Plant Leaf Diseases Detection and Classification Using Image Processing and Deep Learning Techniques

2020 ◽  
Author(s):  
Marwan Adnan Jasim ◽  
Jamal Mustafa AL-Tuwaijari
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Plant Leaf ◽  
Learning Techniques

Use of deep learning techniques for identification of plant leaf stresses: A review

2020 ◽  
Vol 28 ◽  
pp. 100443
Author(s):  
Serosh Karim Noon ◽  
Muhammad Amjad ◽  
Muhammad Ali Qureshi ◽  
Abdul Mannan
Keyword(s):  
Deep Learning ◽  
Plant Leaf ◽  
Learning Techniques

scholarly journals The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

2019 ◽  
Vol 63 (11) ◽  
pp. 1658-1667
Author(s):  
M J Castro-Bleda ◽  
S España-Boquera ◽  
J Pastor-Pellicer ◽  
F Zamora-Martínez
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Supervised Learning ◽  
Image Enhancement ◽  
Super Resolution ◽  
Supervised Machine Learning ◽  
Text Documents ◽  
Learning Techniques ◽  
Printed Text

Abstract This paper presents the ‘NoisyOffice’ database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems.

scholarly journals Automatic Analysis of Bees’ Waggle Dance

2020 ◽  
Author(s):  
Jordan Reece ◽  
Margaret Couvillon ◽  
Christoph Grüter ◽  
Francis Ratnieks ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Background Subtraction ◽  
Time Frame ◽  
Waggle Dance ◽  
Automatic Analysis ◽  
Learning Techniques ◽  
Low Pass ◽  
Processing Steps ◽  
Traditional Image

AbstractThis work describe an algorithm for the automatic analysis of the waggle dance of honeybees. The algorithm analyses a video of a beehive with 13,624 frames, acquired at 25 frames/second. The algorithm employs the following traditional image processing steps: conversion to grayscale, low pass filtering, background subtraction, thresholding, tracking and clustering to detect run of bees that perform waggle dances. The algorithm detected 44,530 waggle events, i.e. one bee waggling in one time frame, which were then clustered into 511 waggle runs. Most of these were concentrated in one section of the hive. The accuracy of the tracking was 90% and a series of metrics like intra-dance variation in angle and duration were found to be consistent with literature. Whilst this algorithm was tested on a single video, the ideas and steps, which are simple as compared with Machine and Deep Learning techniques, should be attractive for researchers in this field who are not specialists in more complex techniques.

Intelligent grading system based on deep learning

2021 ◽  
pp. 002072092098399
Author(s):  
Meng Xiao ◽  
Haibo Yi
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Secondary Schools ◽  
Image Recognition ◽  
Processing Method ◽  
Grading System ◽  
Recognition Method ◽  
Lightweight Framework ◽  
Examination Method ◽  
Learning Techniques

According to the survey, off-line examination is still the main examination method in universities, primary and secondary schools. The grading processing of off-line examination is time-consuming. Besides, since the off-line grading is subjective, it is error-prone. In order to address the challenges in off-line examinations of universities, primary and secondary schools, it is very urgent to improve the efficiency of off-line grading. In order to realize intelligent grading for off-line examinations, we exploit deep learning techniques to off-line grading. First, we propose an image processing method for English letters. Second, we propose a image recognition method based on deep learning for English letters. Third, we propose a lightweight framework for grading. Based on the above designs, we design an intelligent grading system based on deep learning. We implement the system and the result shows that the intelligent grading system can batch grading efficiently. Besides, compared with related designs, the proposed system is more flexible and intelligent.

scholarly journals A Noval Method for Detecting Plant Leaf Disease Using Image Processing and Deep Learning

2021 ◽  
Vol 12 (2) ◽  
pp. 337-340
Author(s):  
M.Sowmiya, Et. al.
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Early Recognition ◽  
Conventional Technique ◽  
The Other ◽  
General Development ◽  
Plant Leaf ◽  
Ideal Approach ◽  
Over The Top

The venture presents a programmed approach for early illness and nourishment insufficiency identification in plant leaf. A great many dollars are being spent to shield the harvests every year. Creepy crawlies, sustenance lack, plant illness and vermin harm the harvests and, in this way, are hazardous for the general development of the yield. One strategy to ensure the harvest is early illness identification and nourishment lack so the yield can be secured. The most ideal approach to think about the soundness of the yield is the convenient assessment of the harvest. On the off chance that sickness or sustenance inadequacy are identified, fitting measures can be taken to shield the harvest from a major creation misfortune toward the end. Early recognition would be useful for limiting the use of the pesticides and would give direction to the determination of the pesticides. It has become a wide territory for research now a days and a great deal of examination has been completed worldwide for programmed location of illnesses. Conventional technique for assessment of the fields is unaided eye assessment however it is exceptionally hard to have a point by point assessment in enormous fields. To inspect the entire field, numerous human specialists are required which is over the top expensive and tedious. Thus a programmed framework is required which can inspect the harvests to distinguish invasion as well as can characterize the kind of sickness on crops. PC vision procedures give viable approaches to breaking down the pictures of leaves. CNN is utilized for order of pictures with and without ailment dependent on the picture highlights. This procedure is less difficult when contrasted with the other mechanized strategies and gives better outcomes

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