Protecting Superfood Olive Crop from Pests and Pathogen Using Image Processing Techniques: A Review

Author(s):  
Smita Sisodiya ◽  
Aditya Sinha ◽  
Mousumi Debnath ◽  
Rajveer Shekhawat ◽  
Surinder Singh Skehkawat

Background: Olive (Oleo europaea L.) cultivars are widely cultivated all over the world. But it is often attacked by pests and pathogens. This deteriorates the quality of the crop leading to less yield of olive oil. Image processing techniques can be used to classify the different pathogens causing similar disease symptoms on olive leaves Objective: With the rapid increase in the availability of data in the field of nutrigenomics, the olive has established itself as a superfood and a potential source of therapeutics. The objective of this review is to emphasize the early detection and classification of the disease using image processing techniques Method: A systematic literature search using keywords, Olive oil, pest and pathogen of olives, metabolic profiling were done on PubMed, ScienceDirect and Google Scholar. Results: Disease infection often led to huge losses and poor quality of olive oil yield. Early understanding of disease infestations can safeguard the olive plant and the olive oil yield Results: Disease infection often led to huge losses and poor quality of olive oil yield. Early understanding of disease infestations can safeguard the olive plant and the olive oil yield

Author(s):  
Nawafil Abdulwahab Ali ◽  
Imad Fakhri Taha Al Shaikhli

Abstract— The restoration of paintings and manuscripts is defined as the process of restoring old and damaged artworks and documents exhibiting cracks. Cracks are caused by three factors; aging, drying up of painting material, and mechanical. It is necessary that cultural heritages be restored to their original or a near-original state. To enhance the overall quality of the image, there are different techniques and methodologies that can be used for conservation and restoration. The main objective of this study is to analyse techniques and methodologies that have been developed for the detection, classification of small patterns, and restoration of cracks in digitized old painting and manuscripts. The purpose of this research is to present previous works on detection and restoration of cracks using image processing techniques and methodologies.


2018 ◽  
Vol 8 (1) ◽  
pp. 42
Author(s):  
Oky Dwi Nurhayati ◽  
Diana Nur Afifah ◽  
Nuryanto . ◽  
Ninik Rustanti

Visually, choosing the quality of salted eggs by looking at egg shells is something that is very difficult to do. In addition, the lighting and the weakness of the senses of vision also becomes difficult to see the quality of salted eggs visually. So far, to determine a good salted egg, only known from the weight of eggs. Not all eggs that have mild density have poor quality. So far, suppliers often get eggs that have bad quality (broken) so that when processed will produce defective salted eggs. The goal achieved as an effort to improve the quality of this production is software design to know the quality of salted eggs. Quality selection technology involves image processing techniques such as gray imagery, histogram equalization, P-Tile segmentation, and first-order statistical feature extraction that serves to recognize the type of egg image quality. The results obtained with the application of image processing techniques have a fairly good accuracy to determine the quality of salted eggs into two good and bad conditions.


2019 ◽  
Vol 29 (1) ◽  
pp. 1226-1234
Author(s):  
Safa Jida ◽  
Hassan Ouallal ◽  
Brahim Aksasse ◽  
Mohammed Ouanan ◽  
Mohamed El Amraoui ◽  
...  

Abstract This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram-based porosity estimation.


2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Nhat-Duc Hoang

To improve the efficiency of the periodic surveys of the asphalt pavement condition, this study puts forward an intelligent method for automating the classification of pavement crack patterns. The new approach relies on image processing techniques and computational intelligence algorithms. The image processing techniques of Laplacian pyramid and projection integral are employed to extract numerical features from digital images. Least squares support vector machine (LSSVM) and Differential Flower Pollination (DFP) are the two computational intelligence algorithms that are employed to construct the crack classification model based on the extracted features. LSSVM is employed for data classification. In addition, the model construction phase of LSSVM requires a proper setting of the regularization and kernel function parameters. This study relies on DFP to fine-tune these two parameters of LSSVM. A dataset consisting of 500 image samples and five class labels of alligator crack, diagonal crack, longitudinal crack, no crack, and transverse crack has been collected to train and verify the established approach. The experimental results show that the Laplacian pyramid is really helpful to enhance the pavement images and reveal the crack patterns. Moreover, the hybridization of LSSVM and DFP, named as DFP-LSSVM, used with the Laplacian pyramid at the level 4 can help us to achieve the highest classification accuracy rate of 93.04%. Thus, the new hybrid approach of DFP-LSSVM is a promising tool to assist transportation agencies in the task of pavement condition surveying.


Author(s):  
Ahmet Kayabasi ◽  
Kadir Sabanci ◽  
Abdurrahim Toktas

In this study, an image processing techniques (IPTs) and a Sugeno-typed neuro-fuzzy system (NFS) model is presented for classifying the wheat grains into bread and durum. Images of 200 wheat grains are taken by a high resolution camera in order to generate the data set for training and testing processes of the NFS model. The features of 5 dimensions which are length, width, area, perimeter and fullness are acquired through using IPT. Then NFS model input with the dimension parameters are trained through 180 wheat grain data and their accuracies are tested via 20 data. The proposed NFS model numerically calculate the outputs with mean absolute error (MAE) of 0.0312 and classify the grains with accuracy of 100% for the testing process. These results show that the IPT based NFS model can be successfully applied to classification of wheat grains.


Author(s):  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Mohd Azraai Mohd Razman ◽  
Muhammad Amirul Abdullah ◽  
Rabiu Muazu Musa ◽  
...  

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