scholarly journals Forecasting the spread of aerially transmitted crop diseases with a binary classifier for inoculum survival

2018 ◽  
Vol 67 (4) ◽  
pp. 920-928 ◽  
Author(s):  
P. Skelsey ◽  
S. R. Dancey ◽  
K. Preedy ◽  
A. K. Lees ◽  
D. E. L. Cooke
Keyword(s):  
Binary Classifier ◽  
Crop Diseases ◽  
Inoculum Survival

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.

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

scholarly journals Resistance of Corynebacterium pseudotuberculosis in the Brazilian semiarid environment

2018 ◽  
Vol 38 (6) ◽  
pp. 1091-1096 ◽  
Author(s):  
Maria C.A. Sá ◽  
Samily A.S. Oliveira ◽  
Edmilson M. Dantas Jr ◽  
Gisele V. Gouveia ◽  
João J.S. Gouveia ◽  
...  
Keyword(s):  
Survival Rates ◽  
Control Measures ◽  
Corynebacterium Pseudotuberculosis ◽  
Maximum Temperature ◽  
Caseous Lymphadenitis ◽  
Biofilm Production ◽  
Brazilian Semiarid ◽  
Higher Temperature ◽  
Inoculum Survival

ABSTRACT: The semiarid northeast of Brazil contains a unique biome known as caatinga, with a maximum temperature of 40 ºC and a relativity humidity of 56%. The caatinga is characterized by a variety of plants, including Cereus jamacaru Dc (mandacaru), Poincianella microphylla Mart. ex G. Don (catingueira), Pilosocereus gounellei FAC Weber (xique-xique) and Mimosa tenuiflora (Willd.) Poir (jurema preta). Sheep and goat industries are economically strong in that region, despite the fact that caseous lymphadenitis is highly prevalent. The aim of the present study was to assess the survival and biofilm production of Corynebacterium pseudotuberculosis isolates in the environment and under controlled temperatures (28°C, 37°C and 42°C) under different surfaces (plants, soil, wood, wire and thorns). In addition, we investigated the effects of applying the disinfectants chlorhexidine, hypochlorite and quaternary ammonia in soil, tiles, wood and vegetation cover. Four strains of C. pseudotuberculosis were selected (two from goats and two from sheep) for inoculation according to their in vitro biofilm production. Adherence to microplates was used to assess the biofilm-forming ability of the bacteria. Lower survival rates were observed when isolates of C. pseudotuberculosis were subjected to a temperature of 42°C. In terms of caatinga biome plants, contamination of jurema-preta plants resulted in the lowest survival rates. The disinfectant quaternary ammonia promoted a lower inoculum survival in all surfaces. The disinfectants and the higher temperature contributed to the reduction of biofilm production in isolates of C. pseudotuberculosis. knowledge of these patterns is important for the establishment of disease control measures, given the questionable efficacy of the treatment and the immuno-prophylaxis of caseous lymphadenitis.

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 Associating Natural Language Comment and Source Code Entities

2020 ◽  
Vol 34 (05) ◽  
pp. 8592-8599
Author(s):  
Sheena Panthaplackel ◽  
Milos Gligoric ◽  
Raymond J. Mooney ◽  
Junyi Jessy Li
Keyword(s):  
Software Development ◽  
Natural Language ◽  
Open Source ◽  
Source Code ◽  
Initial Step ◽  
Binary Classifier ◽  
Sequence Labeling ◽  
Evaluation Dataset ◽  
Revision Histories

Comments are an integral part of software development; they are natural language descriptions associated with source code elements. Understanding explicit associations can be useful in improving code comprehensibility and maintaining the consistency between code and comments. As an initial step towards this larger goal, we address the task of associating entities in Javadoc comments with elements in Java source code. We propose an approach for automatically extracting supervised data using revision histories of open source projects and present a manually annotated evaluation dataset for this task. We develop a binary classifier and a sequence labeling model by crafting a rich feature set which encompasses various aspects of code, comments, and the relationships between them. Experiments show that our systems outperform several baselines learning from the proposed supervision.

scholarly journals The Computational Complexity of Understanding Binary Classifier Decisions

2021 ◽  
Vol 70 ◽  
Author(s):  
Stephan Waeldchen ◽  
Jan Macdonald ◽  
Sascha Hauch ◽  
Gitta Kutyniok
Keyword(s):  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Boolean Circuits ◽  
Binary Classifier ◽  
Minimal Set ◽  
Limited Size ◽  
Approximation Factor ◽  
Special Cases ◽  
Relevant Variables ◽  
Binary Classifiers

For a d-ary Boolean function Φ: {0, 1}d → {0, 1} and an assignment to its variables x = (x1, x2, . . . , xd) we consider the problem of finding those subsets of the variables that are sufficient to determine the function value with a given probability δ. This is motivated by the task of interpreting predictions of binary classifiers described as Boolean circuits, which can be seen as special cases of neural networks. We show that the problem of deciding whether such subsets of relevant variables of limited size k ≤ d exist is complete for the complexity class NPPP and thus, generally, unfeasible to solve. We then introduce a variant, in which it suffices to check whether a subset determines the function value with probability at least δ or at most δ − γ for 0 < γ < δ. This promise of a probability gap reduces the complexity to the class NPBPP. Finally, we show that finding the minimal set of relevant variables cannot be reasonably approximated, i.e. with an approximation factor d1−α for α > 0, by a polynomial time algorithm unless P = NP. This holds even with the promise of a probability gap.

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.

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