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Black Gram Plant Nutrient Deficiency Classification in Combined Images Using Convolutional Neural Network
(2020-03-01)
Myo Han, Kadipa Aung
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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 %.
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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.
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Identification of Plant Nutrient Deficiencies Using Convolutional Neural Networks
(2018-07-02)
Watchareeruetai, Ukrit
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Noinongyao, Pavit
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Wattanapaiboonsuk, Chaiwat
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Khantiviriya, Puriwat
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Duangsrisai, Sutsawat
A novel image analysis method for identifying nutrient deficiencies in plant based on its leaf is proposed. First, the proposed method divides an input leaf image into small blocks. Second, each block of leaf pixels is fed to a set of convolutional neural networks (CNNs). Each CNN is specifically trained for a nutrient deficiency and is utilized to decide if a block is presenting any symptom of the corresponding nutrient deficiency. Next, the responses from all CNNs are integrated to produce a single response for the block using a winner-take-all strategy. Finally, the responses from all blocks are integrated into one using a multi-layer perceptron to produce a final response for the whole leaf. Validation of the proposed method was performed on a set of black gram (Vigna mungo) plants grown under nutrient-controlled environments. Five types of deficiencies, i.e., Ca, Fe, K, Mg, and N deficiencies, and a group of plants with complete nutrients were studied. A dataset consisting of 3,000 leaf images was collected and used for experimentation. Experimental results indicate the superiority of the proposed method over trained humans in nutrient deficiency identification.

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