A Recognition Method for Sweet Pepper Fruits Using LED Light Reflections

Kitamura, Shinsuke; Oka, Koichi

doi:10.9746/jcmsi.2.255

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…The position and velocity of γ and σ for each particle will be updated simultaneously. 7. In order to improve the performance of particle swarm algorithm, a mutation strategy was introduced to improve it.…”

Section: B Improved Lssvm Parameters Optimized By Particle Swarm Optmentioning

confidence: 99%

“…The method has a high recognition rate for target green pepper and the false recognition rate is relatively high, and a suitable method has not been found to reduce the false recognition rate. Kitamura and Oka [7] used LED lights to illuminate green peppers and converted the acquired RGB image into HSI color space. The S and I components are used to identify the reflective areas and to limit the area near the reflective region.…”

Section: Introductionmentioning

confidence: 99%

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Recognition Method of Green Pepper in Greenhouse Based on Least-Squares Support Vector Machine Optimized by the Improved Particle Swarm Optimization

Chen

et al. 2019

IEEE Access

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In the green pepper harvesting robot, the color of green pepper is similar to that of leaves, which makes it difficult to recognize the green pepper target. In order to solve this problem, a green pepper recognition method based on least-squares support vector machine optimized by the improved particle swarm optimization (IPSO-LSSVM) is proposed in this paper. Firstly, the green pepper images are segmented by K-Means method under the Lab color space, and the segmentation images of the target and background are obtained. The processed green pepper image was divided into training and testing samples. Then, the shape and texture features of green pepper targets are extracted separately from the training sample using the hu invariant moment and Tamura texture feature. Meanwhile, in order to reduce the complexity of data calculations and improve the efficiency, the extracted feature vectors are normalized. The feature vector is used as the input eigenvector of the least-squares support vector machine (LSSVM). The particle swarm optimization algorithm is used to obtain the optimal regularization parameter and the kernel function width. In order to maintain the particle activity, the mutation strategy is introduced to improve the particle swarm optimization algorithm. The experimental results show that the recognition rate of IPSO-LSSVM is higher than that of other methods, and the recognition accuracy is 89.04%. It could meet the requirements of green pepper identification.

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Section: B Improved Lssvm Parameters Optimized By Particle Swarm Optmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recognition Method of Green Pepper in Greenhouse Based on Least-Squares Support Vector Machine Optimized by the Improved Particle Swarm Optimization

Chen

et al. 2019

IEEE Access

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show abstract

“…Pound et al [23] proposed a method of classifying wheat grains, nodes, and leaves using a CNN (Convolution Neural Network) but analyzed wheat as a learning factor in a controlled indoor environment. To recognize the shape of plants in an environment where actual plants are grown, color-based 3-dimensional fruit recognition [24], fruit recognition using an LED reflected light and color model [25], color information obtained from an RGB-D camera, and recognition of the fruit stalk by classifying the surface normal vector and curvature into Support Vector Machines [26] have been reported. However, the shape of the plant based on color information could not be recognized because nodes and leaves had the same color information.…”

Section: Introductionmentioning

confidence: 99%

Comparing Performances of CNN, BP, and SVM Algorithms for Differentiating Sweet Pepper Parts for Harvest Automation

et al. 2021

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For harvest automation of sweet pepper, image recognition algorithms for differentiating each part of a sweet pepper plant were developed and performances of these algorithms were compared. An imaging system consisting of two cameras and six halogen lamps was built for sweet pepper image acquisition. For image analysis using the normalized difference vegetation index (NDVI), a band-pass filter in the range of 435 to 950 nm with a broad spectrum from visible light to infrared was used. K-means clustering and morphological skeletonization were used to classify sweet pepper parts to which the NDVI was applied. Scale-invariant feature transform (SIFT) and speeded-up robust features (SURFs) were used to figure out local features. Classification performances of a support vector machine (SVM) using the radial basis function kernel and backpropagation (BP) algorithm were compared to classify local SURFs of fruits, nodes, leaves, and suckers. Accuracies of the BP algorithm and the SVM for classifying local features were 95.96 and 63.75%, respectively. When the BP algorithm was used for classification of plant parts, the recognition success rate was 94.44% for fruits, 84.73% for nodes, 69.97% for leaves, and 84.34% for suckers. When CNN was used for classifying plant parts, the recognition success rate was 99.50% for fruits, 87.75% for nodes, 90.50% for leaves, and 87.25% for suckers.

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“…RGB depth camera was used to detect peduncles of sweet pepper, and color and geometry information acquired from the RGB depth camera were used, and then a supervised-learning approach for the peduncle detection task was utilized; it achieved an AUC (the area under the curve) of 0.71 regarding detection precision of peduncles on field-grown sweet peppers. Kitamura and Oka [17] suggested a method to recognize sweet pepper fruits for a picking robot in a greenhouse. Regions of sweet pepper fruit were detected using the reflection characteristic (that fruits reflected light more than leaves) with LED (Light Emitting Diode) lights, and the recognition success rate for detecting sweet pepper fruits was 79.2%.…”

Section: Introductionmentioning

confidence: 99%

A Vision Servo System for Automated Harvest of Sweet Pepper in Korean Greenhouse Environment

et al. 2019

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Recently, farmers of sweet pepper suffer from the increase of its unit production costs due to the rise of labor costs. The rise of unit production costs of sweet pepper, on the other hand, decreases its productivity and causes the lack of its farming expertise, thus resulting in the quality degradation of products. In this regard, it is necessary to introduce an automated robot harvest system into the farming of sweet pepper. In this study, the authors developed an image-based closed-loop control system (a vision servo system) and an automated sweet pepper harvesting robot system and then carried out experiments to verify its efficiency. The working area of the manipulator that detects products through an imaging sensor in the farming environment of sweet pepper, decides whether to harvest it or not, and then informs the location of the product to the control center, which is set up at the distance scope of 350~600 mm from the center of the system and 1000 mm vertically. In order to confirm the performance of the sweet pepper recognition in this study, 269 sweet pepper images were used to extract fruits. Of 269 sweet pepper images, 82.16% were recognized successfully. The harvesting experiment of the system developed in this study was carried out with 100 sweet peppers. The result of experiment with 100 sweet peppers presents the fact that its approach rate to peduncle is about 86.7%, and via four sessions of repetitive harvest experiment it achieves a maximal 70% harvest rate, and its average time of harvest is 51.1 s.

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A Recognition Method for Sweet Pepper Fruits Using LED Light Reflections

Cited by 5 publications

References 6 publications

Recognition Method of Green Pepper in Greenhouse Based on Least-Squares Support Vector Machine Optimized by the Improved Particle Swarm Optimization

Recognition Method of Green Pepper in Greenhouse Based on Least-Squares Support Vector Machine Optimized by the Improved Particle Swarm Optimization

Comparing Performances of CNN, BP, and SVM Algorithms for Differentiating Sweet Pepper Parts for Harvest Automation

A Vision Servo System for Automated Harvest of Sweet Pepper in Korean Greenhouse Environment

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