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Current IssueEditorial BoardOnline First Articles
Indian Journal of
Agricultural Research
Print ISSN: 0367-8245
Online ISSN: 0976-058X
NASS Rating
5.60
SJR
0.217
CiteScore
0.595

Chief Editor:
V. Geethalakshmi
Tamil Nadu Agricultural University Coimbatore, INDIA
Frequency:Monthly
Indexing:
BIOSIS Preview, ISI Citation Index, Biological Abstracts, Elsevier (Scopus and Embase), AGRICOLA, Go...

Research Article

Indian Journal of Agricultural Research, volume 54 issue 6 (december 2020) : 716-723

Intelligent System to Evaluate the Quality of DRC using Image Processing and then Categorize using Artificial Neural Network (ANN)

Dasharathraj K. Shetty, U. Dinesh Acharya, V.G. Narendra, P.J. Prajual
1Department of Humanities and Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal- 576 104, Karnataka, India.
Cite article:- Shetty K. Dasharathraj, Acharya Dinesh U., Narendra V.G., Prajual P.J. (2020). Intelligent System to Evaluate the Quality of DRC using Image Processing and then Categorize using Artificial Neural Network (ANN). Indian Journal of Agricultural Research. 54(6): 716-723. doi: 10.18805/IJARe.A-5374.

ABSTRACT

Background: In this digital era, agricultural science can significantly benefit from automation. More specifically, automated processes can be used to evaluate the quality of various fruits and vegetables. The main selling point of fruits and vegetables is their sensory characteristics, i.e., their appearance and smell as they significantly impact consumer choice and market value. Sorting and grading of agricultural products are carried out manually, which lead to inconsistency in results; besides, it can also be time-consuming, variable, subjective, onerous, expensive and prone to be influenced by surroundings. These factors build the case for an intelligent automated system to grade fruits and vegetables. 
Methods: This paper proposes an intelligent automated system that uses, Image Processing Techniques and Artificial Neural Network (ANN), to grade Dry Red Chillies(DRC). 
Result: A HSV based algorithm with the efficiency of 80.5 was developed and used to measure the various morphological characteristics of DRC and then clustered into various grades using ANN - Self-Organizing Map (SOM).

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