scholarly journals Leaf Disease Detection of Agricultural plant Using Image Processing

2020 ◽  
pp. 432-435
Author(s):  
Aneel Narayanapur ◽  
Pavankumar Naik ◽  
Priya B Kori ◽  
Naseem Kalaburgi ◽  
Rubiya I M ◽  
...  
Keyword(s):  
Single Cells ◽  
Genetic Material ◽  
Threshold Value ◽  
Life Cycles ◽  
Plant Diseases ◽  
Processing Scheme ◽  
Plant Leaf ◽  
Agricultural Plant ◽  
Color Descriptor ◽  
Color Transformation

The detection of plant leaf is an very important factor to prevent serious outbreak. Automatic detection of plant disease is essential research topic. Most plant diseases are caused by fungi, bacteria, and viruses. Fungi are identified primarily from their morphology, with emphasis placed on their reproductive structures. Bacteria are considered more primitive than fungi and generally have simpler life cycles. With few exceptions, bacteria exist as single cells and increase in numbers by dividing into two cells during a process called binary fission Viruses are extremely tiny particles consisting of protein and genetic material with no associated protein. The term disease is usually used only for the destruction of live plants. The developed processing scheme consists of four main steps, first a color transformation structure for the input RGB image is created, this RGB is converted to HSI because RGB is for color generation and his for color descriptor. Then green pixels are masked and removed using specific threshold value, then the image is segmented and the useful segments are extracted, finally the texture statistics is computed. from SGDM matrices. Finally the presence of diseases on the plant leaf is evaluated.

scholarly journals Covariance Kalman Geometric Graph Based Feature Extraction And Bernoulli Kernel Classifier For Plant Leaf Disease Prediction

2021 ◽  
Vol 12 (3) ◽  
pp. 4904-4917
Author(s):  
Mohammed Zabeeulla A N Et. al.
Keyword(s):  
Feature Extraction ◽  
Agricultural Land ◽  
Geometric Graph ◽  
Plant Diseases ◽  
Classification Error ◽  
Infected Leaf ◽  
Disease Prediction ◽  
Plant Leaf ◽  
Agricultural Plant ◽  
Leaf Disease

As far as the agricultural domain is concerned, one of the most hot research areas of analysis is accurate prediction of leaf disease from the leaf images of a plant. The prediction of agricultural plant diseases bymeans of the image processing techniques will hence reduce the dependence on the farmers to safeguard their agricultural land and also their products. However, with the presence of noise, the leaf disease prediction is said to be hindered. To address this issue, in this paper, Covariance Kalman Geometric Graph-basedBernoulliClassifier (CKGG-BC) for Plant leaf disease prediction is proposed. The CKGG-BC method is split into three parts. To start with the plant leaf image provided as input, the Covariance Kalman Filtered Preprocessing modelintroduced for the image enhancement. Second, Geometric Graph-based Segmented Co-occurrence Feature Extraction model is applied to the preprocessed image to accurately segment the infected leaf areas and followed by which extracting the accurate infected leaf areas. Finally, Bernoulli Online Multiple Kernel Learning Classifier is applied for accurate plant leaf disease prediction with minimum classification error. The proposed method provides a significant refinement with respect to state-of-the-art methods. Even under complex background conditions, i.e., in the presence of noise, the averageaccuracy of the proposed method is said to be improved and hence paves mechanism for prediction of plant leaf disease in a significant manner. Experimentalresults exhibit the effectiveness of the proposed method in terms of computational overhead, accuracy, true positive rate and classification error respectively.

scholarly journals Using single-cell transcriptomics to understand functional states and interactions in microbial eukaryotes

2019 ◽  
Vol 374 (1786) ◽  
pp. 20190098 ◽  
Author(s):  
Chuan Ku ◽  
Arnau Sebé-Pedrós
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Interspecific Interactions ◽  
Single Cells ◽  
Life Cycles ◽  
Modern Biology ◽  
Microbial Eukaryotes ◽  
Eukaryotic Microorganisms

Understanding the diversity and evolution of eukaryotic microorganisms remains one of the major challenges of modern biology. In recent years, we have advanced in the discovery and phylogenetic placement of new eukaryotic species and lineages, which in turn completely transformed our view on the eukaryotic tree of life. But we remain ignorant of the life cycles, physiology and cellular states of most of these microbial eukaryotes, as well as of their interactions with other organisms. Here, we discuss how high-throughput genome-wide gene expression analysis of eukaryotic single cells can shed light on protist biology. First, we review different single-cell transcriptomics methodologies with particular focus on microbial eukaryote applications. Then, we discuss single-cell gene expression analysis of protists in culture and what can be learnt from these approaches. Finally, we envision the application of single-cell transcriptomics to protist communities to interrogate not only community components, but also the gene expression signatures of distinct cellular and physiological states, as well as the transcriptional dynamics of interspecific interactions. Overall, we argue that single-cell transcriptomics can significantly contribute to our understanding of the biology of microbial eukaryotes. This article is part of a discussion meeting issue ‘Single cell ecology’.

scholarly journals The temporal evolution of three-dimensional lightning parameters and their suitability for thunderstorm tracking and nowcasting

2013 ◽  
Vol 13 (1) ◽  
pp. 2217-2242
Author(s):  
V. K. Meyer ◽  
H. Höller ◽  
H. D. Betz
Keyword(s):  
Case Studies ◽  
Temporal Evolution ◽  
Discharge Rate ◽  
Single Cells ◽  
Life Cycles ◽  
Mature Stage ◽  
Cell Area ◽  
Flash Duration ◽  
Discharge Characteristics ◽  
Storm Cells

Abstract. Total lightning (TL) data has been found to provide valuable information about the internal dynamics of a thunderstorm allowing conclusions about its further development as well as indicating potential of thunderstorm-related severe weather at the ground. This paper investigates electrical discharge correlations of strokes and flashes with respect to the temporal evolution of thunderstorms in case studies as well as by statistical means. The recently developed algorithm li-TRAM (tracking and monitoring of lightning-cells, Meyer et al., 2012) has been employed to track and monitor thunderstorms based on three-dimensionally resolved TL lightning data provided as stroke events by the European lightning location network LINET. From statistical investigation of 863 suited thunderstorm life-cycles the cell area turned out to correlate well with (a) the total discharge rate, (b) the in-cloud (IC) discharge rate, and (c) the mean IC discharge height per lightning-cell as identified by li-TRAM. All three parameter correlations consistently show an abrupt change in discharge characteristics around a cell area of 170 km2. Statistical investigations supported by the comparison of three case studies – selected to represent a single storm, a multi-cell and a supercell – strongly suggest that the correlation functions include the temporal evolution as well as the storm type. With the help of volumetric radar data, it can also be suggested that the well defined break observed at 170 km2 marks the region, where the transition occurs from short-lived and rather simple structured single storm cells to better organized, more persistent, and more complex structured thunderstorm forms, e.g. multi-cells and super-cells. All three storm-types experience similar discharge characteristics during their growing and dissipating phases. However, while the poorly organized and short-lived cells preferentially remain small during a short mature phase, mainly the more persistent thunderstorm types develop to sizes above 170 km2 during a pronounced mature stage. At that stage they exhibit on average higher discharge rates at higher altitudes as compared with matured single-cells. With the maximum stroke distance set to 10 km and a flash duration set to 1 s the parameterisation functions found for the stroke rate as function of the cell area has been transformed to a flash rate. The presented study suggests that, with respect to the storm type, stroke and flash correlations can be parameterized. There is also strong evidence, that parameterization functions include the time parameter, so that altogether TL stroke information has good potential to pre-estimate the further evolution (nowcast) of a currently observed storm in an object-oriented thunderstorm nowcasting approach.

Life Cycles

2001 ◽  
Author(s):  
Jan A. Pechenik
Keyword(s):  
Life Cycle ◽  
Genetic Material ◽  
Life Cycles ◽  
Offspring Size ◽  
Free Living ◽  
Complex Life Cycles ◽  
Volume Number ◽  
Larval Stages ◽  
Underlying Mechanisms ◽  
Life Cycle Evolution

I have a Hardin cartoon on my office door. It shows a series of animals thinking about the meaning of life. In sequence, we see a lobe-finned fish, a salamander, a lizard, and a monkey, all thinking, “Eat, survive, reproduce; eat, survive, reproduce.” Then comes man: “What's it all about?” he wonders. Organisms live to reproduce. The ultimate selective pressure on any organism is to survive long enough and well enough to pass genetic material to a next generation that will also be successful in reproducing. In this sense, then, every morphological, physiological, biochemical, or behavioral adaptation contributes to reproductive success, making the field of life cycle evolution a very broad one indeed. Key components include mode of sexuality, age and size at first reproduction (Roff, this volume), number of reproductive episodes in a lifetime, offspring size (Messina and Fox, this volume), fecundity, the extent to which parents protect their offspring and how that protection is achieved, source of nutrition during development, survival to maturity, the consequences of shifts in any of these components, and the underlying mechanisms responsible for such shifts. Many of these issues are dealt with in other chapters. Here I focus exclusively on animals, and on a particularly widespread sort of life cycle that includes at least two ecologically distinct free-living stages. Such “complex life cycles” (Istock 1967) are especially common among amphibians and fishes (Hall and Wake 1999), and within most invertebrate groups, including insects (Gilbert and Frieden 1981), crustaceans, bivalves, gastropods, polychaete worms, echinoderms, bryozoans, and corals and other cnidarians (Thorson 1950). In such life cycles, the juvenile or adult stage is reached by metamorphosing from a preceding, free-living larval stage. In many species, metamorphosis involves a veritable revolution in morphology, ecology, behavior, and physiology, sometimes taking place in as little as a few minutes or a few hours. In addition to the issues already mentioned, key components of such complex life cycles include the timing of metamorphosis (i.e., when it occurs), the size at which larvae metamorphose, and the consequences of metamorphosing at particular times or at particular sizes. The potential advantages of including larval stages in the life history have been much discussed.

Plant Disease Detection Using Machine Learning Approaches

2021 ◽  
pp. 122-130
Author(s):  
Sukanta Ghosh ◽  
Shubhanshu Arya ◽  
Amar Singh
Keyword(s):  
Plant Disease ◽  
Crop Yields ◽  
Detection System ◽  
Visual Observation ◽  
Plant Diseases ◽  
Disease Detection ◽  
Learning Approaches ◽  
Factors Affecting ◽  
Plant Leaf ◽  
Market Situation

Agricultural production is one of the main factors affecting a country's domestic market situation. Many problems are the reasons for estimating crop yields, which vary in different parts of the world. Overuse of chemical fertilizers, uneven distribution of rainfall, and uneven soil fertility lead to plant diseases. This forces us to focus on effective methods for detecting plant diseases. It is important to find an effective plant disease detection technique. Plants need to be monitored from the beginning of their life cycle to avoid such diseases. Observation is a kind of visual observation, which is time-consuming, costly, and requires a lot of experience. For speeding up this process, it is necessary to automate the disease detection system. A lot of researchers have developed plant leaf detection systems based on various technologies. In this chapter, the authors discuss the potential of methods for detecting plant leaf diseases. It includes various steps such as image acquisition, image segmentation, feature extraction, and classification.

scholarly journals Emergent multicellular life cycles in filamentous bacteria owing to density-dependent population dynamics

2011 ◽  
Vol 8 (65) ◽  
pp. 1772-1784 ◽  
Author(s):  
Valentina Rossetti ◽  
Manuela Filippini ◽  
Miroslav Svercel ◽  
A. D. Barbour ◽  
Homayoun C. Bagheri
Keyword(s):  
Cell Division ◽  
Population Growth ◽  
Generation Time ◽  
Single Cells ◽  
Bacterial Species ◽  
Life Forms ◽  
Life Cycles ◽  
Morphological Properties ◽  
Filamentous Bacteria ◽  
Growth Phases

Filamentous bacteria are the oldest and simplest known multicellular life forms. By using computer simulations and experiments that address cell division in a filamentous context, we investigate some of the ecological factors that can lead to the emergence of a multicellular life cycle in filamentous life forms. The model predicts that if cell division and death rates are dependent on the density of cells in a population, a predictable cycle between short and long filament lengths is produced. During exponential growth, there will be a predominance of multicellular filaments, while at carrying capacity, the population converges to a predominance of short filaments and single cells. Model predictions are experimentally tested and confirmed in cultures of heterotrophic and phototrophic bacterial species. Furthermore, by developing a formulation of generation time in bacterial populations, it is shown that changes in generation time can alter length distributions. The theory predicts that given the same population growth curve and fitness, species with longer generation times have longer filaments during comparable population growth phases. Characterization of the environmental dependence of morphological properties such as length, and the number of cells per filament, helps in understanding the pre-existing conditions for the evolution of developmental cycles in simple multicellular organisms. Moreover, the theoretical prediction that strains with the same fitness can exhibit different lengths at comparable growth phases has important implications. It demonstrates that differences in fitness attributed to morphology are not the sole explanation for the evolution of life cycles dominated by multicellularity.

SOYBEAN LEAF DISEASES DETECTION AND CLASSIFICATION USING RECENT IMAGE PROCESSING TECHNIQUES

2020 ◽  
Vol 8 (3) ◽  
pp. 01-08
Author(s):  
Arpan Singh Rajput ◽  
Shailja Shukla ◽  
S. S. Thakur
Keyword(s):  
Image Processing ◽  
Early Stage ◽  
Weather Condition ◽  
Plant Diseases ◽  
Soybean Plant ◽  
Plant Leaf ◽  
Image Processing Techniques ◽  
Soybean Leaf ◽  
Processing Techniques ◽  
Soybean Diseases

Purpose: India is an agricultural country and soybean production is one of the major sources of earning. Due to the major factors like diseases, pest attacks, and sudden changes in the weather condition, the productivity of the soybean crop decreases. Automatic detection of soybean plant diseases is essential to detect the symptoms of soybean diseases as early as they appear on the growing stage. This paper proposed a methodology for the analysis and detection of soybean plant leaf diseases using recent digital image processing techniques. In this paper, experimental results demonstrate that the proposed method can successfully detect and classify the major soybean diseases. Methodology: MatLab 18a is used for the simulation for the result and machine learning-based recent image processing techniques for the detection of the soybean leaf disease. Main Findings: The main finding of this work is to create the soybean leaf database which includes healthy and unhealthy leaves and achieved 96 percent accuracy in this work using the proposed methodology. Applications of this study: To detect soybean plant leaf diseases in the early stage in Agricultural. The novelty of this study: Self-prepared database of healthy and unhealthy images of soybean leaf with the proposed algorithm.

Dynamic Behavior and In-Situ Diagnostics of PEFCs

2006 ◽  
Author(s):  
Kaspar Andreas Friedrich ◽  
Till Kaz ◽  
Stefan Scho¨nbauer ◽  
Heinz Sander
Keyword(s):  
Fuel Cell ◽  
Density Distribution ◽  
Current Density ◽  
Single Cells ◽  
Current Density Distribution ◽  
Bipolar Plate ◽  
Life Cycles ◽  
Operating Conditions ◽  
Bipolar Plates

During fuel cell operation the electrochemical activity often is not homogenous over the electrode area. This may be caused by an non-uniform water content in the membrane, an inhomogeneous temperature distribution, and reactant gradients in the cell. Consequently a variation of the current density over the cell area occurs which tends to result in inferior performance. For in situ measurements of the current density distribution in fuel cell stacks a segmented bipolar plate was developed. The segmented bipolar plate was first tested in single cells with stack endplates to verify the function of all components. The tests showed that the measurement tool works very reliable and accurate. The insight in an operating fuel cell stack via current density distribution measurement is very helpful to investigate interactions between cells. Results can be used to validate models and to optimise stack components, e.g. flow field and manifold design, as well as to detect the best stack operating conditions. By applying segmented bipolar plates as sensor plates for stack system controls an improved performance, safe operation and longer life cycles can be achieved. The developed segmented bipolar plates with integrated current sensors were used to assemble a short stack consisting of 3 cells; each of them having an active area of 25cm2 divided into 49 segments. The design of the bipolar plate proofed very suitable for easy assembling of single cells and stacks. First measurement results show that different current distributions can appear in the cells and these can vary from cell to cell, depending on the operating conditions of the stack. Electrical coupling between the cells was investigated and found to be only marginal for the assembly used.

scholarly journals Plant leaf Damage Detection using Segmentation

2019 ◽  
Vol 9 (2S3) ◽  
pp. 238-243
Keyword(s):  
Agricultural Production ◽  
Primary Source ◽  
Plant Diseases ◽  
Leaf Damage ◽  
Agricultural System ◽  
Technical Knowledge ◽  
Plant Leaves ◽  
Employment Opportunities ◽  
Plant Leaf ◽  
Advanced Technologies

A primary source of livelihood is agriculture. In developing country like India, wide-ranging employment opportunities are provided by Agriculture for the villagers. Various crops are included in the agricultural system of India and 70% of the population depends upon agriculture as reported by survey. Because of lagging in technical knowledge, manual cultivation is adopted by majority of the Indian farmers. The kind of crops that grows well on their land is unaware by the farmers. The agriculture production is affected by the heterogeneous diseases that affect the plant leaves and result in the productive loss. Moreover, the quality as well as quantity of the agricultural production is reduced by it. A key role is played by the leaves in the rapid growth of the plants and production of crops. The identification of diseases related to plant leaf is a difficult task for the farmers and for the researchers. At present, various pesticides were sprayed on the plants that directly or indirectly affect the human health and the economy. Various methods must be adopted for detecting these kinds of plant diseases. This paper presents a review of various plant diseases and several advanced technologies in detecting the diseases.

scholarly journals Detection and Recognition of Paddy Plant Leaf Diseases using Machine Learning Technique

2020 ◽  
Vol 9 (5) ◽  
pp. 634-638
Keyword(s):  
Machine Learning ◽  
Plant Diseases ◽  
Blast Disease ◽  
Brown Spot ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
World Population ◽  
Main Concern ◽  
Plant Leaf ◽  
Agriculture Industry

Rice is one of the important food crops and the most staple food for half of the world population. Farmers are often faces several obstacles in paddy production because of various paddy leaf diseases. As a result, rice production is extensively reduced. For finding the paddy plant leaf diseases, there are many techniques are available in the computer vision-based area. Now, it is the main concern to fast and accurate recognition of paddy plant diseases in the initial stage. For this reason, we proposed a better approach for early paddy plant leaf disease detection by using simple image processing and machine learning techniques. There are four types of paddy leaf diseases are highlighted in this paper; which are Brown Spot, Sheath Blight, Blast Disease and Narrow Brown Spot. To do this, at first the required normal and diseased paddy plant leaf images are captured directly from different paddy fields. The unnecessary background of the leaves images are eliminated by using mask in the pre-processing section. Then output is fed into the segmentation part where K-means clustering is used to separate the normal portion and diseased portion of the leaf images. Finally, the mentioned diseases are classified using Support Vector Machine (SVM) algorithm. The accuracy of the system is 94%. This technique can be also applied anywhere in the agriculture industry for plant leaf diseases detection.

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