Search Results

Now showing 1 - 3 of 3

Some of the metrics are blocked by your
consent settings
Black Gram Plant Nutrient Deficiency Classification in Combined Images Using Convolutional Neural Network
(2020-03-01)
Myo Han, Kadipa Aung
;
Watchareeruetai, Ukrit
Plant nutrient deficiency classification is vital for the agricultural industry to improve both the qualities and the quantities of crops. Computer vision and deep learning technologies, especially convolutional neural networks, perform an essential role in agricultural and biological sectors to solve the various kinds of complex problems. In this paper, we conducted the classification of the complete nutrient and six types of nutrient deficiency of black gram over the combined images of old leaf and young leaf. We found that the combined image supports more useful information than a single image. We accomplished the feature extraction process by taking the advantages of the deep pre-trained model to extract the features from the image automatically. Extracted features from ResNet50 deep pre-trained model are fed into three different classifiers as the input: (1) logistic regression, (2) support vector machine and (3) multilayer perceptron and compared the performance of these models. The multilayer perceptron models achieved superior performance than support vector machine and logistic regression by the accuracy of 88.33 %.
Some of the metrics are blocked by your
consent settings
Classification of nutrient deficiency in black gram using deep convolutional neural networks
(2019-07-01)
Han, Kadipa Aung Myo
;
Watchareeruetai, Ukrit
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.
Some of the metrics are blocked by your
consent settings
Separation of abnormal regions on black gram leaves using image analysis
(2017-09-05)
Noinongyao, Pavit
;
Watchareeruetai, Ukrit
;
Khantiviriya, Puriwat
;
Wattanapaiboonsuk, Chaiwat
;
Duangsrisai, Sutsawat
This paper proposes an image analysis method for separating abnormal regions caused by nutrient deficiencies on plants' leaves. The proposed method analyzes a histogram of normal leaves' colors to identify abnormalities on leaves. It can be divided into three main steps. Firstly, color features of leaf region in an input image are computed. Secondly, for each pixel, its color features are compared to the corresponding bin in the histogram to determine whether the pixel is abnormal. Finally, a post-processing technique is then applied to reduce noises in the result. Experiments have been conducted using black gram (Vigna mungo) leaves with five different nutrient deficiencies. The experimental results show that the proposed method can separate abnormal regions with an accuracy of above 90%.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results