A robust algorithm based on color features for grape cluster segmentation

Behroozi‐Khazaei, Nasser; Maleki, Mohammad Reza

doi:10.1016/j.compag.2017.08.025

Cited by 51 publications

(17 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In another study, Behroozi-Khazaei and Maleki [7] pointed out that using image processing for garden operations, especially in segmentation, is a very challenging problem. They proposed an algorithm for the segmentation of ripe grape clusters from leaves and background based on color features, using artificial neural networks and genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

Sabzi

Abbaspour‐Gilandeh

Garcı́a-Mateos

et al. 2018

Water

View full text Add to dashboard Cite

Due to the changes in the lighting intensity and conditions throughout the day, machine vision systems used in precision agriculture for irrigation management should be prepared for all possible conditions. For this purpose, a complete segmentation algorithm has been developed for a case study on apple fruit segmentation in outdoor conditions using aerial images. This algorithm has been trained and tested using videos with 16 different light intensities from apple orchards during the day. The proposed segmentation algorithm consists of five main steps: (1) transforming frames in RGB to CIE L*u*v* color space and applying thresholds on image pixels; (2) computing texture features of local standard deviation; (3) using intensity transformation to remove background pixels; (4) color segmentation applying different thresholds in RGB space; and (5) applying morphological operators to refine the results. During the training process of this algorithm, it was observed that frames in different light conditions had more than 58% color sharing. Results showed that the accuracy of the proposed segmentation algorithm is higher than 99.12%, outperforming other methods in the state of the art that were compared. The processed images are aerial photographs like those obtained from a camera installed in unmanned aerial vehicles (UAVs). This accurate result will enable more efficient support in the decision making for irrigation and harvesting strategies.

show abstract

Section: Introductionmentioning

confidence: 99%

Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

Sabzi

Abbaspour‐Gilandeh

Garcı́a-Mateos

et al. 2018

Water

View full text Add to dashboard Cite

show abstract

“…A grape identification method based on an artificial neural network (ANN) and a genetic algorithm (GA) using color features is presented in [ 15 ]. A GA is employed to optimize the ANN structure and select superior color features simultaneously.…”

Section: Plant and Fruit Detection Approachesmentioning

confidence: 99%

Machine Vision Systems in Precision Agriculture for Crop Farming

et al. 2019

View full text Add to dashboard Cite

Machine vision for precision agriculture has attracted considerable research interest in recent years. The aim of this paper is to review the most recent work in the application of machine vision to agriculture, mainly for crop farming. This study can serve as a research guide for the researcher and practitioner alike in applying cognitive technology to agriculture. Studies of different agricultural activities that support crop harvesting are reviewed, such as fruit grading, fruit counting, and yield estimation. Moreover, plant health monitoring approaches are addressed, including weed, insect, and disease detection. Finally, recent research efforts considering vehicle guidance systems and agricultural harvesting robots are also reviewed.

show abstract

“…Dubey et al proposed a disease classification method for apple fruit which based on image processing technology [14]. To segment the images of grape from background and leaves, Behroozi-Khazaei and Maleki presented an method based on genetic algorithm (GA) and artificial neural network (ANN) [15]. Ma et al presented an algorithm based on the information on color and region growing for identifying foliar diseases in greenhouse vegetable [16].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning based Rapid Diagnosis System for Identifying Tomato Nutrition Disorders

Zhang¹,

Jia²,

Li³

et al. 2019

KSII TIIS

View full text Add to dashboard Cite

Nutritional disorders are one of the most common diseases of crops and they often result in significant loss of agricultural output. Moreover, the imbalance of nutrition element not only affects plant phenotype but also threaten to the health of consumers when the concentrations above the certain threshold. A number of disease identification systems have been proposed in recent years. Either the time consuming or accuracy is difficult to meet current production management requirements. Moreover, most of the systems are hard to be extended, only detect a few kinds of common diseases with great difference. In view of the limitation of current approaches, this paper studies the effects of different trace elements on crops and establishes identification system. Specifically, we analysis and acquire eleven types of tomato nutritional disorders images. After that, we explore training and prediction effects and significances of super resolution of identification model. Then, we use pre-trained enhanced deep super-resolution network (EDSR) model to pre-processing dataset. Finally, we design and implement of diagnosis system based on deep learning. And the final results show that the average accuracy is 81.11% and the predicted time less than 0.01 second. Compared to existing methods, our solution achieves a high accuracy with much less consuming time. At the same time, the diagnosis system has good performance in expansibility and portability.

show abstract

A robust algorithm based on color features for grape cluster segmentation

Cited by 51 publications

References 24 publications

Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

Machine Vision Systems in Precision Agriculture for Crop Farming

Deep Learning based Rapid Diagnosis System for Identifying Tomato Nutrition Disorders

Contact Info

Product

Resources

About