Predicting Chlorophyll Intensity Of Various Plants Using Improved Convolutional Neural Network
Chlorophyll pigment is beneficial during photosynthesis to absorb sufficient light energy and provide nutrients to the plant to grow healthy. A convenient assessment of chlorophyll content is essential in smart management agriculture. Several attempts have been made to implements computer vision...
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| Main Author: | |
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| Format: | Final Year Project Report / IMRAD |
| Language: | en en |
| Published: |
Universiti Malaysia Sarawak, (UNIMAS)
2023
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/42987/1/MICHELLE%20NASHRIN%20%2824pgs%29.pdf http://ir.unimas.my/id/eprint/42987/2/MICHELLE%20NASHRIN%20%28Fulltext%29.pdf http://ir.unimas.my/id/eprint/42987/ |
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| Summary: | Chlorophyll pigment is beneficial during photosynthesis to absorb sufficient light energy
and provide nutrients to the plant to grow healthy. A convenient assessment of
chlorophyll content is essential in smart management agriculture. Several attempts have
been made to implements computer vision to enhance the precision agriculture
techniques. However, problems still arise as the applied algorithms of machine learning
are time consuming, very complex architectures, high computational cost, as well as less
generalization towards various plant species and size of datasets. Hence, in this project, a
rapid and straightforward convolutional neural network (CNN) algorithm was proposed
to predict chlorophyll intensity of various plant species based on leaf reflectance spectra.
The datasets were taken from ANGERS Leaf Optical Properties Database (2003). The
proposed model consists of Hybrid CNN as a feature extractor and support vector
regression (SVR) network as a predictor. Hybrid CNN was designed by modifying the
architectures of AlexNet and PNet using MATLAB R2023a. The performance of Hybrid
CNN with SVR (CNN-SVR) was also compared with AlexNet, PNet, and SVR. Results
showed that the best CNN also can be designed with one input, four convolutional, four
max-pooling and three fully connected layers which can be found in Hybrid CNN-SVR.
The experimental results show that the prediction accuracy of chlorophyll intensity is
satisfying with a mean square error (MSE) of 0.1558 and 1.149 for training and testing
sets, respectively. |
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