Open AccessA Deep Learning Approach for Plant Material Disease Identification
Author(s) -
Rishabh Tripathi
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1116/1/012133
Subject(s) - plant disease , feature (linguistics) , identification (biology) , convolutional neural network , plant identification , artificial intelligence , computer science , deep learning , population , pattern recognition (psychology) , feature extraction , machine learning , microbiology and biotechnology , botany , biology , philosophy , linguistics , demography , sociology
Plant Material Disease Identification is essential for the food safety. To increase the crop production for the growing population of the world, the proper treatment is required on proper time to save the plant. Therefore, disease diagnosing on time is very important. This paper uses a deep learning convolutional neural network model to identify the plant disease. The pre-existing deep learning model Alexnet has been employed for plant disease identification in which an external feature of segmented plant material (leaves) is passed to the deepest fully connected layer. This combination of extracted feature by Alexnet and external feature of segmented plant material helps in plant disease identification. Experimental analysis has been done on a standard dataset Plant Village which has total 54,306 leaf images of 15 distinct plants having 38 diseases. The presented CNN approach worked well and outperformed to the existing approach.