Computer Vision Based Fruit Grading System for Quality Evaluation of Tomato in Agriculture industry

Arakeri, Megha P.; Lakshmana,

doi:10.1016/j.procs.2016.03.055

Cited by 153 publications

(72 citation statements)

References 14 publications

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“…The steps in this stage were based on the image processing techniques proposed by Arakeri and Laksmana Arakeri and Laksmana (2016), as detailed below: • Locations of samples. Fruits for each class determined in the previous step were placed on the conveyor belt and then organized in a grid-like arrangement with four rows and five to seven columns.…”

Section: Figure 2 Ripeness States Of Cape Gooseberrymentioning

confidence: 99%

“…However, the visual inspection process suffers from certain disadvantages: it is subjective, variable, tedious, laborious, inconsistent and easily influenced by the environment Arakeri and Laksmana (2016). Consequently, there is growing interest in reducing the subjectivity of visual inspection using innovative and non-contact measurements such as artificial vision systems, which can measure the entire surface of a sample; as a result, these types of systems are more representative than colorimeters, which are based on point-to-point measurements Chen et al (2010); Romano et al (2012); Sozer (2016); Brosnan and Sun (2004).…”

Section: Introductionmentioning

confidence: 99%

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Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

Castro

Oblitas

De-la-Torre

et al. 2019

Preprint

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The classification of fresh fruits according to their visual ripeness is typically a subjective and tedious task; consequently, there is growing interest in the use of non-contact techniques to automate this process. Machine learning techniques such as artificial neural networks, support vector machines, decision trees, and K-nearest neighbor algorithms have been successfully applied for classification problems in the literature, particularly for images of fruit. However, the particularities of each classification problem make it difficult, if not impossible, to select a general technique that is applicable to all types of fruit. In this paper, combinations of four machine learning techniques and three color spaces (RGB, HSV, and L*a*b*) were evaluated with regard to their ability to classify Cape gooseberry fruits. To this end, 925 Cape gooseberry fruit samples were collected, and each fruit was manually classified into one of seven different classes according to its level of ripeness. The color values of each fruit image in the three color spaces and their corresponding ripening stages were organized for training and validation following a 5-fold cross-validation strategy in an iterative process repeated 100 times. According to the results, the classification of Cape gooseberry fruits by their ripeness level was sensitive to both the color space and the classification technique used. The models based on the L*a*b* color space and the SVM classifier showed the highest f-measure regardless of the color space, and the principal component analysis combination of color spaces improved the performance of the models at the expense of an increased complexity.

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Section: Figure 2 Ripeness States Of Cape Gooseberrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

Castro

Oblitas

De-la-Torre

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Quality inspection is another issue, in which a human operator is likely to have a limitation in accuracy. Automatic grading using computer vision techniques can be the answer to overcome the limitation [18]. The machine design should also include the process of technical drawing, modeling, calculation and technical description [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Manufacturing process design for multi commodities in agriculture

Prasetyawan

Santosa

2017

AIP Conference Proceedings

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Abstract. High-potential commodities within particular agricultural sectors should be accompanied by maximum benefit value that can be attained by both local farmers and business players. In several cases, the business players are smallmedium enterprises (SMEs) which have limited resources to perform added value process of the local commodities into the potential products. The weaknesses of SMEs such as the manual production process with low productivity, limited capacity to maintain prices, and unattractive packaging due to conventional production. Agricultural commodity is commonly created into several products such as flour, chips, crackers, oil, juice, and other products. This research was initiated by collecting data by interview method particularly to obtain the perspectives of SMEs as the business players. Subsequently, the information was processed based on the Quality Function Deployment (QFD) to determine House of Quality from the first to fourth level. A proposed design as the result of QFD was produced and evaluated with Technology Assessment Model (TAM) and continued with a revised design. Finally, the revised design was analyzed with financial perspective to obtain the cost structure of investment, operational, maintenance, and workers. The machine that performs manufacturing process, as the result of revised design, was prototyped and tested to determined initial production process. The designed manufacturing process offers IDR 337,897, 651 of Net Present Value (NPV) in comparison with the existing process value of IDR 9,491,522 based on similar production input.

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“…Arakeri and Lakshmana [1] proposed a computer vision based automatic system for tomato grading using ANN (artificial neural network). Wang et al [2] also proposed an automatic grading system of diced potatoes based on computer vision and near-infrared lighting.…”

Section: Introductionmentioning

confidence: 99%

Multi-Input Convolutional Neural Network for Flower Grading

Sun

Zhu

Wang

et al. 2017

Journal of Electrical and Computer Engineering

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Flower grading is a significant task because it is extremely convenient for managing the flowers in greenhouse and market. With the development of computer vision, flower grading has become an interdisciplinary focus in both botany and computer vision. A new dataset named BjfuGloxinia contains three quality grades; each grade consists of 107 samples and 321 images. A multi-input convolutional neural network is designed for large scale flower grading. Multi-input CNN achieves a satisfactory accuracy of 89.6% on the BjfuGloxinia after data augmentation. Compared with a single-input CNN, the accuracy of multi-input CNN is increased by 5% on average, demonstrating that multi-input convolutional neural network is a promising model for flower grading. Although data augmentation contributes to the model, the accuracy is still limited by lack of samples diversity. Majority of misclassification is derived from the medium class. The image processing based bud detection is useful for reducing the misclassification, increasing the accuracy of flower grading to approximately 93.9%.

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Computer Vision Based Fruit Grading System for Quality Evaluation of Tomato in Agriculture industry

Cited by 153 publications

References 14 publications

Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level

Manufacturing process design for multi commodities in agriculture

Multi-Input Convolutional Neural Network for Flower Grading

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