Pattern recognition of lettuce varieties with machine learning / Seri' Aisyah Hassim
Determination of lettuce varieties through image processing is considered as part of precision farming. Automatic classification is becoming vital for precision farming practice as it is rapidly developing with emergence of many applications for agriculture. It is a hassling process to differenti...
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Format: | Thesis |
Published: |
2019
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Online Access: | http://studentsrepo.um.edu.my/11434/1/Seri_Aisyah_Hassim.jpg http://studentsrepo.um.edu.my/11434/8/seri.pdf http://studentsrepo.um.edu.my/11434/ |
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Summary: | Determination of lettuce varieties through image processing is considered as part of
precision farming. Automatic classification is becoming vital for precision farming
practice as it is rapidly developing with emergence of many applications for agriculture.
It is a hassling process to differentiate and identify the lettuce varieties through human
capabilities as it is time consuming and prone to errors in identification process. Hence,
there is a need to do this assisted by a machine capability which makes it faster with
greater accuracy. Application of machine learning in agricultural is still not widely
applied and many phases need to be improved. Differentiation of lettuce varieties with
colour or shape similarity is quite challenging. This study focuses on designing the lettuce
varieties recognition by using Convolution Neural Network (CNN) in MATLAB. The
neural network model consists of layers such as Convolution Layer, Normalization Layer,
ReLU Layer, Fully Connected Layer, Softmax Layer, and Classification Layer. The
network needs to undergo training sessions before being able to recognize the lettuce
varieties. A set of data are prepared for prediction after training. The accuracy for overall
classifications is 94.4% while accuracy for specific lettuce varieties of Butterhead
Lettuce, Celtucelove Lettuce, Italian Lettuce, Red Coral Lettuce, Red lettuce, Red
Oakleaf Lettuce and Salad Grand Rapid Lettuce were 94.7%, 99.7%, 97%, 94%, 90.7%,
98%, 87% respectively. |
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