Design of Smart System to Detect Ripeness of Tomato and Chili with New Approach in Data Acquisition

Taofik, Ahmad; Ismail, Nanang; Gerhana, Yana Aditia; Komarujaman, K.; Ramdhani, Muhammad Ali

doi:10.1088/1757-899x/288/1/012018

Cited by 61 publications

(25 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Taofik et al [6] developed a ripeness, detection system which adequates to defect 4 levels of [unripe 1, unripe 2, medium and ripe] ripeness of tomatoes and chilies. They experimented the system in each stage and the preliminary results were partially carried on between each stage and refined by PC, In spite of the fact that if the developed system was static, it is not possible to detect fruit color when the fruit is still on the tree.…”

Section: Review Of Existing Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Literature Review on Machine Vision based Approaches for Ripeness Detection of Fruits

Mohan¹,

Sreekumar²

2019

IJCA

View full text Add to dashboard Cite

Plump fruits are a vital part of the human diet giving mandatory vitamins, minerals and other health encouraging compounds. Quality assessment and finding of fruit ripeness is a major concern in agriculture business and becomes a growing research concern in computer vision. Image processing is an advanced field which led to a higher demand to reduce the high rate of errors and given more possible results. Therefore, the objective of many types of research is to standardize and reduce manual work in the classification of tomatoes ripeness. One of the most important feature of an image is color. Estimating the ripeness of fruits via color can be performed as it is the dominant feature in describing the information of the image. However, each color models have been given a different performance when used in the experiment. This paper is a survey of different techniques that are deployed over different varieties of fruit images in order to detect maturity stages for ripening, fruit region estimation and also, the effect of different color models and other features on detecting ripeness was studied in this literature survey.

show abstract

Section: Review Of Existing Methodsmentioning

confidence: 99%

“…ANNs outperformed SVM by using both spatial domain features and wavelet features 6 Taofik, A., et al [6] Color features were extracted.…”

mentioning

confidence: 99%

A Literature Review on Machine Vision based Approaches for Ripeness Detection of Fruits

Mohan¹,

Sreekumar²

2019

IJCA

View full text Add to dashboard Cite

show abstract

“…Zhang, Y. et al [5] used a novel CNN architecture for subtle classification of various ripening stages of banana. Supriatna, A. D. and Setiawan, R. [6] designed a smart to work on a mobile device to detect four levels of ripeness of tomato and chili fruits. Katarzyna, R. and Paweł, M. [7] proposed vision-based method for fruit classification using deep CNN.…”

Section: Introductionmentioning

confidence: 99%

Predictive Model for Optimum Fruit Maturity Grading

Gururaj*,

Vinod

2019

IJITEE

View full text Add to dashboard Cite

Metabolic changes to the climacteric fruits like Mango during the post-harvest lifecycle has significant impact on the marketability of the fruit. One such change is in the structure of the cuticle layer on the surface of the mango and its composition changes as the fruit matures and expands. By looking at the structure of the cuticle the right maturity stage of the fruit can be determined which can decrease storage costs and increase market outcomes. The objective of this paper is to classify the mango fruit according to its maturity stage by looking at the cuticle structure of the mango fruit images as seen using light microscope under magnification. The data set consists of structural microscopic images of 2 varieties of mango viz. Banganapalli and Kili Mooku/Banglora and the classification is done using Convolutional Neural network (CNN) based on deep learning techniques. The combined results of both varieties yield a classification accuracy of 83% for all maturity stages. With this neural network model one can identify the ripening stage of the mango in a non-destructive manner that can greatly improve the mango harvesting strategies

show abstract

“…Personnel information system is part of management information system [10]. Refers to several research, an information system has a high capability in decision making, the system has an accurate data accessibility [11], efficient run-time [12], high accuracy [13], and to support a proper decision [14], low cost [15], extended accessibility [16], intensify user knowledge [17], increase productivity [18], and provide a better data and information [19].…”

Section: Introductionmentioning

confidence: 99%

Design of personnel information systems using rapid application development method

2018

View full text Add to dashboard Cite

Information system is a system that is interconnected and integrated with each other and aims to provide personnel information to support operations, management and decision-making functions within an organization. The purpose of this research is to build personnel information system with Rapid Application Development (RAD) method. The methodology used in this design is the RAD method which consists of identifying the objectives and information requirements, designing the system, building the system, and introducing the new system. This study is limited to the design stage only. The design result is a more effective and efficient employee information system design. The conclusion of this research is that the information system built with RAD method takes shorter time, but required good coordination between system designer with user.

show abstract

Design of Smart System to Detect Ripeness of Tomato and Chili with New Approach in Data Acquisition

Cited by 61 publications

References 1 publication

A Literature Review on Machine Vision based Approaches for Ripeness Detection of Fruits

A Literature Review on Machine Vision based Approaches for Ripeness Detection of Fruits

Predictive Model for Optimum Fruit Maturity Grading

Design of personnel information systems using rapid application development method

Contact Info

Product

Resources

About