TY - JOUR
T1 - Techniques of deep learning and image processing in plant leaf disease detection
T2 - a review
AU - Kini, Anita S.
AU - Reddy, Prema K.V.
AU - Pai, Smitha N.
N1 - Funding Information:
Authors are thankful to the Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal for supporting with expertise that has supported this work.
Publisher Copyright:
© 2023 Institute of Advanced Engineering and Science. All rights reserved.
PY - 2023/6
Y1 - 2023/6
N2 - Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold the ophthalmic functionality of the human eye. In agriculture, leaf examination is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated.
AB - Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold the ophthalmic functionality of the human eye. In agriculture, leaf examination is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated.
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U2 - 10.11591/ijece.v13i3.pp3029-3040
DO - 10.11591/ijece.v13i3.pp3029-3040
M3 - Article
AN - SCOPUS:85149144023
SN - 2088-8708
VL - 13
SP - 3029
EP - 3040
JO - International Journal of Electrical and Computer Engineering
JF - International Journal of Electrical and Computer Engineering
IS - 3
ER -
