Multispectral Imaging and Convolutional Neural Network for Photosynthetic Pigments Prediction

被引:0
|
作者
Prilianti, Kestrilia R. [1 ]
Onggara, Ivan C. [1 ]
Adhiwibawa, Marcelinus A. S. [2 ]
Brotosudarmo, Tatas H. P. [2 ]
Anam, Syaiful [3 ]
Suryanto, Agus [3 ]
机构
[1] Univ Ma Chung, Dept Informat Engn, Malang, Indonesia
[2] Ma Chung Res Ctr Photosynthet Pigments MRCPP, Malang, Indonesia
[3] Univ Brawijaya, Dept Math, Malang, Indonesia
来源
2018 5TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, COMPUTER SCIENCE AND INFORMATICS (EECSI 2018) | 2018年
关键词
convolutional neural network; multispectral digital image; non-destructive evaluation; photosynthetic pigments; CLASSIFICATION;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The evaluation of photosynthetic pigments composition is an essential task in agricultural studies. This is due to the fact that pigments composition could well represent the plant characteristics such as age and varieties. It could also describe the plant conditions, for example, nutrient deficiency, senescence, and responses under stress. Pigment role as light absorber makes it visually colorful. This colorful appearance provides benefits to the researcher on conducting a non-destructive analysis through a plant color digital image. In this research, a multispectral digital image was used to analyze three main photosynthetic pigments, i.e., chlorophyll, carotenoid, and anthocyanin in a plant leaf. Moreover, Convolutional Neural Network (CNN) model was developed to deliver a real-time analysis system. Input of the system is a plant leaf multispectral digital image, and the output is a content prediction of the pigments. It is proven that the CNN model could well recognize the relationship pattern between leaf digital image and pigments content. The best CNN architecture was found on ShallowNet model using Adaptive Moment Estimation (Adam) optimizer, batch size 30 and trained with 15 epoch. It performs satisfying prediction with MSE 0.0037 for in sample and 0.0060 for out sample prediction (actual data range -0.1 up to 2.2).
引用
收藏
页码:554 / 559
页数:6
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