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Classification of nutrient deficiency in black gram using deep convolutional neural networks
Author(s)
Han, Kadipa Aung Myo
Watchareeruetai, Ukrit
Date Issued
July 1, 2019
Type
Conference Paper
Abstract
This paper investigates the use of various deep convolutional neural networks (CNNs) with transfer learning to identify nutrient deficiencies from a leaf image. Experiments were conducted with a dataset containing 4,088 images of black gram (Vigna mungo) leaves grown under seven different treatments, i.e., complete nutrient treatment and six nutrient deficiency treatment, including calcium (Ca), iron (Fe), magnesium (Mg), nitrogen (N), potassium (K), and phosphorus (P) deficiencies. Experimental results indicate that a deep CNN model known as ResNet50 was the best among all experimented models with a test accuracy of 65.44% and a F-measure of 66.15%. In addition, We found that the ResNet50 model obviously outperformed a block-based method and the human performance reported in a literature.
Citation
Jcsse 2019 16th International Joint Conference on Computer Science and Software Engineering Knowledge Evolution Towards Singularity of Man Machine Intelligence, 277-282, 2019