Open Access Open Access  Restricted Access Subscription Access

Applications of Pattern Recognition Algorithms in Agriculture:A Review


Affiliations
1 Rai University, Ahmedabad, Gujarat, India
2 ISTAR, Anand, Gujarat, India
3 Narmada College of Computer Application, Zadeshwar, Bharuch, Gujarat, India
 

Pattern recognition has its ischolar_mains in artificial intelligence and is a branch of machine learning that focuses on the recognition of patterns and regularities in data. Data can be in the form of image, text, video or any other format. Under normal scenario, pattern recognition is implemented by first formalizing a problem, explain and at last visualize the pattern. In contrast to pattern matching, pattern recognition algorithms generally provide a fair result for all possible inputs by considering statistical variations. Probabilistic classifiers have supported Agricultural statistical inference for decades. Potential applications of this technique in agriculture are numerous like pattern recognition from satellite imagery, identifying the type of disease from leaf image, weed detectionetc. This paper explores employment of pattern recognition in an agricultural domain.

Keywords

Classification, Feature Extraction, Feature Selection, Pattern Recognition, Pattern Recognition Models, Agriculture.
User
Notifications
Font Size

Abstract Views: 117

PDF Views: 3




  • Applications of Pattern Recognition Algorithms in Agriculture:A Review

Abstract Views: 117  |  PDF Views: 3

Authors

M. P. Raj
Rai University, Ahmedabad, Gujarat, India
P. R. Swaminarayan
ISTAR, Anand, Gujarat, India
J. R. Saini
Narmada College of Computer Application, Zadeshwar, Bharuch, Gujarat, India
D. K. Parmar
Rai University, Ahmedabad, Gujarat, India

Abstract


Pattern recognition has its ischolar_mains in artificial intelligence and is a branch of machine learning that focuses on the recognition of patterns and regularities in data. Data can be in the form of image, text, video or any other format. Under normal scenario, pattern recognition is implemented by first formalizing a problem, explain and at last visualize the pattern. In contrast to pattern matching, pattern recognition algorithms generally provide a fair result for all possible inputs by considering statistical variations. Probabilistic classifiers have supported Agricultural statistical inference for decades. Potential applications of this technique in agriculture are numerous like pattern recognition from satellite imagery, identifying the type of disease from leaf image, weed detectionetc. This paper explores employment of pattern recognition in an agricultural domain.

Keywords


Classification, Feature Extraction, Feature Selection, Pattern Recognition, Pattern Recognition Models, Agriculture.