Identification of Plant Diseases: A short review

2021 ◽  
Vol 2 (3) ◽  
pp. 1-10
Author(s):  
Tirtha Tarafdar
Keyword(s):  
Short Review ◽  
Plant Diseases ◽  
Disease Detection ◽  
Bacterial Diseases ◽  
The Common ◽  
Potential Loss

As per FAO (2000) reports, 67% of population are involved in Agriculture, and alone contributing to 34% of overall GDP. Diseases in Plants cases major losses therefore it’s quite evident that proper monitoring and disease detection is necessary for sustainability. According to several reports, in the period 2001-2003, Global potential loss varied to about 29% in soyabean, 50% in wheat to about 80% in Cotton. This short synopsis will be acting a small referral piece of document to give an idea about the common Fungal, Viral, Bacterial diseases. This shall also be discussing about some of the conventional techniques for disease detection in plants viz.; Microarray, PCR, ELISA etc.

scholarly journals Deep Learning Models for Leaf Disease Detection for Crops in Agriculture Field : A Survey

2020 ◽  
pp. 104-110
Author(s):  
Sunidhi Shrivastava ◽  
Pankaj Gugnani ◽  
Neha Garg
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Food Production ◽  
Plant Diseases ◽  
Disease Detection ◽  
Comparison Study ◽  
Learning Techniques ◽  
The Common ◽  
Common Problems ◽  
Processing Techniques

Crop and plant Diseases are the common problems in the food production fields. This is necessary for the improvement of the food production in agriculture and for fulfills the need of the society to solve these problems. In India most of the part of the country based on the production of food as a tradition. To solve these problems some advanced image processing, machine learning, computer vision etc. advancements included. This survey research on the identification of all that kind of technologies and the existing work also has done using them. How many kinds of models are proposed and what amount of success they have achieved by utilizing them. Image processing techniques provides the automatic disease detection technique to detect and identify the diseases in plants. Deep learning techniques are very good at prediction of the growth of plan and possibility of having disease within them. A comparison study also performed of several machine and deep learning techniques based on their accuracy.

scholarly journals Tomato Leaf Disease Detection using Back Propagation Neural Network

2020 ◽  
Vol 9 (8) ◽  
pp. 529-538
Keyword(s):  
Neural Network ◽  
Traditional Approach ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Plant Diseases ◽  
Disease Detection ◽  
Large Drop ◽  
Tomato Crop ◽  
Leaf Disease ◽  
Area Of Interest

Most of the Indian economy rely on agriculture, so identifying any diseases crop in early stages is very crucial as these diseases in plants causes a large drop in the production and economy of the farmers and therefore, degradation of the crop which emphasize on the early detection of the plant disease. These days, detection of plant diseases has become a hot topic in the area of interest of the researchers. Farmers followed a traditional approach for identifying and detecting diseases in plants with naked eyes, which didn’t help much as the disease may have caused much damage to the plant. Tomato crop shares a huge portion of Indian cuisine and can be prone to various Air-Bourne and Soil-Bourne diseases. In this paper, we tried to automate the Tomato Plant Leaf disease detection by studying the various features of diseased and healthy leaves. The technique used is pattern recognition using Back-Propagation Neural network and comparing the results of this neural network on different features set. Several steps included are image acquisition, image pre-processing, features extraction, subset creation and BPNN classification.

scholarly journals A Review on Advances in Automated Plant Disease Detection

2021 ◽  
Vol 11 (4) ◽  
pp. 251-264
Author(s):  
Radhika Bhagwat ◽  
Yogesh Dandawate
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Detection Methods ◽  
Economic Losses ◽  
Disease Detection ◽  
Range Images

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.

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 Isostatic postglacial rebound over Fennoscandia with a self-gravitating spherical visco-elastic Earth model

1996 ◽  
Vol 23 ◽  
pp. 318-327 ◽  
Author(s):  
E. Le Meur
Keyword(s):  
Present Model ◽  
Short Review ◽  
Earth Model ◽  
Postglacial Rebound ◽  
Mantle Viscosity ◽  
Surface Displacements ◽  
Grounding Line ◽  
The Common ◽  
Characteristic Features ◽  
Sensitive Issues

Accounting for isostasy in glaciological models has always been a necessity but these models mostly use very simple parameterizations (Le Meur and Huybrechts, 1996). The need for more realistic isostatic parameterizations rapidly became apparent, especially in the treatment of bedrock-sensitive issues such as the grounding-line migration (Huybrechts, 1990a, b). To this end, a rather sophisticated Earth model, avoiding most of the common assumptions, has been developed and is presented here. The two key groups of parameters, to which the model is most sensitive, are the Earth properties and the rheological law used for the mantle. The aim of this paper is first to justify the use of Maxwell rheology for the mantle and then to tune the most sensitive Earth parameter, namely the mantle viscosity, in order to match the numerous present-day uplift data over Fennoscandia. So, in the first instance, a short review of the different available rheologies is presented and discussed. The visco-elastic theory, as well as the mathematical background used in the present model, is also briefly sketched. Secondly, a glacial scenario over Fennoscandia served as an input for the model in a calibration test on the mantle-viscosity values. The degree of agreement of the model outputs with the present-day measurements gives a reference set of Green functions, to which one can reasonably refer when modelling the isostatic response over areas where such a control is not possible (Le Meur and Huybrechts, 1996). Finally, a closer look to the time-dependent surface displacements will confirm the ability for the model to reproduce correctly the main postglacial rebound characteristic features.

The Productivity of Labour in Roman Agriculture

Antiquity ◽  
1965 ◽  
Vol 39 (154) ◽  
pp. 102-107 ◽  
Author(s):  
K. D. White
Keyword(s):  
Economic History ◽  
Crop Yields ◽  
Short Review ◽  
Precise Information ◽  
De Re ◽  
Ancient Agriculture ◽  
Agricultural Efficiency ◽  
Average Return ◽  
General Average ◽  
The Common

Some years ago, in a short review of some of the major questions concerning agricultural efficiency in Roman times, I pointed out that we do not possess the materials on which to base an accurate computation. In attempting to make an assessment of agricultural efficiency we should require as a minimum basis a body of statistical information on the following points: first, the numbers of persons engaged; second, average yields per acre of certain crops for comparison with average yields in other producing countries; and third, statistics of output measured in man-hours according to recognized methods of determining the productivity of labour. The type of dficulty mentioned here is not confined to research in ancient agriculture; lack of records, and paucity of precise information, make investigation difficult in almost every department of ancient economic history. But lack of precise information has not deterred historians from making rough analyses and generalizations. The evidence on wheat-yields showed, inter alia, that it is not legitimate to use Columella’s general average return on Italian wheat of four-fold as evidence of a generally low standard of productivity in cereals (De Re Rust., III, iii, 4). So far as crop-yields are concerned, the common postulate of a low level of agricultural technique cannot be upheld.

scholarly journals Tunnel Monitoring and Measuring System Using Mobile Laser Scanning: Design and Deployment

2020 ◽  
Vol 12 (4) ◽  
pp. 730 ◽  
Author(s):  
Haili Sun ◽  
Zhengwen Xu ◽  
Lianbi Yao ◽  
Ruofei Zhong ◽  
Liming Du ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Development Trend ◽  
Image Resolution ◽  
Measuring System ◽  
Deformation Analysis ◽  
Disease Detection ◽  
Environmental Disease ◽  
The Common ◽  
Tunnel Monitoring ◽  
The Development Trend

The common statistical methods for rail tunnel deformation and disease detection usually require a large amount of equipment and manpower to achieve full section detection, which are time consuming and inefficient. The development trend in the industry is to use laser scanning for full section detection. In this paper, a design scheme for a tunnel monitoring and measuring system with laser scanning as the main sensor for tunnel environmental disease and deformation analysis is proposed. The system provides functions such as tunnel point cloud collection, section deformation analysis, dislocation analysis, disease extraction, tunnel and track image generation, roaming video generation, etc. Field engineering indicated that the repeatability of the convergence diameter detection of the system can reach ±2 mm, dislocation repeatability can reach ±3 mm, the image resolution is about 0.5 mm/pixel in the ballast part, and the resolution of the inner wall of the tunnel is about 1.5 mm/pixel. The system can include human–computer interaction to extract and label diseases or appurtenances and support the generation of thematic disease maps. The developed system can provide important technical support for deformation and disease detection of rail transit tunnels.

scholarly journals Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 939 ◽  
Author(s):  
Marko Arsenovic ◽  
Mirjana Karanovic ◽  
Srdjan Sladojevic ◽  
Andras Anderla ◽  
Darko Stefanovic
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Real Life ◽  
Weather Conditions ◽  
Plant Diseases ◽  
Disease Classification ◽  
Generative Adversarial Networks ◽  
Disease Detection ◽  
Single Leaf ◽  
The Impact

Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant disease detection models are presented and discussed in this paper. Furthermore, a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images. Images were taken in various weather conditions, at different angles, and daylight hours with an inconsistent background mimicking practical situations. Two approaches were used to augment the number of images in the dataset: traditional augmentation methods and state-of-the-art style generative adversarial networks. Several experiments were conducted to test the impact of training in a controlled environment and usage in real-life situations to accurately identify plant diseases in a complex background and in various conditions including the detection of multiple diseases in a single leaf. Finally, a novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment. The trained model achieved an accuracy of 93.67%.

scholarly journals Coronavirus Disease 2019 (COVID–19): A Short Review on Hematological Manifestations

Pathogens ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 493 ◽  
Author(s):  
Artur Słomka ◽  
Mariusz Kowalewski ◽  
Ewa Żekanowska
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Disease Progression ◽  
Present Article ◽  
Disease Severity ◽  
Hematological Parameters ◽  
Short Review ◽  
Global Pandemic ◽  
The Common ◽  
Hematological Manifestations ◽  
Effective Treatments

Infection with severe acute respiratory syndrome coronavirus 2 (SARS–CoV–2) is a rapidly spreading and devastating global pandemic. Many researchers are attempting to clarify the mechanisms of infection and to develop a drug or vaccine against the virus, but there are still no proven effective treatments. The present article reviews the common presenting hematological manifestations of coronavirus disease 2019 (COVID–19). Elucidating the changes in hematological parameters in SARS–CoV–2 infected patients could help to understand the pathophysiology of the disease and may provide early clues to diagnosis. Several studies have shown that hematological parameters are markers of disease severity and suggest that they mediate disease progression.

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