scholarly journals Pest Detection using Image Processing

2019 ◽  
Vol 9 (2) ◽  
pp. 1496-1498
Keyword(s):  
Image Processing ◽  
Economic Activity ◽  
Large Scale ◽  
Early Stage ◽  
Productivity Loss ◽  
Morphological Operations ◽  
Image Processing Techniques ◽  
Pest Detection ◽  
Processing Techniques ◽  
Leaf Image

Agriculture is one of the most significant economic activity. They are many ways that leads to the low productivity of agriculture, but the best method to protect the crop is by detecting the diseases in the early stage. In most of the cases diseases are caused by pest, insects, pathogens which reduce the productivity of the crop at the large scale. If pests are detected on the leaves then, precautions should be taken to avoid huge productivity loss at the end. The main objective of this paper is to identify the pests using image processing techniques like Gaussian blur, segmentation, watershed separation, morphological operations. These techniques are more efficient and less time consuming while identifying the pests over the leaf image with high intensity.

scholarly journals Lung Cancer Detection using Convolutional Neural Network

2019 ◽  
Vol 8 (6) ◽  
pp. 3256-3262 ◽  
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Detection System ◽  
Histogram Equalization ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Morphological Operations ◽  
Image Processing Techniques ◽  
Cad System ◽  
Processing Techniques

The mortality rate is increasing among the growing population and one of the leading causes is lung cancer. Early diagnosis is required to decrease the number of deaths and increase the survival rate of lung cancer patients. With the advancements in the medical field and its technologies CAD system has played a significant role to detect the early symptoms in the patients which cannot be carried out manually without any error in it. CAD is detection system which has combined the machine learning algorithms with image processing using computer vision. In this research a novel approach to CAD system is presented to detect lung cancer using image processing techniques and classifying the detected nodules by CNN approach. The proposed method has taken CT scan image as input image and different image processing techniques such as histogram equalization, segmentation, morphological operations and feature extraction have been performed on it. A CNN based classifier is trained to classify the nodules as cancerous or non-cancerous. The performance of the system is evaluated in the terms of sensitivity, specificity and accuracy

scholarly journals Pest Detection and Extraction Using Image Processing Techniques

2014 ◽  
Vol 3 (3) ◽  
pp. 189-192 ◽  
Author(s):  
Johnny L. Miranda ◽  
Bobby D. Gerardo ◽  
Bartolome T. Tanguilig III
Keyword(s):  
Image Processing ◽  
Image Processing Techniques ◽  
Pest Detection ◽  
Processing Techniques

SOYBEAN LEAF DISEASES DETECTION AND CLASSIFICATION USING RECENT IMAGE PROCESSING TECHNIQUES

2020 ◽  
Vol 8 (3) ◽  
pp. 01-08
Author(s):  
Arpan Singh Rajput ◽  
Shailja Shukla ◽  
S. S. Thakur
Keyword(s):  
Image Processing ◽  
Early Stage ◽  
Weather Condition ◽  
Plant Diseases ◽  
Soybean Plant ◽  
Plant Leaf ◽  
Image Processing Techniques ◽  
Soybean Leaf ◽  
Processing Techniques ◽  
Soybean Diseases

Purpose: India is an agricultural country and soybean production is one of the major sources of earning. Due to the major factors like diseases, pest attacks, and sudden changes in the weather condition, the productivity of the soybean crop decreases. Automatic detection of soybean plant diseases is essential to detect the symptoms of soybean diseases as early as they appear on the growing stage. This paper proposed a methodology for the analysis and detection of soybean plant leaf diseases using recent digital image processing techniques. In this paper, experimental results demonstrate that the proposed method can successfully detect and classify the major soybean diseases. Methodology: MatLab 18a is used for the simulation for the result and machine learning-based recent image processing techniques for the detection of the soybean leaf disease. Main Findings: The main finding of this work is to create the soybean leaf database which includes healthy and unhealthy leaves and achieved 96 percent accuracy in this work using the proposed methodology. Applications of this study: To detect soybean plant leaf diseases in the early stage in Agricultural. The novelty of this study: Self-prepared database of healthy and unhealthy images of soybean leaf with the proposed algorithm.

scholarly journals Image processing techniques for lemons and tomatoes classification

Bragantia ◽  
2008 ◽  
Vol 67 (3) ◽  
pp. 785-789 ◽  
Author(s):  
Antonio Carlos Loureiro Lino ◽  
Juliana Sanches ◽  
Inacio Maria Dal Fabbro
Keyword(s):  
Image Processing ◽  
Classification System ◽  
Fruit Shape ◽  
Morphological Operations ◽  
Border Detection ◽  
Image Processing Techniques ◽  
Operational Costs ◽  
Image Improvement ◽  
Processing Techniques ◽  
Vegetable Quality

Vegetable quality is frequently referred to size, shape, mass, firmness, color and bruises from which fruits can be classified and sorted. However, technological by small and middle producers implementation to assess this quality is unfeasible, due to high costs of software, equipment as well as operational costs. Based on these considerations, the proposal of this research is to evaluate a new open software that enables the classification system by recognizing fruit shape, volume, color and possibly bruises at a unique glance. The software named ImageJ, compatible with Windows, Linux and MAC/OS, is quite popular in medical research and practices, and offers algorithms to obtain the above mentioned parameters. The software allows calculation of volume, area, averages, border detection, image improvement and morphological operations in a variety of image archive formats as well as extensions by means of "plugins" written in Java.

scholarly journals Automatic Detection of Optic Disc for Diabetic Retinopathy

2020 ◽  
Vol 9 (7) ◽  
pp. 1013-1015
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Optic Disc ◽  
Early Stage ◽  
Vital Role ◽  
Retinal Images ◽  
Image Processing Techniques ◽  
Canny Edge ◽  
Total Blindness ◽  
Processing Techniques

Diabetic Retinopathy affects the retina of the eye and eventually it may lead to total visual impairment. Total blindness can be avoided by detecting Diabetic Retinopathy at an early stage. Various manual tests are used by the doctors to detect the presence of disease, but they are tedious and expensive. Some of the features of Diabetic Retinopathy are exudates, haemorrhages and micro aneurysms. Detection and removal of optic disc plays a vital role in extraction of these features. This paper focuses on detection of optic disc using various image processing techniques, algorithms such as Canny edge, Circular Hough (CHT). Retinal images from IDRiD, Diaret_db0, Diaret_db1, Chasedb and Messidor datasets were used.

Efficient Image Denoising for Effective Digitization using Image Processing Techniques and Neural Networks

2016 ◽  
Vol 7 (4) ◽  
pp. 77-93 ◽  
Author(s):  
K.G. Srinivasa ◽  
B.J. Sowmya ◽  
D. Pradeep Kumar ◽  
Chetan Shetty
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Network Models ◽  
Morphological Operations ◽  
Neural Network Models ◽  
Image Processing Techniques ◽  
Optical Character ◽  
Processing Techniques

Vast reserves of information are found in ancient texts, scripts, stone tablets etc. However due to difficulty in creating new physical copies of such texts, knowledge to be obtained from them is limited to those few who have access to such resources. With the advent of Optical Character Recognition (OCR) efforts have been made to digitize such information. This increases their availability by making it easier to share, search and edit. Many documents are held back due to being damaged. This gives rise to an interesting problem of removing the noise from such documents so it becomes easier to apply OCR on them. Here the authors aim to develop a model that helps denoise images of such documents retaining on the text. The primary goal of their project is to help ease document digitization. They intend to study the effects of combining image processing techniques and neural networks. Image processing techniques like thresholding, filtering, edge detection, morphological operations, etc. will be applied to pre-process images to yield higher accuracy of neural network models.

scholarly journals Analysis and automatic segmentation of images for lungs regions extraction in X-ray chest

2020 ◽  
pp. 18-27
Author(s):  
Joel Quintanilla-Domínguez ◽  
Juan Israel Yañez-Vargas ◽  
Miriam Butanda-Serrano ◽  
Enrique Sánchez-Torrecitas
Keyword(s):  
Image Processing ◽  
Contrast Enhancement ◽  
Digital Image Processing ◽  
Digital Image ◽  
Automatic Segmentation ◽  
Morphological Operations ◽  
X Ray ◽  
Image Processing Techniques ◽  
Segmentation Of Images ◽  
Processing Techniques

One of the main disease caused by the COVID-19 in the humans is the pneumonia. This disease mainly attacks the lungs and one of the effective methods for diagnosis is through X-ray chest analysis. Due this in this work a methodology that allow the segmentation and analysis of regions that belong to the lungs in images of X-ray chest is presented. This methodology is based mainly in the implementation of some digital image processing techniques such as: contrast enhancement, segmentation, binarization and the application of morphological operations as the erosion and dilatation.

scholarly journals A Survey on various image processing techinques for glaucoma diagnosis

2013 ◽  
Vol 5 (1) ◽  
pp. 393-396
Author(s):  
Mrs. Kinjan Chauhan ◽  
Dr. Ravi Gulati
Keyword(s):  
Image Processing ◽  
Early Stage ◽  
Image Processing Techniques ◽  
Intra Ocular Pressure ◽  
Glaucoma Diagnosis ◽  
Ocular Pressure ◽  
Processing Techniques

Glaucoma is considered to be the leading cause for blindness in patients. Intra-Ocular Pressure(IOP) is one of theprimary factors which plays an important role in the development of Glaucoma. There is a need for checking progression of Glaucoma in patients at early stage so thats progression can be controlled and/or necessary action can be taken. In this paper, we have surveyed various image processing techniques used for detection of Glaucoma in patients using various opthalmic tests.

scholarly journals Plant Disease Detection using Image Processing Techniques

2020 ◽  
pp. 295-300
Author(s):  
Chinmayee Sawant ◽  
Mithila Shirgaonkar ◽  
Sakshi Khule ◽  
Prajakta Jadhav
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Early Stage ◽  
Disease Detection ◽  
Image Processing Techniques ◽  
Affected Plant ◽  
Processing Techniques ◽  
Large Work ◽  
Respective Product ◽  
Automatic Technique

The Indian economy is highly dependent on Agriculture productivity. Having diseases in plants are natural, so disease detection in plant plays an important role in agriculture field. If proper care is not taken, then it causes very serious effects on plants, so that respective product quality and product quantity is affected. Plant disease detection using automatic technique is very useful because it reduces a large work of monitoring in big farms. At very early stage itself it detects the symptoms of diseases when they appear on plant leaves. This project focuses on an approach based on image processing techniques to detect the disease of plants.

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