we are able to predict six classes of bacteria with up to 98% accuracy with the application of the RBF network. Conclusion This type of bacteria data analysis and feature extraction is very difficult. But we can conclude that this combined use of three nonlinear methods can solve the feature extraction problem with very complex data and enhance the performance of Cyranose 320

Intelligent Bayes Classifier (IBC) for ENT infection classification in hospital environment

Electronic Nose based ENT bacteria identification in hospital environment is a classical and challenging problem of classification. In this paper an electronic nose (e-nose), comprising a hybrid array of 12 tin oxide sensors (SnO(2)) and 6 conducting polymer sensors has been used to identify three species of bacteria, Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), and Pseudomonas aeruginosa (P. aeruginosa) responsible for ear nose and throat (ENT) infections when collected as swab sample from infected patients and kept in ISO agar solution in the hospital environment. In the next stage a sub-classification technique has been developed for the classification of two different species of S. aureus, namely Methicillin-Resistant S. aureus (MRSA) and Methicillin Susceptible S. aureus (MSSA). An innovative Intelligent Bayes Classifier (IBC) based on "Baye's theorem" and "maximum probability rule" was developed and investigated for these three main groups of ENT bacteria. Along with the IBC three other supervised classifiers (namely, Multilayer Perceptron (MLP), Probabilistic neural network (PNN), and Radial Basis Function Network (RBFN)) were used to classify the three main bacteria classes. A comparative evaluation of the classifiers was conducted for this application. IBC outperformed MLP, PNN and RBFN. The best results suggest that we are able to identify and classify three bacteria main classes with up to 100% accuracy rate using IBC. We have also achieved 100% classification accuracy for the classification of MRSA and MSSA samples with IBC. We can conclude that this study proves that IBC based e-nose can provide very strong and rapid solution for the identification of ENT infections in hospital environment

"Maximum probability rule" based classification of MRSA infections in hospital environment: Using electronic nose

An electronic nose (e-nose), comprising an array of 32 polymer carbon black composite sensors has been used to identify two species of Staphylococcus aureus bacteria, namely methicillin-resistant S. aureus (MRSA) and methicillinsusceptible S. aureus (MSSA) responsible for ear nose and throat (ENT) infections when present in standard agar solution in the hospital environment. Polymer sensors based e-nose has also been used to identify coagulase-negative staphylococci (C-NS) in the hospital environment. This e-nose based ENT bacteria identification is a classical and challenging problem of classification. In this paper an innovative classification method depending upon "Baye's theorem" and "maximum probability rule" was investigated for these three groups of S. aureus data. Two different statistical scalar feature extraction techniques, namely 'Kurtosis of the sensory data', and 'Skewness of the data', are also tested. The best results suggest that we are able to identify and classify three bacteria classes with up to 99.83% accuracy rate with the application of adaptive kernel method along with 'Kurtosis of the sensory data', and 'Skewness of the data' as feature. This type of bacteria data analysis and feature extraction is very difficult. But we can conclude that this study proves that "maximum probability rule" based classification can provide very strong solution for identification of S. aureus infections in hospital environment and very rapid detection. (c) 2006 Elsevier B.V. All rights reserved

Making Apsim Open Data Driven

Sustainable agriculture information is on high demand for farmers to make informed decisions in the crop management process. There are some tools available for predicting the crop yield and agricultural business profitability, among which is the Agriculture Production System Simulator (APSIM) [1-2]. The APSIM is a modelling framework which is developed by the Australian Commonwealth Scientific Research Organization (CSIRO) and the Queensland Government Agency. This modelling framework is used for simulating the biophysical process in a farming system for crop system management or risk prediction with environmental inputs such as rainfall, humidity, temperature, solar radiation, and wind speed. It follows the “plug-in/pull-out” approach in which users can configure the model with crop, soil and utility modules for the crop management. The model can simulate more than 20 crops and forests, e.g. alfalfa, eucalyptus, cowpea, pigeonpea, peanuts, cotton, lupin, maize, wheat, barley, sunflower, sugarcane, chickpea, and tomato. All simulation processes of the APSIM model are based on the data from the BOM-SILO database [3] which is maintained by the Queensland Climate Excellence within the Department of Science, Information Technology, Innovation and the Arts (DSITTA). The data was collected from 4760 BOM weather stations from 1889 until today. Most of the data used in the model are historical data. Being a closed system, APSIM is not suitable for dynamic information integration. As there is no external plug-in available for accessing the model, it is hard to run the model for prediction within external applications. Furthermore, while recent trends in dynamic decision support systems are very much inclined to the use of mobile applications, there is no web based services around APSIM for that purpose

Weekly data for 2012

<p>Weekly data for 2012</p

Weekly data for 2008

<p>Weekly data for 2008</p