LeafCheckIT: A Banana Leaf Analyzer for Identifying Macronutrient Deficiency
Jonilyn A. Tejada & Glenn Paul P. Gara 3rd International Conference on Communication and Information Processing, 2017
Abstract
Nutrient deficiency affects the production of banana fruits. Plant malnutrition may visually reflect on their leaves, however, identifying deficiency symptoms could be a difficult task and often requires laboratory tests such as leaf and soil analysis. This paper introduces the LeafCheckIT, a web and mobile application that uses Random Forest machine learning algorithm to identify Nitrogen (N), Phosphorus (P), and Potassium (K) collectively known as macronutrients deficiency symptoms on banana leaves. Based on the training set evaluation and 10-fold cross validation test conducted on WEKA data mining software, the technique used in the application resulted in 100 and 91.64 percent accuracy rate respectively.
BibTeX
@inproceedings{tejada2017_leafcheckit,
author = {Tejada, Jonilyn A. and Gara, Glenn Paul P.},
title = {LeafcheckIT: A Banana Leaf Analyzer for Identifying Macronutrient Deficiency},
year = {2017},
isbn = {9781450353656},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3162957.3163035},
doi = {10.1145/3162957.3163035},
booktitle = {Proceedings of the 3rd International Conference on Communication and Information Processing},
pages = {458–463},
numpages = {6},
keywords = {random forest, nutrient deficiency analyzer, image analysis, leaf classification, machine learning},
location = {Tokyo, Japan},
series = {ICCIP ’17}
}
Cite
Jonilyn A. Tejada and Glenn Paul P. Gara. 2017. LeafcheckIT: a banana leaf analyzer for identifying macronutrient deficiency. In Proceedings of the 3rd International Conference on Communication and Information Processing (ICCIP ’17). Association for Computing Machinery, New York, NY, USA, 458–463. DOI:https://doi.org/10.1145/3162957.3163035