Identification of Nitrogen Content of Vernonia amygdalina Leave Based on Artificial Neural Network Modeling

DOI

10.2991/978-94-6463-274-3_17How to use a DOI?

Keywords

Artificial Neural Network; Chlorophyll; Color; Nitrogen

Abstract

The level of greenness or the content of chlorophyll in the leaves is one indicator of plant health, where plants that are fertile and have enough nutrients will look green on their leaves. This indicates that the nitrogen (N) content, which is the constituent of leaf chlorophyll, is fulfilled properly and increases plant productivity higher. Knowing the nitrogen content in a plant can inform nutritional needs and monitor plant development quickly and precisely. This research aims to develop a mathematical model to predict the chlorophyll and nitrogen content in leaves using a machine vision method with texture and color analysis. Texture analysis uses the color features of Grey, RGB, HSL, HSV, and L*a*b* and the color co-occurrence matrix (CCM). The best 8 features have been obtained using Correlation as a selection attribute. The best ANN model was selected from 75% of training data and 25% of validation data with a topology structure of 8-30-40-2 with a learning rate value of 0.1 and momentum 0.5, trainlm as the selected learning function, tansig the activation function in the hidden layer and output layer. The selected ANN structure produces a validation correlation coefficient (R) of 0.99073 and a validation MSE of 0.0793.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sandra
AU  - Retno Damayanti
AU  - Mochamad Bagus Hermanto
AU  - Rut Januar Nainggolan
AU  - Danuh Kanara Anta
AU  - Arini Robbil Izzati
AU  - Siska Ratna Anggraeni
AU  - Mitha Saadiyah
PY  - 2023
DA  - 2023/10/27
TI  - Identification of Nitrogen Content of Vernonia amygdalina Leave Based on Artificial Neural Network Modeling
BT  - Proceedings of the 7th International Conference on Food, Agriculture, and Natural Resources (IC-FANRES 2022)
PB  - Atlantis Press
SP  - 198
EP  - 207
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-274-3_17
DO  - 10.2991/978-94-6463-274-3_17
ID  - 2023
ER  -