Embedded vision detection of defective orange by fast adaptive lightness correction algorithm

Rong, Dian; Rao, Xiuqin

doi:10.1016/j.compag.2017.03.021

Cited by 19 publications

(4 citation statements)

References 24 publications

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“…Grapes are one of the most important agricultural crops, as they represent a significant source of income for local farmers and breeders, and also play an important role in wine production. However, the achievement of high yields of grapes in these regions can be significantly limited by various diseases [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Monitoring System to Assess Plant Development State Based on Computer Vision in Viticulture

Rudenko,

Kazak,

Oleinikov

et al. 2023

Computation

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Plant health plays an important role in influencing agricultural yields and poor plant health can lead to significant economic losses. Grapes are an important and widely cultivated plant, especially in the southern regions of Russia. Grapes are subject to a number of diseases that require timely diagnosis and treatment. Incorrect identification of diseases can lead to large crop losses. A neural network deep learning dataset of 4845 grape disease images was created. Eight categories of common grape diseases typical of the Black Sea region were studied: Mildew, Oidium, Anthracnose, Esca, Gray rot, Black rot, White rot, and bacterial cancer of grapes. In addition, a set of healthy plants was included. In this paper, a new selective search algorithm for monitoring the state of plant development based on computer vision in viticulture, based on YOLOv5, was considered. The most difficult part of object detection is object localization. As a result, the fast and accurate detection of grape health status was realized. The test results showed that the accuracy was 97.5%, with a model size of 14.85 MB. An analysis of existing publications and patents found using the search “Computer vision in viticulture” showed that this technology is original and promising. The developed software package implements the best approaches to the control system in viticulture using computer vision technologies. A mobile application was developed for practical use by the farmer. The developed software and hardware complex can be installed in any vehicle. Such a mobile system will allow for real-time monitoring of the state of the vineyards and will display it on a map. The novelty of this study lies in the integration of software and hardware. Decision support system software can be adapted to solve other similar problems. The software product commercialization plan is focused on the automation and robotization of agriculture, and will form the basis for adding the next set of similar software.

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Section: Introductionmentioning

confidence: 99%

Intelligent Monitoring System to Assess Plant Development State Based on Computer Vision in Viticulture

Rudenko,

Kazak,

Oleinikov

et al. 2023

Computation

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“…The data created in the digital systems have reached significantly in recent years. These big data are processed by popular computer technologies such as deep learning [1], image detection and recognition [2,3], data mining [4], machine learning [5] and artificial intelligence [6] to be used in several fields such as health [7], education, sports and agriculture [8][9][10][11]. The impact of the technological developments in the field of agriculture has led to the emergence of smart farming systems [12][13][14] as a new popular field.…”

Section: Introductionmentioning

confidence: 99%

Image Steganography-Based GUI Design to Hide Agricultural Data

Solak

Altınışık

2021

Gazi University Journal of Science

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“…Thresholding is the fast segmentation method with low computation cost (Garcia-Lamont et al, 2018). Recently, a global thresholdingbased segmentation method to classify seven types of orange defects was developed by (Rong, Ying, & Rao, 2017), wherein a fast adaptive lightness correction algorithm was implemented to reduce the adverse effects of non-uniform illumination. On comparison with some state of the art techniques, the performance of this algorithm was noticed better in terms of overall time complexity.…”

Section: Introductionmentioning

confidence: 99%

Automatic surface defects classification of Kinnow mandarins using combination of multi‐feature fusion techniques

Hadimani

Garg

2020

J Food Process Engineering

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Analyzing the quality of biological products is a difficult task, as their physicochemical properties such as size, shape, color, and texture change over time. Among many quality attributes, automatic classification of defects is a challenging work due to similarity or diversity of defects in terms of shape, color, and texture within intra and inter cultivars. In this work, an image analysis and machine learning‐based method to identify and classify four defects of Kinnow mandarins is proposed. A simple and fast adaptive thresholding technique was used to segment the defects. Defects discriminatory abilities of three prevalent texture descriptors namely local binary patterns, gray level co‐occurrence matrix (GLCM), and gray level run length matrix (GLRLM) were explored. In order to measure the effectiveness of color models in food analysis techniques, texture features were extracted on individual and combined color channels of three popular color models, that is, RGB, HSV, and CIELAB. Two machine‐learning techniques: random forest (RF) and artificial neural networks (ANNs) classifiers were trained with extracted features to predict the defects. The highest accuracies of 93.5 and 89.3% and average accuracies of 88.95 and 80.67% were achieved by ANN and RF classifiers, respectively, for the feature set {GLCM, GLRLM} on {H,S,V} color set.Practical applicationsKinnow mandarins' cultivation area occupies around 50% of the total citrus farming regions in India. Currently, the expert laborers manually carry out the grading of Kinnow fruits based on the existence of external defects. This process is labor‐intensive, inefficient, and time‐consuming. However, now this labor‐intensive task can be replaced with automatic fruit classification machines based on computer vision (CV) and machine learning (ML) technologies. Presently, there is a scarcity of studies as well as algorithms to automate the process of Kinnow fruit defects classification. The developed CV‐ and ML‐based algorithm is capable to identify and discriminate four types of external defects pertaining to Kinnow mandarins. The classification accuracy and other performance measures achieved on the developed algorithm make it ideal for real‐time online Kinnow defects classification systems.

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Embedded vision detection of defective orange by fast adaptive lightness correction algorithm

Cited by 19 publications

References 24 publications

Intelligent Monitoring System to Assess Plant Development State Based on Computer Vision in Viticulture

Intelligent Monitoring System to Assess Plant Development State Based on Computer Vision in Viticulture

Image Steganography-Based GUI Design to Hide Agricultural Data

Automatic surface defects classification of Kinnow mandarins using combination of multi‐feature fusion techniques

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