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Author: search.filters.author.Watchareeruetai, UkritSubject: search.filters.subject.image analysis

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    Identification of Plant Nutrient Deficiencies Using Convolutional Neural Networks
    (2018-07-02)
    Watchareeruetai, Ukrit
    ;
    Noinongyao, Pavit
    ;
    Wattanapaiboonsuk, Chaiwat
    ;
    Khantiviriya, Puriwat
    ;
    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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    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%.
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    Detection of fibrosis in liver biopsy images using multi-objective genetic programming
    (2017-07-01)
    Thong-On, Purit
    ;
    Watchareeruetai, Ukrit
    This paper proposes an automatic construction of feature extractor for liver fibrosis detection using a multiobjective genetic programming approach in which a constructed feature extractor was measured in different aspects in which becomes the objectives of the evolutionary run. The result of the evolutionary run is a set of solutions with different strengths and weaknesses. A solution from each experiment is selected and compared with a benchmark handcraft method in by each experiment and top-five manners. One of the best result obtained has 2.09 fibrosis estimation error which is less than the benchmark method with 2.63 fibrosis estimation error.