Artificial neural networks to detect forest fire prone areas in the southeast fire district of Mississippi

An analysis of the fire occurrences parameters is essential to save human lives, property, timber resources and conservation of biodiversity. Data conversion formats such as raster to ASCII facilitate the integration of various GIS software’s in the context of RS and GIS modeling. This research explores fire occurrences in relation to human interaction, fuel density interaction, euclidean distance from the perennial streams and slope using artificial neural networks. The human interaction (ignition source) and density of fuels is assessed by Newton’s Gravitational theory. Euclidean distance to perennial streams and slope that do posses a significant role were derived using GIS tools. All the four non linear predictor variables were modeled using the inductive nature of neural networks. The Self organizing feature map (SOM) utilized for fire size risk classification produced an overall classification accuracy of 62% and an overall kappa coefficient of 0.52 that is moderate (fair) for annual fires

A Review on Tomato Leaf Disease Detection using Deep Learning Approaches

Agriculture is one of the major sectors that influence the India economy due to the huge population and ever-growing food demand. Identification of diseases that affect the low yield in food crops plays a major role to improve the yield of a crop. India holds the world's second-largest share of tomato production. Unfortunately, tomato plants are vulnerable to various diseases due to factors such as climate change, heavy rainfall, soil conditions, pesticides, and animals. A significant number of studies have examined the potential of deep learning techniques to combat the leaf disease in tomatoes in the last decade. However, despite the range of applications, several gaps within tomato leaf disease detection are yet to be addressed to support the tomato leaf disease diagnosis. Thus, there is a need to create an information base of existing approaches and identify the challenges and opportunities to help advance the development of tools that address the needs of tomato farmers. The review is focussed on providing a detailed assessment and considerations for developing deep learning-based Convolutional Neural Networks (CNNs) architectures like Dense Net, ResNet, VGG Net, Google Net, Alex Net, and LeNet that are applied to detect the disease in tomato leaves to identify 10 classes of diseases affecting tomato plant leaves, with distinct trained disease datasets. The performance of architecture studies using the data from plantvillage dataset, which includes healthy and diseased classes, with the assistance of several different architectural designs. This paper helps to address the existing research gaps by guiding further development and application of tools to support tomato leaves disease diagnosis and provide disease management support to farmers in improving the crop

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Satellite image classification and spatial analysis of agricultural areas for land cover mapping of grizzly bear habitat

Habitat loss and human-caused mortality are the most serious threats facing grizzly bear (Ursus arctosi L.) populations in Alberta, with conflicts between people and bears in agricultural areas being especially important. For this reason, information is needed about grizzly bears in agricultural areas. The objectives of this research were to find the best possible classification approach for determining multiple classes of agricultural and herbaceous land cover for the purpose of grizzly bear habitat mapping, and to determine what, if any, spatial and compositional components of the landscape affected the bears in these agricultural areas. Spectral and environmental data for five different land-cover types of interest were acquired in late July, 2007, from Landsat Thematic Mapper satellite imagery and field data collection in two study areas in Alberta. Three different classification methods were analyzed, the best method being the Supervised Sequential Masking (SSM) technique, which gave an overall accuracy of 88% and a Kappa Index of Agreement (KIA) of 83%. The SSM classification was then expanded to cover 6 more Landsat scenes, and combined with bear GPS location data. Analysis of this data revealed that bears in agricultural areas were found in grasses / forage crops 77% of the time, with small grains and bare soil / fallow fields making up the rest of the visited land-cover. Locational data for 8 bears were examined in an area southwest of Calgary, Alberta. The 4494 km2 study area was divided into 107 sub-landscapes of 42 km2. Five-meter spatial resolution IRS panchromatic imagery was used to classify the area and derive compositional and configurational metrics for each sub-landscape. It was found that the amount of agricultural land did not explain grizzly bear use; however, secondary effects of agriculture on landscape configuration did. High patch density and variation in distances between neighboring similar patch types were seen as the most significant metrics in the abundance models; higher variation in patch shape, greater contiguity between patches, and lower average distances between neighboring similar patches were the most consistently significant predictors in the bear presence / absence models. Grizzly bears appeared to prefer areas that were structurally correlated to natural areas, and avoided areas that were structurally correlated to agricultural areas. Grizzly bear presence could be predicted in a particular sub-landscape with 87% accuracy using a logistic regression model. Between 30% and 35% of the grizzlies‟ landscape scale habitat selection was explained

Machine learning methods for discriminating natural targets in seabed imagery

The research in this thesis concerns feature-based machine learning processes and methods for discriminating qualitative natural targets in seabed imagery. The applications considered, typically involve time-consuming manual processing stages in an industrial setting. An aim of the research is to facilitate a means of assisting human analysts by expediting the tedious interpretative tasks, using machine methods. Some novel approaches are devised and investigated for solving the application problems. These investigations are compartmentalised in four coherent case studies linked by common underlying technical themes and methods. The first study addresses pockmark discrimination in a digital bathymetry model. Manual identification and mapping of even a relatively small number of these landform objects is an expensive process. A novel, supervised machine learning approach to automating the task is presented. The process maps the boundaries of ≈ 2000 pockmarks in seconds - a task that would take days for a human analyst to complete. The second case study investigates different feature creation methods for automatically discriminating sidescan sonar image textures characteristic of Sabellaria spinulosa colonisation. Results from a comparison of several textural feature creation methods on sonar waterfall imagery show that Gabor filter banks yield some of the best results. A further empirical investigation into the filter bank features created on sonar mosaic imagery leads to the identification of a useful configuration and filter parameter ranges for discriminating the target textures in the imagery. Feature saliency estimation is a vital stage in the machine process. Case study three concerns distance measures for the evaluation and ranking of features on sonar imagery. Two novel consensus methods for creating a more robust ranking are proposed. Experimental results show that the consensus methods can improve robustness over a range of feature parameterisations and various seabed texture classification tasks. The final case study is more qualitative in nature and brings together a number of ideas, applied to the classification of target regions in real-world sonar mosaic imagery. A number of technical challenges arose and these were surmounted by devising a novel, hybrid unsupervised method. This fully automated machine approach was compared with a supervised approach in an application to the problem of image-based sediment type discrimination. The hybrid unsupervised method produces a plausible class map in a few minutes of processing time. It is concluded that the versatile, novel process should be generalisable to the discrimination of other subjective natural targets in real-world seabed imagery, such as Sabellaria textures and pockmarks (with appropriate features and feature tuning.) Further, the full automation of pockmark and Sabellaria discrimination is feasible within this framework

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

The 1995 Science Information Management and Data Compression Workshop

This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on October 26-27, 1995, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival, and retrieval of large quantities of data in future Earth and space science missions. It consisted of fourteen presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The Workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center

A Modular Approach to Lung Nodule Detection from Computed Tomography Images Using Artificial Neural Networks and Content Based Image Representation

Lung cancer is one of the most lethal cancer types. Research in computer aided detection (CAD) and diagnosis for lung cancer aims at providing effective tools to assist physicians in cancer diagnosis and treatment to save lives. In this dissertation, we focus on developing a CAD framework for automated lung cancer nodule detection from 3D lung computed tomography (CT) images. Nodule detection is a challenging task that no machine intelligence can surpass human capability to date. In contrast, human recognition power is limited by vision capacity and may suffer from work overload and fatigue, whereas automated nodule detection systems can complement expert’s efforts to achieve better detection performance. The proposed CAD framework encompasses several desirable properties such as mimicking physicians by means of geometric multi-perspective analysis, computational efficiency, and the most importantly producing high performance in detection accuracy. As the central part of the framework, we develop a novel hierarchical modular decision engine implemented by Artificial Neural Networks. One advantage of this decision engine is that it supports the combination of spatial-level and feature-level information analysis in an efficient way. Our methodology overcomes some of the limitations of current lung nodule detection techniques by combining geometric multi-perspective analysis with global and local feature analysis. The proposed modular decision engine design is flexible to modifications in the decision modules; the engine structure can adopt the modifications without having to re-design the entire system. The engine can easily accommodate multi-learning scheme and parallel implementation so that each information type can be processed (in parallel) by the most adequate learning technique of its own. We have also developed a novel shape representation technique that is invariant under rigid-body transformation and we derived new features based on this shape representation for nodule detection. We implemented a prototype nodule detection system as a demonstration of the proposed framework. Experiments have been conducted to assess the performance of the proposed methodologies using real-world lung CT data. Several performance measures for detection accuracy are used in the assessment. The results show that the decision engine is able to classify patterns efficiently with very good classification performance