A new portable application for automatic segmentation of plants in agriculture

Hernández-Hernández, José Luis; Ruiz-Hernández, José António; Garcı́a-Mateos, Ginés; González-Esquiva, J.M.; Ruiz-Canales, A.; Molina-Martínez, José Miguel

doi:10.1016/j.agwat.2016.08.013

Cited by 29 publications

(25 citation statements)

References 20 publications

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“…Finally, the most effective features automatically selected were channel b* in the L*a*b* color space, and the color purity index, C*, from the L*C*h space, which is also derived from a* and b* channels (see Table 1). This predominance of the L*a*b* color space has also been reported in other applications of computer vision in agriculture [38]. However, it should be noted that these results may depend on the specific application domain.…”

Section: Selection Of the Color Features And Configuration Of Ann-icasupporting

confidence: 76%

Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions

et al. 2019

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Color segmentation is one of the most thoroughly studied problems in agricultural applications of remote image capture systems, since it is the key step in several different tasks, such as crop harvesting, site specific spraying, and targeted disease control under natural light. This paper studies and compares five methods to segment plum fruit images under ambient conditions at 12 different light intensities, and an ensemble method combining them. In these methods, several color features in different color spaces are first extracted for each pixel, and then the most effective features are selected using a hybrid approach of artificial neural networks and the cultural algorithm (ANN-CA). The features selected among the 38 defined channels were the b* channel of L*a*b*, and the color purity index, C*, from L*C*h. Next, fruit/background segmentation is performed using five classifiers: artificial neural network-imperialist competitive algorithm (ANN-ICA); hybrid artificial neural network-harmony search (ANN-HS); support vector machines (SVM); k nearest neighbors (kNN); and linear discriminant analysis (LDA). In the ensemble method, the final class for each pixel is determined using the majority voting method. The experiments showed that the correct classification rate for the majority voting method excluding LDA was 98.59%, outperforming the results of the constituent methods.

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Section: Selection Of the Color Features And Configuration Of Ann-icasupporting

confidence: 76%

Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions

et al. 2019

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“…The results obtained by this method in the apple images are presented in Table 8. The second method used for comparison is also based on a recent research by Hernández-Hernández et al [13]. The basis of this method is to estimate the probability that a color belongs to the classes of interest by using the Bayes rule.…”

Section: Accuracy and Comparison Of The Proposed Segmentation Algorithmmentioning

confidence: 99%

“…Another application is for testing the moisture content in leaves of lettuce samples [12]. In this regard, Hernández-Hernández et al [13] proposed a method for choosing the optimal color space for plant segmentation in the agricultural domain. To train the proposed algorithm, they took 182 images of two kinds of lettuce (var.…”

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

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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.

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“…Computer vision systems have a wide range of applications in agronomy and food industry such as irrigation, grading, harvesting, and automatic detection of different varieties of seeds as non-destructive assessment [7][8][9][10][11]. Some research works have used machine vision systems for the classification of different seeds [12].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Chickpea Varieties Using Computer Vision Techniques

et al. 2019

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There are about 90 different varieties of chickpeas around the world. In Iran, where this study takes place, there are five species that are the most popular (Adel, Arman, Azad, Bevanij and Hashem), with different properties and prices. However, distinguishing them manually is difficult because they have very similar morphological characteristics. In this research, two different computer vision methods for the classification of the variety of chickpeas are proposed and compared. The images were captured with an industrial camera in Kermanshah, Iran. The first method is based on color and texture features extraction, followed by a selection of the most effective features, and classification with a hybrid of artificial neural networks and particle swarm optimization (ANN-PSO). The second method is not based on an explicit extraction of features; instead, image patches (RGB pixel values) are directly used as input for a three-layered backpropagation ANN. The first method achieved a correct classification rate (CCR) of 97.0%, while the second approach achieved a CCR of 99.3%. These results prove that visual classification of fruit varieties in agriculture can be done in a very precise way using a suitable method. Although both techniques are feasible, the second method is generic and more easily applicable to other types of crops, since it is not based on a set of given features.

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A new portable application for automatic segmentation of plants in agriculture

Cited by 29 publications

References 20 publications

Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions

Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions

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

Automatic Classification of Chickpea Varieties Using Computer Vision Techniques

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