Photodynamic control of citrus crop diseases

2021 ◽  
Vol 37 (12) ◽  
Author(s):  
Joana R. M. Ferreira ◽  
Isabel N. Sierra-Garcia ◽  
Samuel Guieu ◽  
Artur M. S. Silva ◽  
Raquel Nunes da Silva ◽  
...  
Keyword(s):  
Crop Diseases ◽  
Photodynamic Control

Digital Image Processing for Detecting and Classifying Plant Diseases

2017 ◽  
Vol 2 (11) ◽  
pp. 1-7
Author(s):  
Izay A. ◽  
Onyejegbu L. N.
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Programming Language ◽  
Design Methodology ◽  
Fuzzy Logic Controller ◽  
Object Oriented ◽  
Plant Diseases ◽  
Analysis And Design ◽  
Crop Diseases ◽  
Growing Population

Agriculture is the backbone of human sustenance in this world. With growing population, there is need for increased productivity in agriculture to be able to meet the demands. Diseases can occur on any part of a plant, but in this paper only the symptoms in the fruits of a plant is considered using segmentation algorithm and edge/ sizing detectors. We also looked at image processing using fuzzy logic controller. The system was designed using object oriented analysis and design methodology. It was implemented using MySQL for the database, and PHP programming language. This system will be of great benefit to farmers and will encourage them in investing their resources since crop diseases can be detected and eliminated early.

scholarly journals Bodipy-Loaded Micelles Based on Polylactide as Surface Coating for Photodynamic Control of Staphylococcus aureus

Coatings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 223
Author(s):  
Enrico Caruso ◽  
Viviana Teresa Orlandi ◽  
Miryam Chiara Malacarne ◽  
Eleonora Martegani ◽  
Chiara Scanferla ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Pathogenic Bacteria ◽  
Surface Coatings ◽  
Supramolecular System ◽  
Amphiphilic Copolymers ◽  
Oxygen Penetration ◽  
Short Period ◽  
Excellent Activity ◽  
Coating Matrix ◽  
Photodynamic Control

Decontaminating coating systems (DCSs) represent a challenge against pathogenic bacteria that may colonize hospital surfaces, causing several important infections. In this respect, surface coatings comprising photosensitizers (PSs) are promising but still controversial for several limitations. PSs act through a mechanism of antimicrobial photodynamic inactivation (aPDI) due to formation of reactive oxygen species (ROS) after light irradiation. However, ROS are partially deactivated during their diffusion through a coating matrix; moreover, coatings should allow oxygen penetration that in contact with the activated PS would generate 1O2, an active specie against bacteria. In the attempt to circumvent such constraints, we report a spray DCS made of micelles loaded with a PS belonging to the BODIPY family (2,6-diiodo-1,3,5,7-tetramethyl-8-(2,6-dichlorophenyl)-4,4′-difluoroboradiazaindacene) that is released in a controlled manner and then activated outside the coating. For this aim, we synthesized several amphiphilic copolymers (mPEG–(PLA)n), which form micelles, and established the most stable supramolecular system in terms of critical micelle concentration (CMC) and ∆Gf values. We found that micelles obtained from mPEG–(PLLA)2 were the most thermodynamically stable and able to release BODIPY in a relatively short period of time (about 80% in 6 h). Interestingly, the BODIPY released showed excellent activity against Staphylococcus aureus even at micromolar concentrations.

Synthesis of Carbamic Esters Derivatives of Itols:  Antifungal Activity against Various Crop Diseases

1997 ◽  
Vol 45 (1) ◽  
pp. 3-6 ◽  
Author(s):  
Christophe Len ◽  
Denis Postel ◽  
Gino Ronco ◽  
Pierre Villa ◽  
Christel Goubert ◽  
...  
Keyword(s):  
Antifungal Activity ◽  
Crop Diseases ◽  
Carbamic Esters ◽  
Derivatives Of

A Real-Time Data Analytics-Based Crop Diseases Recognition System

2019 ◽  
pp. 245-256
Author(s):  
Chiranji Lal Chowdhary ◽  
Rachit Bhalla ◽  
Esha Kumar ◽  
Gurpreet Singh ◽  
K. Bhagyashree ◽  
...  
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Recognition System ◽  
Time Data ◽  
Real Time Data ◽  
Crop Diseases

scholarly journals An Enhanced Faster-RCNN Based Deep Learning Model for Crop Diseases Detection and Classification

2019 ◽  
Vol 8 (6) ◽  
pp. 4714-4719
Keyword(s):  
Neural Network ◽  
Vital Role ◽  
Convolution Neural Network ◽  
Plant Phenotyping ◽  
Early Disease ◽  
Early Disease Detection ◽  
Proposed Model ◽  
Crop Diseases ◽  
Deep Learning Model ◽  
State Of Art

Recently Plant phenotyping has gained the attention of many researchers such that it plays a vital role in the context of enhancing agricultural productivity. Indian economy highly depends on agriculture and this factor elevates the importance of early disease detection of the crops within the agricultural fields. Addressing this problem several researchers have proposed Computer Vision and Pattern recognition based mechanisms through which they have attempted to identify the infected crops in the early stages.in this scenario, CNN convolution neural network-based architecture has demonstrated exceptional performance when compared with state-of-art mechanisms. This paper introduces an enhanced RCNN recurrent convolution neural network-based architecture that enhances the prediction accuracy while detecting the crop diseases in early stages. Based on the simulative studies is observed that the proposed model outperforms when compared with CNN and other state-of-art mechanisms.

scholarly journals Role of Machine Learning Approach for Detection and Classification of Diseases in Cotton Plant

2021 ◽  
Vol 12 (5) ◽  
pp. 810-817
Author(s):  
Sandhya N. dhage, Dr. Vijay Kumar Garg
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Cotton Plant ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Early Detection Of Disease ◽  
Cotton Crop ◽  
Machine Learning Approach ◽  
Crop Diseases

Qualitative and quantitative agricultural production leads to economic benefits which can be achieved by periodic monitoring of crop, detection and prevention of crop diseases and insects. Quality of crop production is reduced by pest infection and crop diseases. Existing measures involves manual detection of cotton diseases by farmers and experts which requires  regular monitoring and detection manifest at middle to later stage of infection which causes many disadvantages such as becoming  too late for diseases to be cured.  Lack of early detection of diseases causes the diseases to be spread in nearby crops in the field and also spraying of pesticides is done on entire field for minimizing the infection of disease. The main goal of proposed research topic is to find the solution to the agriculture problem which involves detecting disease in cotton plant at early stage and classify the disease based on symptoms. Early detection of disease at an early stage prevent it from spreading to another area and preventive measures can be taken by farmers by spraying pesticides to control its growth which helps to increase the cotton yield production. Automatic identification of the different diseases affecting cotton crop will give many benefits to the farmers so that time, money will be saved and also gives healthy life to the crop. The contribution of this paper is to present the machine learning approach used for cotton crop disease diagnosis and classification.

Overview of Pasture and Forage Crop Diseases in Australia

1996 ◽  
Author(s):  
Sukumar Chakraborty ◽  
John A.G. Irwin ◽  
Gordon M. Murray ◽  
Robert D. Davis
Keyword(s):  
Forage Crop ◽  
Crop Diseases

scholarly journals Applying the Method of Paired Comparison Technique to Determine the Most Critical Issue Associated with the Livelihood Security of the Tribal Farmers of Meghalaya

2019 ◽  
pp. 1-7
Author(s):  
Deepa Thangjam ◽  
J. K. Chauhan ◽  
R. J. Singh ◽  
Ram Singh ◽  
L. Hemochandra ◽  
...  
Keyword(s):  
Survey Data ◽  
Paired Comparison ◽  
Critical Issue ◽  
Graduate Studies ◽  
Post Graduate ◽  
Livelihood Security ◽  
Pest Infestation ◽  
Crop Diseases ◽  
Tribal Farmers ◽  
Comparison Technique

Ensuring livelihood security of the tribal farmers of Meghalaya has been the main focus of the policymakers. To accelerate the process, it is necessary to identify the most serious issue encountered by the farmers of the region. This paper presents a list of agricultural issues associated with the livelihood security of the farmers. Using survey data from beneficiary farmers of Tribal Sub-Plan (TSP) project of College of Post Graduate Studies in Agricultural Sciences (CPGSAS), Umiam, Meghalaya and College of Home Science (CoHSc), Tura, Meghalaya, Central Agricultural University, Imphal [CAU(I)], the method of paired comparison is applied to prioritize the list of issues. The data was collected in the year 2018 from 390 beneficiary farmers from Ri-Bhoi district and West Garo Hill of Meghalaya state. The result indicates that crop diseases and pest infestation were the most critical issue. Both present and future policymaker need to intervene according to the need base situation of the farmer to ensure their livelihood security.

Sign in / Sign up

Export Citation Format

Share Document