Application of Fuzzy Logic in Plant Disease Management

2020 ◽  
pp. 261-302
Author(s):  
Reva Nagi ◽  
Sanjaya Shankar Tripathy
Keyword(s):  
Fuzzy Logic ◽  
Feature Extraction ◽  
Disease Management ◽  
Disease Severity ◽  
Early Stage ◽  
Advanced Stage ◽  
Severity Estimation ◽  
Infected Area ◽  
Plant Disease Management ◽  
Leaf Image

The timely detection of the infection in plants and its severity is a major concern for the farmers. Although various techniques have been employed to identify and estimate the severity of infection, they generally use a fixed threshold to segment the infected areas from the leaf image. Such methods define the participation of a pixel, as part of the infected area, in the form of a classical or crisp set. Use of fuzzy logic in feature extraction, grading the disease post identification, and estimating the disease severity are seen as rapidly growing techniques. Using fuzzy logic, the infected area is calculated by considering the degree of contribution provided by neighboring pixels to the current pixel. The severity estimation is performed on the basis of the infected area and the number of lesions in the leaf image. Depending on the amount of infection, severity has been classified into early, middle, later, and advanced stage. The proposed technique will help the farmers to identify the disease class at an early stage.

Cotton Leaf Disease Detection by Feature Extraction

2016 ◽  
pp. 89-104
Author(s):  
Savita N. Ghaiwat ◽  
Parul Arora
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Feature Extraction ◽  
Early Stage ◽  
Cotton Leaf ◽  
Control Methods ◽  
The Neural Network ◽  
The World ◽  
Leaf Image

Cotton leaf diseases have occurred all over the world, including India. They adversely affect cotton quality and yield. Technology can help in identifying disease in early stage so that effective treatment can be given immediately. Now, the control methods rely mainly on artificial means. This paper propose application of image processing and machine learning in identifying three cotton leaf diseases through feature extraction. Using image processing, 12 types of features are extracted from cotton leaf image then the pattern was learned using BP Neural Network method in machine learning process. Three diseases have been diagnosed, namely Powdery mildew, Downy mildew and leafminer. The Neural Network classification performs well and could successfully detect and classify the tested disease.

scholarly journals An Algorithm for Severity Estimation of Plant Leaf Diseases by the Use of Colour Threshold Image Segmentation and Fuzzy Logic Inference: A Proposed Algorithm to Update a “Leaf Doctor” Application

2019 ◽  
Vol 1 (2) ◽  
pp. 205-219 ◽  
Author(s):  
Malusi Sibiya ◽  
Mbuyu Sumbwanyambe
Keyword(s):  
Fuzzy Logic ◽  
Image Segmentation ◽  
Disease Severity ◽  
Precision Agriculture ◽  
Inference Rules ◽  
Plant Leaf ◽  
Fuzzy Logic Inference ◽  
Severity Estimation ◽  
Current Algorithm ◽  
Logic Inference

This paper explains a proposed algorithm for severity estimation of plant leaf diseases by using maize leaf diseased samples. In the literature, a number of researchers have addressed the problem of plant leaf disease severity estimation, but a few, such as Sannakki et al., have used fuzzy logic to determine the severity estimations of the plant leaf diseases. The present paper aims to update the current algorithm used in the “Leaf Doctor” application that is used to estimate the severities of the plant leaf diseases by introducing the benefits of fuzzy logic decision making rules. This method will contribute to precision agriculture technology as it introduces an algorithm that may be embedded in smartphone devices and used in applications, such as a “Leaf Doctor” application. The applications designed based on the algorithm proposed in this study will help users who are inexperienced and not plant pathologists understand the level of the estimated disease severity. The use of fuzzy logic inference rules along with image segmentation determines the novelty of this approach in comparison with the available methods in the literature.

A Smart Procedure for Severity Estimation of the Maize Leaf Diseases by the Use of Image Segmentation and Fuzzy Logic Inference

2019 ◽  
Author(s):  
Malusi Sibiya ◽  
Mbuyu Sumbwanyambe
Keyword(s):  
Fuzzy Logic ◽  
Image Segmentation ◽  
Disease Severity ◽  
Current Knowledge ◽  
Maize Leaf ◽  
Plant Leaf ◽  
Fuzzy Logic Inference ◽  
Severity Estimation ◽  
Detailed Procedure ◽  
Logic Inference

This paper explains a proposed taxonomic and smart procedure for severity estimation of the maize leaf diseases. However, few researchers have addressed the problem of disease severity estimation, but none have used a detailed procedure by the use of fuzzy logic. The present paper aims to broaden current knowledge of estimating the severity of plant leaf diseases by using fuzzy logic and image segmentation.

Efficacy of the Liver US Score in Discriminating Early Stage Cirrhosis (F4) from Advanced Stage Chronic Hepatitis (F3)

Choonpa Igaku ◽  
2006 ◽  
Vol 33 (6) ◽  
pp. 655-663 ◽  
Author(s):  
Tetsuya NISHIURA ◽  
Hideaki WATANABE ◽  
Yoshihiko KOUNO ◽  
Masahiro ITO ◽  
Kazuyuki OOHATA ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Early Stage ◽  
Advanced Stage

scholarly journals Primary Care Networks and Starfield’s 4Cs: A Case for Enhanced Chronic Disease Management

2021 ◽  
Vol 18 (6) ◽  
pp. 2926
Author(s):  
Chuan De Foo ◽  
Shilpa Surendran ◽  
Geronimo Jimenez ◽  
John Pastor Ansah ◽  
David Bruce Matchar ◽  
...  
Keyword(s):  
Primary Care ◽  
Chronic Disease ◽  
Disease Management ◽  
Chronic Disease Management ◽  
Early Stage ◽  
Healthcare Delivery ◽  
Stage Of Development ◽  
Facilitators And Barriers ◽  
Design Options ◽  
First Contact

The primary care network (PCN) was implemented as a healthcare delivery model which organises private general practitioners (GPs) into groups and furnished with a certain level of resources for chronic disease management. A secondary qualitative analysis was conducted with data from an earlier study exploring facilitators and barriers GPs enrolled in PCN’s face in chronic disease management. The objective of this study is to map features of PCN to Starfield’s “4Cs” framework. The “4Cs” of primary care—comprehensiveness, first contact access, coordination and continuity—offer high-quality design options for chronic disease management. Interview transcripts of GPs (n = 30) from the original study were purposefully selected. Provision of ancillary services, manpower, a chronic disease registry and extended operating hours of GP practices demonstrated PCN’s empowering features that fulfil the “4Cs”. On the contrary, operational challenges such as the lack of an integrated electronic medical record and disproportionate GP payment structures limit PCNs from maximising the “4Cs”. However, the enabling features mentioned above outweighs the shortfalls in all important aspects of delivering optimal chronic disease care. Therefore, even though PCN is in its early stage of development, it has shown to be well poised to steer GPs towards enhanced chronic disease management.

Development of carrier-free self-assembled nanoparticles based on fenhexamid and polyhexamethylene biguanide for sustainable plant disease management

2021 ◽  
Vol 23 (6) ◽  
pp. 2531-2540
Author(s):  
Gang Tang ◽  
Yuyang Tian ◽  
Junfan Niu ◽  
Jingyue Tang ◽  
Jiale Yang ◽  
...  
Keyword(s):  
Disease Management ◽  
Plant Disease ◽  
Polyhexamethylene Biguanide ◽  
Adverse Impacts ◽  
Pesticide Formulations ◽  
Carrier Free ◽  
Self Assembled ◽  
Plant Disease Management

The utilization of nanotechnology for the design of pesticide formulations has enormous potential to enhance the efficiency of pesticides and reduce their adverse impacts on the environment

scholarly journals Does Time-to-Chemotherapy after Primary Complete Macroscopic Cytoreductive Surgery Influence Prognosis for Patients with Epithelial Ovarian Cancer? A Study of the FRANCOGYN Group

2021 ◽  
Vol 10 (5) ◽  
pp. 1058
Author(s):  
Grégoire Rocher ◽  
Thomas Gaillard ◽  
Catherine Uzan ◽  
Pierre Collinet ◽  
Pierre-Adrien Bolze ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Cytoreductive Surgery ◽  
Retrospective Cohort ◽  
Early Stage ◽  
Cohort Analysis ◽  
Recurrence Free Survival ◽  
Advanced Stage ◽  
Free Survival ◽  
Retrospective Cohort Analysis

To determine if the time-to-chemotherapy (TTC) after primary macroscopic complete cytoreductive surgery (CRS) influences recurrence-free survival (RFS) and overall survival (OS) in patients with epithelial ovarian cancer (EOC). We conducted an observational multicenter retrospective cohort analysis of women with EOC treated from September 2006 to November 2016 in nine institutions in France (FRANCOGYN research group) with maintained EOC databases. We included women with EOC (all FIGO stages) who underwent primary complete macroscopic CRS prior to platinum-based adjuvant chemotherapy. Two hundred thirty-three patients were included: 73 (31.3%) in the early-stage group (ESG) (FIGO I-II), and 160 (68.7%) in the advanced-stage group (ASG) (FIGO III-IV). Median TTC was 43 days (36–56). The median OS was 77.2 months (65.9–106.6). OS was lower in the ASG when TTC exceeded 8 weeks (70.5 vs. 59.3 months, p = 0.04). No impact on OS was found when TTC was below or above 6 weeks (78.5 and 66.8 months, respectively, p = 0.25). In the whole population, TTC had no impact on RFS or OS. None of the factors studied were associated with an increase in TTC. Chemotherapy should be initiated as soon as possible after CRS. A TTC greater than 8 weeks is associated with poorer OS in patients with advanced stage EOC.

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