A review of neural networks in plant disease detection using hyperspectral data

© 2018 China Agricultural University This paper reviews advanced Neural Network (NN) techniques available to process hyperspectral data, with a special emphasis on plant disease detection. Firstly, we provide a review on NN mechanism, types, models, and classifiers that use different algorithms to process hyperspectral data. Then we highlight the current state of imaging and non-imaging hyperspectral data for early disease detection. The hybridization of NN-hyperspectral approach has emerged as a powerful tool for disease detection and diagnosis. Spectral Disease Index (SDI) is the ratio of different spectral bands of pure disease spectra. Subsequently, we introduce NN techniques for rapid development of SDI. We also highlight current challenges and future trends of hyperspectral data

Analytical customer relationship management in retailing supported by data mining techniques

Tese de doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

A review of clustering techniques and developments

© 2017 Elsevier B.V. This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. Clustering is defined as an unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. The approaches used in these methods are discussed with their respective states of art and applicability. The measures of similarity as well as the evaluation criteria, which are the central components of clustering, are also presented in the paper. The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted

A hybrid electronic nose system for monitoring the quality of potable water

This PhD thesis reports on the potential application of an electronic nose to analysing the quality of potable water. The enrichment of water by toxic cyanobacteria is fast becoming a severe problem in the quality of water and a common source of environmental odour pollution. Thus, of particular interest is the classification and early warning of toxic cyanobacteria in water. This research reports upon the first attempt to identify electronically cyanobacteria in water. The measurement system comprises a Cellfacts instrument and a Warwick e-nose specially constructed for the testing of the cyanobacteria in water. The Warwick e- nose employed an array of six commercial odour sensors and was set-up to monitor not only the different strains, but also the growth phases, of cyanobacteria. A series of experiments was carried out to analyse the nature of two closely related strains of cyanobacteria, Microcystis aeruginosa PCC 7806 which produces a toxin and PCC 7941 that does not. Several pre-processing techniques were explored in order to remove the noise factor associated with running the electronic nose in ambient air, and the normalised fractional difference method was found to give the best PCA plot. Three supervised neural networks, MLP, LVQ and Fuzzy ARTMAP, were used and compared for the classification of both two strains and four different growth phases of cyanobacteria (lag, growth, stationary and late stationary). The optimal MLP network was found to classify correctly 97.1 % of unknown non-toxic and 100 % of unknown toxic cyanobacteria. The optimal LVQ and Fuzzy ARTMAP algorithms were able to classify 100% of both strains of cyanobacteria. The accuracy of MLP, LVQ and Fuzzy ARTMAP algorithms with 4 different growth phases of toxic cyanobacteria was 92.3 %, 95.1 % and 92.3 %, respectively. A hybrid e-nose system based on 6 MOS, 6 CP, 2 temperature sensors, 1 humidity sensor and 2 flow sensors was finally developed. Using the hybrid system, data were gathered on six different cyanobacteria cultures for the classification of growth phase. The hybrid resistive nose showed high resolving power to discriminate six growth stages as well as three growth phases. Even though time did not permit many series of the continuous monitoring, because of the relatively long life span (30-40 days) of cyanobacteria, improved results indicate the use of a hybrid nose. The HP 4440 chemical sensor was also used for the discrimination of six different cyanobacteria samples and the comparison with the electronic nose. The hybrid resistive nose based on 6 MOS and 6 CP showed a better resolving power to discriminate six growth stages as well as three growth phases than the HP 4440 chemical sensor. Although the mass analyser detects individual volatile chemicals accurately, it proves no indication of whether the volatile is an odour. The results demonstrate that it is possible to apply the e-nose system for monitoring the quality of potable water. It would be expected that the hybrid e-nose could be applicable to a large number of applications in health and safety with a greater flexibility

Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks

Two novel ensemble approaches for improving classification of neural networks

x, 77 leaves ; 29 cmThe task of pattern recognition is one of the most recurrent tasks that we encounter in our lives. Therefore, there has been a significant interest of automating this task for many decades. Many techniques have been developed to this end, such as neural networks. Neural networks are excellent pattern classifiers with very robust means of learning and a relatively high classification power. Naturally, there has been an increasing interest in further improving neural networks’ classification for complex problems. Many methods have been proposed. In this thesis, we propose two novel ensemble approaches to further improving neural networks’ classification power, namely paralleling neural networks and chaining neural networks. The first seeks to improve a neural network’s classification by combining the outputs of a set of neural networks together via another neural network. The second improves a neural network’s accuracy by feeding the outputs of a neural network into another and continually doing so in a chaining fashion until the error is reduced sufficiently. The effectiveness of both approaches has been demonstrated through a series of experiments. i

Multi-image classification and compression using vector quantization

Vector Quantization (VQ) is an image processing technique based on statistical clustering, and designed originally for image compression. In this dissertation, several methods for multi-image classification and compression based on a VQ design are presented. It is demonstrated that VQ can perform joint multi-image classification and compression by associating a class identifier with each multi-spectral signature codevector. We extend the Weighted Bayes Risk VQ (WBRVQ) method, previously used for single-component images, that explicitly incorporates a Bayes risk component into the distortion measure used in the VQ quantizer design and thereby permits a flexible trade-off between classification and compression priorities. In the specific case of multi-spectral images, we investigate the application of the Multi-scale Retinex algorithm as a preprocessing stage, before classification and compression, that performs dynamic range compression, reduces the dependence on lighting conditions, and generally enhances apparent spatial resolution. The goals of this research are four-fold: (1) to study the interrelationship between statistical clustering, classification and compression in a multi-image VQ context; (2) to study mixed-pixel classification and combined classification and compression for simulated and actual, multispectral and hyperspectral multi-images; (3) to study the effects of multi-image enhancement on class spectral signatures; and (4) to study the preservation of scientific data integrity as a function of compression. In this research, a key issue is not just the subjective quality of the resulting images after classification and compression but also the effect of multi-image dimensionality on the complexity of the optimal coder design

Representing meaning: a feature-based model of object and action words

The representation of word meaning has received substantial attention in the psycholinguistic literature over the past decades, yet the vast majority of studies have been limited to words referring to concrete objects. The aim of the present work is to provide a theoretically and neurally plausible model of lexical-semantic representations, not only for words referring to concrete objects but also for words referring to actions and events using a common set of assumptions across domains. In order to do so, features of meaning are generated by naïve speakers, and used as a window into important aspects of representation. A first series of analyses test how the meanings of words of different types are reflected in features associated with different modalities of sensory-motor experience, and how featural properties may be related to patterns of impairment in language-disordered populations. The features of meaning are then used to generate a model of lexical-semantic similarity, in which these different types of words are represented within a single system, under the assumption that lexical-semantic representations serve to provide an interface between conceptual knowledge derived in part from sensory-motor experience, and other linguistic information such as syntax, phonology and orthography. Predictions generated from this model are tested in a series of behavioural experiments designed to test two main questions: whether similarity measures based on speaker- generated features can predict fine-grained semantic similarity effects, and whether the predictive quality of the model is comparable for words referring to objects and words referring to actions. The results of five behavioural experiments consistently reveal graded semantic effects as predicted by the feature-based model, of similar magnitude for objects and actions. The model's fine-grained predictive performance is also found to be superior to other word-based models of representation (Latent Semantic Analysis, and similarity measures derived from Wordnet)

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

Historical Land use/Land cover classification and its change detection mapping using Different Remotely Sensed Data from LANDSAT (MSS, TM and ETM+) and Terra (ASTER) sensors: a case study of the Euphrates River Basin in Syria with focus on agricultural irrigation projects

This thesis deals spatially and regionally with the natural boundaries of the Euphrates River Basin (ERB) in Syria. Scientifically, the research covers the application of remote sensing science (optical remote sensing: LANDSAT-MSS, TM, and ETM+; and TERRA: ASTER); and methodologically, in Land Use/Land Cover (LULC) classification and mapping, automatically and/or semi-automatically; in LULC-change detection; and finally in the mapping of historical irrigation and agricultural projects for the extraction of differing crop types and the estimation of their areas. With regard to time, the work is based on the years 1975, 1987, 2005 and 2007. Initially, preprocessing of the satellite data (geometric- and radiometric- processing, image enhancement, best bands composite selection, transformation, mosaicing and finally subsetting) was carried out. Then, the Land Use/Land Cover Classification System (LCCS) of the Food and Agriculture Organization (FAO) was chosen. The following steps were followed in LULC- classification and change detection mapping: visual interpretation in addition to digital image processing techniques; pixel-based classification methods; unsupervised classification: ISODATA-method; and supervised classification and multistage supervised approaches using the algorithms: Maximum Likelihood Classifier (MLC), Neural Network classifier (NN) and Support Vector Machines (SVM). These were trialed on a test area to determine the optimized classification approach/algorithm for application on the whole study area (ERB) based on the available imagery. Pre- and post- classification change detection methods (comparison approaches) were used to detect changes in land use/land cover-classes (for the years 1975, 1987 and 2007) in the study area. The remote sensing methods show a high potential in mapping historical and present land use/land cover classes and its changes over time. Significant results are also possible for agricultural crop classification in relatively large regional areas (the ERB in Syria is almost 50,335 km²). Change trends in the study area and period was characterized by land-intensive agricultural expansion. The rapid, more labor- and capital- intensive growth in the agricultural sector was enabled by the introduction of fertilizer, improved access to rural roads and markets, and the expansion of the government irrigation projects. Irrigated areas increased 148 % in the past 32 years from 249,681 ha in 1975 to 596,612 ha in 2007